Feasibility of Spinal Neuronavigation and Evaluation of

Feasibility of Spinal Neuronavigation and Evaluation of Registration and Application Error Modalities Using Optical

Topographic Imaging

by

Daipayan Guha

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Institute of Medical Science University of Toronto

© Copyright by Daipayan Guha (2018)

ii

Feasibility of Spinal Neuronavigation and Evaluation of

Registration and Application Error Modalities Using Optical

Topographic Imaging

Daipayan Guha

Doctor of Philosophy

Institute of Medical Science

University of Toronto

2018

Abstract

Intra-operative navigation began with the localization of subsurface structures in cranial

neurosurgery using frame-based stereotaxy. Advances in imaging and computing power have led

to the development of modern frameless three-dimensional (3D) computer-assisted navigation

(CAN), employed across multiple surgical disciplines. In spinal surgery, CAN may guide

implant placement, bony decompression and soft-tissue resection. However, adoption of 3D

CAN by spinal surgeons has been limited by cumbersome registration protocols, workflow

disruption, high capital cost, and questionable quantitative and clinical utility. A novel technique

for image-to-patient registration has recently been developed, based on optical topographic

imaging (OTI). Whether OTI-based CAN is able to provide accurate intra-operative image-

guidance for common spinal procedures, while addressing current limitations of CAN

techniques, warrants study. First, we explored the current paradigms of reporting CAN accuracy

in the context of spinal procedures, finding that quantitative application accuracy and

radiographic screw placement do not correlate. We therefore proposed a combined quantitative

and radiographic system of reporting CAN accuracy. Second, we examined the registration

workflow and accuracy of OTI-CAN in open posterior thoracolumbar instrumentation, in pre-

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clinical swine and cadaveric models and subsequently in clinical in-vivo testing. We found that

OTI-CAN is comparably accurate to but significantly faster than existing 3D CAN techniques.

We subsequently found that OTI-CAN was similarly accurate, with maintained workflow

improvements, in minimally-invasive (MIS) thoracolumbar and open cervical approaches.

Finally, we explored mechanisms by which current CAN and specifically surface-based

registration techniques, including OTI, may fail. We found that navigation error increases with

greater working distance to the dynamic reference frame (DRF), and with greater geometric

symmetry over the osseous posterior elements. Taken together, this body of work demonstrates

that OTI is a feasible technique for spinal CAN, and may alleviate the primary issues plaguing

current systems to allow increased adoption into settings where CAN may be most useful.

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Acknowledgments

I owe my sincerest gratitude to Dr. Victor Yang for this opportunity and for continued guidance

and mentorship throughout my residency. It has been my utmost honour to be able to observe

and work with you on your journey developing novel surgical applications of optical imaging

devices. What has struck me most, and what I hope to be able to replicate in my own career, is

how incredibly you have been able to balance your clinical, research and entrepreneurial interests

with your family life.

I would like to thank Dr. Albert Yee, as a member of my thesis committee, for your invaluable

guidance and support. Despite a hectic schedule you never ceased to take time out to discuss

approaches for a new study, or to go out of your way to assist in my progression as a clinician.

I also wish to thank Dr. Michael Fehlings, as a member of my thesis committee, without whom

this work would not be possible. Your keen interest and attention to detail, as well as knowledge

of how to plan, perform and communicate scientific work, was instrumental in raising the quality

of this work.

I would like to extend my sincerest thanks also to Dr. Nir Lipsman, for personifying scientific

curiosity and levelheadedness from our initial interactions at the conclusion of my medical

training, to our clinical rotations as junior/senior residents, and finally now as a member of my

thesis committee.

Numerous other advisors, colleagues and friends, impossible to name individually here, have

contributed significantly to my research training. In particular, I would like to thank the

University of Toronto’s Department of Surgery and Division of Neurosurgery for prioritizing the

Surgeon-Scientist Training Program. In this, a number of individuals have made notable

contributions: Chairman of the Department of Surgery Dr. James Rutka, Chairman of the

Division of Neurosurgery Dr. Andres Lozano, Program Director Dr. Abhaya Kulkarni, Sandi

Amaral, Val Cabral, and my fellow clinical and research trainees.

Curiosity, passion, work ethic, and a drive to improve upon the status quo are key tenets of the

life and career I am striving to build. For instilling these qualities in me and for raising me with

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these very values at the forefront, with no sacrifice spared so that I could have every opportunity

to succeed and grow, I owe my absolute deepest gratitude to my parents Abhijit and Soma. I also

thank my sister, Tanya, for always remaining lighthearted, never allowing me to lose sight of the

truly important moments, and for always being available to lend an ear in our shared journey

towards our doctorates. Finally, thank you to my fiancée, Shatabdi, for your continual

encouragement and unyielding love throughout the long hours spent composing and writing this

thesis.

I would also like to acknowledge the following granting/scholarship programs, without whom

this work would not have been possible: Canadian Institutes of Health Research (CIHR), Natural

Sciences and Engineering Research Council of Canada (NSERC), Postgraduate Medical

Education at the University of Toronto, Surgeon Scientist Training Program at the University of

Toronto, Clinician Investigator Program at the Royal College of Physicians and Surgeons of

Canada, and the International Society for Optics and Photonics (SPIE).

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Contributions

Daipayan Guha (author) solely prepared this thesis. All aspects of this body of work, including

the planning, execution, analysis, and writing of all original research and publications, was

performed in whole or in part by the author. The following individual contributions are formally

acknowledeged:

Dr. Victor X.D. Yang (Primary Supervisor, Thesis Committee Member) – mentorship;

laboratory resources; guidance and assistance in the planning, execution and analysis of

experiments as well as manuscript and thesis preparation

Dr. Albert Yee (Thesis Committee Member) – mentorship; laboratory resources; guidance and

assistance in the planning, execution and analysis of experiments as well as manuscript and

thesis preparation

Dr. Michael G. Fehlings (Thesis Committee Member) – mentorship; guidance and assistance in

the planning and analysis of experiments as well as manuscript and thesis preparation

Dr. Nir Lipsman (Thesis Committee Member) – mentorship; guidance and assistance in the

interpretation of results as well as thesis preparation

Dr. Todd G. Mainprize – mentorship; laboratory resources, study supervision

Dr. Raphael Jakubovic – assistance with the quantitative engineering analysis in Chapters 4 and

5, and with the image processing for quantitative analysis in Chapters 4-9

Shaurya Gupta – assistance with the quantitative engineering analysis in Chapters 4 and 5, and

with execution of the experiments in Chapter 6-8

Joel Ramjist – assistance with the execution of experiments in Chapters 4, 5, 7 and 8

Michael K. Leung – assistance with the image processing and software development/refinement

of OTI in Chapter 5, and execution of experiments in Chapter 9

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Ryan Deorajh – assistance with the execution of experiments in Chapters 5-7

Dr. Naif M. Alotaibi – assistance with the interpretation of results in Chapters 3, 4, 6, 7

Jamil Jivraj – assistance with the execution of experiments in Chapter 5

Michael Lu – assistance with the execution of experiments in Chapter 5

Dr. Ali Moghaddamjou – assistance with statistical analysis in Chapter 3

Zaneen H. Jiwani – assistance with the execution of experiments in Chapter 3

Dr. David W. Cadotte – assistance with the execution of experiments in Chapters 4-5

Dr. Leodante B. da Costa – assistance with the execution of experiments in Chapters 4-5

Dr. Rajeesh George – assistance with radiographic analysis in Chapters 4-5

Dr. Chris Heyn – assistance with radiographic analysis in Chapters 4-5

Dr. Peter Howard – assistance with radiographic analysis in Chapters 4-5

Dr. Anish Kapadia – assistance with radiographic analysis in Chapter 4

Dr. Jesse M. Klostranec – assistance with radiographic analysis in Chapter 4

Dr. Nicolas Phan – assistance with the execution of experiments in Chapters 4-5

Dr. Gamaliel Tan – assistance with radiographic analysis in Chapters 4-5

Dr. Beau Standish – assistance with the hardware and software development of OTI in Chapter 5

Dr. Adrian Mariampillai – assistance with the hardware and software development of OTI in

Chapter 5

Dr. Kenneth Lee – assistance with the hardware and software development of OTI in Chapter 5

Dr. Peter Siegler – assistance with the hardware and software development of OTI in Chapter 5

Patrick Skowron – assistance with quantitative engineering analysis in Chapter 5

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Hamza Farooq – assistance with quantitative engineering analysis in Chapter 5

Nhu Nguyen – assistance with quantitative engineering analysis in Chapter 5

Joseph Alarcon – assistance with quantitative engineering analysis in Chapter 5

Dr. Michael Ford – assistance with the execution of experiments in Chapter 5

Dr. Sidharth Saini – assistance with radiographic analysis in Chapter 6

Dr. Howard J. Ginsberg – assistance with the interpretation of results in Chapter 9

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Table of Contents

ACKNOWLEDGMENTS ...................................................................................................................... IV

CONTRIBUTIONS .............................................................................................................................. VI

TABLE OF CONTENTS ........................................................................................................................ IX

LIST OF ABBREVIATIONS ................................................................................................................. XIV

LIST OF TABLES .............................................................................................................................. XVI

LIST OF FIGURES ............................................................................................................................ XVII

CHAPTER 1 GENERAL INTRODUCTION .................................................................................................1

1.1 THESIS ORGANIZATION ......................................................................................................................1

CHAPTER 2 INTRA-OPERATIVE SPINAL NAVIGATION ...........................................................................2

INTRODUCTION .........................................................................................................................2

2.1 EVOLUTION OF COMPUTER-ASSISTED NAVIGATION ................................................................................2

2.1.1 History of Frameless Stereotaxy ..............................................................................................4

2.1.2 History of Spinal Computer-Assisted Navigation .....................................................................5

2.1.3 Current Applications of Spinal Computer-Assisted Navigation................................................9

2.1.3.1 Rationale for Spinal Computer-Assisted Navigation .................................................................................. 12

2.2 REGISTRATION, IMAGING AND ACTUATION TECHNIQUES IN SPINAL COMPUTER-ASSISTED NAVIGATION ....... 21

2.2.1 2D Navigation ....................................................................................................................... 21

2.2.2 3D Navigation ....................................................................................................................... 23

2.2.2.1 Imaging Techniques ................................................................................................................................... 23

2.2.2.2 Registration Techniques ............................................................................................................................. 28

2.2.2.2.1 Paired-Point Matching .......................................................................................................................... 29

2.2.2.2.2 Surface Contour Matching .................................................................................................................... 32

2.2.2.2.3 Hybrid Matching ................................................................................................................................... 36

2.2.2.2.4 Automatic Registration ......................................................................................................................... 37

2.2.2.2.5 Optical Topographic Imaging ................................................................................................................ 38

2.2.3 Instrument Tracking and Actuation ...................................................................................... 44

2.3 EVALUATION OF NAVIGATION ACCURACY .......................................................................................... 50

2.4 THESIS AIMS AND HYPOTHESES ........................................................................................................ 53

CHAPTER 3 SPATIO-TEMPORAL TRENDS IN SPINAL CAN IMPLEMENTATION ....................................... 56

PREAMBLE ............................................................................................................................... 56

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3.1 ABSTRACT .................................................................................................................................... 57

3.2 INTRODUCTION ............................................................................................................................. 58

3.3 METHODS .................................................................................................................................... 60

3.3.1 Study Design ......................................................................................................................... 60

3.3.2 Database – Patient Selection ................................................................................................ 60

3.3.3 Database – Data Extraction .................................................................................................. 61

3.3.4 Database – Statistical Analysis ............................................................................................. 61

3.3.5 Online Survey ........................................................................................................................ 62

3.4 RESULTS ....................................................................................................................................... 63

3.4.1 Spatio-Temporal Trends in Spinal CAN Usage ...................................................................... 63

3.4.2 Impact of CAN Usage on Revision Surgery Rates.................................................................. 66

3.4.3 Survey of Surgical Trainees – Demographics ........................................................................ 68

3.4.4 Utilization of CAN by Trainees .............................................................................................. 69

3.4.5 Impact of CAN on Trainee Proficiency .................................................................................. 71

3.5 DISCUSSION .................................................................................................................................. 73

3.6 CONCLUSIONS ............................................................................................................................... 76

3.7 SUPPLEMENTAL | DIAGNOSTIC AND FEE CODING ................................................................................ 77

3.8 SUPPLEMENTAL | ONLINE SURVEY.................................................................................................... 80

CHAPTER 4 CORRELATION BETWEEN CLINICAL AND ABSOLUTE ENGINEERING ACCURACY IN SPINAL

COMPUTER-ASSISTED NAVIGATION .................................................................................................. 84

PREAMBLE ............................................................................................................................... 84

4.1 ABSTRACT .................................................................................................................................... 85

4.2 INTRODUCTION ............................................................................................................................. 86

4.3 METHODS .................................................................................................................................... 88

4.3.1 Patient Selection ................................................................................................................... 88

4.3.2 Intra-Operative Navigation ................................................................................................... 88

4.3.3 Clinical Grading ..................................................................................................................... 88

4.3.4 Quantitative Navigation Application Accuracy ..................................................................... 90

4.3.5 Statistical Analysis ................................................................................................................ 92

4.4 RESULTS ....................................................................................................................................... 93

4.4.1 Clinical Accuracy ................................................................................................................... 93

4.4.2 Absolute Application Accuracy ............................................................................................. 94

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4.4.3 Clinical-Engineering Correlation ........................................................................................... 94

4.4.4 Surgeon Compensation for Navigation Error ........................................................................ 97

4.5 DISCUSSION .................................................................................................................................. 99

4.6 CONCLUSIONS ............................................................................................................................. 103

CHAPTER 5 OPTICAL TOPOGRAPHIC IMAGING WITH EFFICIENT REGISTRATION TO CT FOR SPINAL

INTRA-OPERATIVE THREE-DIMENSIONAL NAVIGATION ................................................................... 104

PREAMBLE ............................................................................................................................. 104

5.1 ABSTRACT .................................................................................................................................. 105

5.2 INTRODUCTION ........................................................................................................................... 106

5.3 METHODS .................................................................................................................................. 109

5.3.1 OTI System Design .............................................................................................................. 109

5.3.2 Specimen/Patient Selection ................................................................................................ 112

5.3.3 Pre-Clinical Testing ............................................................................................................. 112

5.3.4 Human Clinical Testing ....................................................................................................... 113

5.3.5 Clinicoradiographic Accuracy Assessment .......................................................................... 116

5.3.6 Quantitative Application/Engineering Accuracy ................................................................ 117

5.3.7 Statistical Analysis .............................................................................................................. 119

5.4 RESULTS ..................................................................................................................................... 120

5.4.1 Pre-Clinical Validation ......................................................................................................... 120

5.4.2 Human Clinical Validation................................................................................................... 120

5.5 DISCUSSION ................................................................................................................................ 125

5.6 CONCLUSIONS ............................................................................................................................. 128

CHAPTER 6 OPTICAL TOPOGRAPHIC IMAGING FOR SPINAL INTRA-OPERATIVE THREE-DIMENSIONAL

NAVIGATION IN MINI-OPEN APPROACHES ...................................................................................... 129

PREAMBLE ............................................................................................................................. 129

6.1 ABSTRACT .................................................................................................................................. 130

6.2 INTRODUCTION ........................................................................................................................... 131

6.3 METHODS .................................................................................................................................. 133


6.3.2 Surgical Technique .............................................................................................................. 133

6.3.3 Registration and Intra-Operative Navigation ..................................................................... 136

6.3.4 Evaluation of Navigation Accuracy ..................................................................................... 137

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6.4 RESULTS ..................................................................................................................................... 139

6.4.1 Image-to-Patient Registration ............................................................................................ 139

6.4.2 Quantitative Navigation Application Accuracy ................................................................... 140

6.4.3 Radiographic Navigation Accuracy ..................................................................................... 143

6.5 DISCUSSION ................................................................................................................................ 145

6.6 CONCLUSIONS ............................................................................................................................. 148

CHAPTER 7 OPTICAL TOPOGRAPHIC IMAGING FOR SPINAL INTRA-OPERATIVE THREE-DIMENSIONAL

NAVIGATION IN THE CERVICAL SPINE ............................................................................................. 149

PREAMBLE ............................................................................................................................. 149

7.1 ABSTRACT .................................................................................................................................. 150

7.2 INTRODUCTION ........................................................................................................................... 151

7.3 METHODS .................................................................................................................................. 152


7.3.2 Surgical Technique .............................................................................................................. 153

7.3.3 Registration and Intra-Operative Navigation ..................................................................... 153

7.3.4 Evaluation of Navigation Accuracy ..................................................................................... 155


7.4 RESULTS ..................................................................................................................................... 158

7.4.1 Quantitative Navigation Application Accuracy ................................................................... 159

7.4.2 Radiographic Navigation Accuracy ..................................................................................... 159

7.5 DISCUSSION ................................................................................................................................ 162

7.6 CONCLUSIONS ............................................................................................................................. 164

CHAPTER 8 ERROR PROPAGATION IN SPINAL INTRA-OPERATIVE THREE-DIMENSIONAL NAVIGATION

FROM NON-SEGMENTAL REGISTRATION ........................................................................................ 165

PREAMBLE ............................................................................................................................. 165

8.1 ABSTRACT .................................................................................................................................. 166

8.2 INTRODUCTION ........................................................................................................................... 168

8.3 METHODS .................................................................................................................................. 170


8.3.2 Quantification of Navigation Error from Proximity to DRF ................................................. 170

8.3.3 Quantification of Navigation Error from Surgical Manipulation ........................................ 171

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8.3.4 Quantification of Navigation Error from Respiration-Induced Motion............................... 171

8.3.5 Statistical Analyses ............................................................................................................. 174

8.4 RESULTS ..................................................................................................................................... 175

8.4.1 Navigation Error from Proximity to DRF ............................................................................. 175

8.4.2 Navigation Error from Surgical Manipulation .................................................................... 176

8.4.3 Navigation Error from Respiration-Induced Motion ........................................................... 179

8.5 DISCUSSION ................................................................................................................................ 182

8.6 CONCLUSIONS ............................................................................................................................. 185

CHAPTER 9 GEOMETRIC CONGRUENCE IN SURFACE REGISTRATION FOR SPINAL INTRA-OPERATIVE

THREE-DIMENSIONAL NAVIGATION ................................................................................................ 186

PREAMBLE ............................................................................................................................. 186

9.1 ABSTRACT .................................................................................................................................. 187

9.2 INTRODUCTION ........................................................................................................................... 188

9.3 METHODS .................................................................................................................................. 190


9.3.2 OTI Registration .................................................................................................................. 190

9.3.3 Computational Modelling of Geometric Congruence ......................................................... 192


9.4 RESULTS ..................................................................................................................................... 195

9.4.1 Geometric Congruence by Spinal Region ............................................................................ 195

9.4.2 Geometric Congruence by Laterality .................................................................................. 198

9.4.3 Geometric Congruence by Inclusion of the Spinous Process ............................................... 201

9.5 DISCUSSION ................................................................................................................................ 204

9.6 CONCLUSIONS ............................................................................................................................. 208

CHAPTER 10 CONCLUDING SUMMARY, GENERAL DISCUSSION, AND FUTURE DIRECTIONS ............... 209

PREAMBLE ............................................................................................................................. 209

10.1 CONCLUDING SUMMARY .............................................................................................................. 210

10.2 UNIFYING DISCUSSION.................................................................................................................. 214

10.3 FUTURE DIRECTIONS .................................................................................................................... 218

REFERENCES .................................................................................................................................. 223

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List of Abbreviations

2D Two-dimensional

3D Three-dimensional

ANOVA Analysis of variance

AP Antero-posterior

AR Augmented reality

BBL Biophotonics and Bioengineering Laboratory

BRW Brown-Roberts-Wells

CAN Computer-assisted navigation

CB Cone-beam

CC Cranio-caudal

CRW Cosman-Roberts-Wells

CT Computed tomography

DICOM Digital Imaging and Communications in Medicine

DRF Dynamic reference frame

EM Electromagnetic

EMG Electromyography

FB Fan-beam

FLE Fiducial localization error

FRE Fiducial registration error

GPU Graphics processing unit

ICP Iterative closest-point

IGS Image-guided surgery

II Image-intensifier

IQR Interquartile range

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IR Infra-red

LED Light-emitting diode

LITT Laser interstitial thermal therapy

LM Lateral mass

MIS Minimally-invasive

ML Medio-lateral

MPR Multiplanar reconstruction

MRI Magnetic resonance imaging

OR Operating room

OTI Optical topographic imaging

OTS Optical tracking system

RMS Root-mean-square

TL Trsanslaminar

TRE Target registration error

VR Virtual reality

XR X-ray

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List of Tables

TABLE 2-1. STUDIES OF PEDICLE SCREW ACCURACY. .......................................................................... 14

TABLE 2-2. STUDIES OF PEDICLE SCREW MISPLACEMENT. .................................................................. 16

TABLE 2-3. INTRAOPERATIVE OUTCOMES WITH ROBOTIC GUIDANCE. ............................................... 50

TABLE 3-1. UNIVARIATE ANALYSIS WITH REVISION SURGERY AS OUTCOME. ..................................... 67

TABLE 4-1. HEARY CLASSIFICATION FOR PEDICLE SCREW PLACEMENT. .............................................. 89

TABLE 4-2. 2MM CLASSIFICATION FOR PEDICLE SCREW PLACEMENT. ................................................ 89

TABLE 4-3. CLINICORADIOGRAPHIC GRADES OF 209 PEDICLE SCREWS. .............................................. 94

TABLE 5-1. NAVIGATION ERROR AS A FUNCTION OF SPINE REGION AND NAVIGATION TECHNIQUE.. 124

TABLE 6-1. CHARACTERISTICS OF CADAVERIC OTI REGISTRATIONS THROUGH MINI-OPEN EXPOSURES.

...................................................................................................................................................... 141

TABLE 7-1. KELLGREN CLASSIFICATION OF RADIOGRAPHIC CERVICAL SPONDYLOSIS. ....................... 157

TABLE 7-2. NUMBER OF SCREWS IN CADAVERIC AND CLINICAL TESTING, BY LEVEL AND KELLGREN

GRADE. .......................................................................................................................................... 158

TABLE 9-1. GEOMETRIC CONGRUENCE FOR UNILATERAL REGISTRATIONS BY SPINAL LEVEL. ............. 197

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List of Figures

FIGURE 2-1. THE EARLIEST FRAME-BASED STEREOTAXY. ......................................................................3

FIGURE 2-2. AN EARLY FRAMELESS STEREOTACTIC NAVIGATION SYSTEM. ...........................................5

FIGURE 2-3. INTRA-OPERATIVE BIPLANE FLUOROSCOPY. .....................................................................7

FIGURE 2-4. THE FIRST SPINAL INTRA-OPERATIVE NAVIGATION SYSTEM..............................................9

FIGURE 2-5. RADIOGRAPHIC ACCURACY OF PEDICLE SCREWS. ........................................................... 13

FIGURE 2-6. ‘VIRTUAL’ (2D) FLUOROSCOPY. ...................................................................................... 22

FIGURE 2-7. INTRA-OPERATIVE IMAGING TECHNQIUES FOR 3D CAN. ................................................. 27

FIGURE 2-8. PAIRED-POINT IMAGE-TO-PATIENT REGISTRATION. ....................................................... 30

FIGURE 2-9. MANUAL SURFACE MAPPING. ....................................................................................... 34

FIGURE 2-10. ITERATIVE CLOSEST-POINT REGISTRATION. .................................................................. 36

FIGURE 2-11. COMPONENTS AND COORDINATE SYSTEMS OF AUTOMATIC REGISTRATION

TECHNIQUES. ................................................................................................................................... 38

FIGURE 2-12. PASSIVE STEREOVISION AND CORRESPONDENCE. ........................................................ 40

FIGURE 2-13. STRUCTURED LIGHT 3D SCANNING............................................................................... 42

FIGURE 2-14. STRUCTURED LIGHT ILLUMINATION PATTERNS. ........................................................... 42

FIGURE 2-15. OPTICAL INSTRUMENT TRACKING SYSTEMS. ................................................................ 46

FIGURE 2-16. ELECTROMAGNETIC INSTRUMENT TRACKING. .............................................................. 48

FIGURE 2-17. ROBOTIC INSTRUMENTATION GUIDANCE. .................................................................... 50

FIGURE 2-18. CLASSIFICATION OF ERRORS IN FRAMELESS STEREOTACTIC NAVIGATION. .................... 52

FIGURE 3-1. COHORT DEMOGRAPHICS. ............................................................................................. 63

FIGURE 3-2. TEMPORAL TRENDS IN SPINAL CAN USAGE. ................................................................... 65

FIGURE 3-3. SURVEY DEMOGRAPHICS. .............................................................................................. 69

FIGURE 3-4. TRAINEE-REPORTED CAN USAGE. ................................................................................... 70

FIGURE 3-5. TRAINEE PROFICIENCY IN CAN. ...................................................................................... 72

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FIGURE 4-1. QUANTIFICATION OF NAVIGATION APPLICATION ACCURACY. ........................................ 91

FIGURE 4-2. ABSOLUTE NAVIGATION APPLICATION ACCURACY FOR 209 PEDICLE SCREWS. ................ 95

FIGURE 4-3. CORRELATION BETWEEN ABSOLUTE NAVIGATION APPLICATION ERROR AND

CLINICORADIOGRAPHIC GRADE. ....................................................................................................... 96

FIGURE 4-4. POTENTIAL MECHANISM FOR SURGEON COMPENSATION. ............................................. 98

FIGURE 4-5. CORRELATION BETWEEN TRANSLATIONAL AND ANGULAR NAVIGATION ERRORS. .......... 98

FIGURE 5-1. IDEAL THORACIC PEDICLE SCREW PLACEMENT. ............................................................ 108

FIGURE 5-2. CLINICAL PROTOTYPE OF AN EXPERIMENTAL OTI NAVIGATION SYSTEM. ...................... 110

FIGURE 5-3. OPTICAL TOPOGRAPHIC IMAGING (OTI) EXPERIMENTAL NAVIGATION TECHNIQUE. ...... 111

FIGURE 5-4. FLOW DIAGRAM OF OTI HUMAN CLINICAL TRIALS. ....................................................... 114

FIGURE 5-5. QUANTIFICATION OF ABSOLUTE NAVIGATION APPLICATION ACCURACY. ..................... 118

FIGURE 5-6. BLAND-ALTMAN ANALYSIS COMPARING BENCHMARK AND OTI NAVIGATION ACCURACY.

...................................................................................................................................................... 123

FIGURE 6-1. CADAVERIC MINI-OPEN EXPOSURE. ............................................................................. 134

FIGURE 6-2. IN-VIVO HUMAN CLINICAL MINI-OPEN EXPOSURES. ..................................................... 135

FIGURE 6-3. PROTOTYPE OTI CONFIGURATION. .............................................................................. 137

FIGURE 6-4. CORRELATION OF REGISTERED POINTS TO EXPOSURE SIZE AND SPINAL LEVEL. ............. 142

FIGURE 6-5. NAVIGATION APPLICATION ACCURACY, BY SPINAL LEVEL, IN CADAVERIC TESTING. ....... 142

FIGURE 6-6. NAVIGATION APPLICATION ACCURACY, BY SPINAL LEVEL, IN CLINICAL TESTING. ........... 143

FIGURE 6-7. REPRESENTATIVE INTENTIONAL PLACEMENT OF A POORLY-GRADED SCREW. ............... 144

FIGURE 7-1. TRACKED CERVICAL DRILL GUIDE NAVIGATED WITH OTI. .............................................. 154

FIGURE 7-2. QUANTIFICATION OF ABSOLUTE NAVIGATION APPLICATION ACCURACY. ..................... 156

FIGURE 7-3. ABSOLUTE NAVIGATION APPLICATION ACCURACY IN CADAVERIC TESTING. .................. 160

FIGURE 7-4. ABSOLUTE NAVIGATION APPLICATION ACCURACY IN CLINICAL TESTING. ...................... 161

FIGURE 8-1. RESPIRATORY MOTION TRACKING WITH A CUSTOM SPINOUS PROCESS CLAMP............ 172

FIGURE 8-2. VERTEBRAL RESPIRATORY MOTION TRACKING. ............................................................ 173

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FIGURE 8-3. TRANSLATIONAL NAVIGATION ERROR FROM DISTANCE TO DRF. .................................. 177

FIGURE 8-4. TRANSLATIONAL NAVIGATION ERROR WITH SURGICAL MANIPULATION. ..................... 178

FIGURE 8-5. RESPIRATION-INDUCED VERTEBRAL MOTION. .............................................................. 180

FIGURE 8-6. RESPIRATORY CYCLES WITH CLINICALLY SIGNIFICANT VERTEBRAL MOTION. ................. 181

FIGURE 9-1. CADAVERIC MIDLINE EXPOSURES FOR OTI. .................................................................. 191

FIGURE 9-2. RECONSTRUCTION OF OTI SURFACE MAP POINT CLOUDS. ............................................ 193

FIGURE 9-3. FITTING OF SYMMETRICAL GEOMETRIES TO OTI POINT CLOUDS. .................................. 193

FIGURE 9-4. GEOMETRIC CONGRUENCE BY SPINE REGION. .............................................................. 196

FIGURE 9-5. GEOMETRIC CONGRUENCE BY REGISTRATION LATERALITY. .......................................... 199

FIGURE 9-6. REDUCTION IN GEOMETRIC CONGRUENCE WITH BILATERAL REGISTRATION. ................ 200

FIGURE 9-7. GEOMETRIC CONGRUENCE BY SPINOUS PROCESS INCLUSION. ..................................... 202

FIGURE 9-8. REDUCTION IN GEOMETRIC CONGRUENCE WITH INCLUSION OF IPSILATERAL SPINOUS

PROCESS BASE. .............................................................................................................................. 203

FIGURE 9-9. PROTOCOL FOR MANUAL REGISTRATION VERIFICATION. ............................................. 207

1

Chapter 1 General Introduction

1.1 Thesis Organization

This thesis is organized in a ‘paper’ format rather than the traditional ‘continuous’ structure,

using primarily peer-reviewed content that has either already been published, or is in submission

for publication. Each chapter addresses a unique component of a novel technique for spinal intra-

operative three-dimensional navigation, including assessing the current climate of navigation

usage, existing paradigms of evaluating navigation techniques, and subsequently multiple routes

of investigation on the merits and pitfalls of this navigation technique. Chapter 2 represents a

comprehensive review of the existing work on spinal navigation and intra-operative imaging

techniques. The final section of this chapter outlines the research questions and hypotheses

addressed in the remainder of the thesis. Chapters 3-9 present original research addressing each

of these objectives, each as a self-contained manuscript. Chapter 3 is a reformatted version of a

manuscript currently in submission for publication. Chapter 4 is a reformatted version of a paper

published in The Spine Journal.(Guha, Jakubovic, Gupta, Alotaibi, et al., 2017) Chapters 5-9 are

reformatted versions of manuscripts submitted for journal publication. The discussion sections in

each of the data chapters (3-9) are complemented by Chapter 10, in which a general summary

and discussion of the thesis findings are presented, along with ongoing and specific future

directions for this work.

2

Chapter 2 Intra-Operative Spinal Navigation

Introduction

This review chapter is divided into 4 primary sections. The first (Section 2.1) discusses the

history and evolution of intra-operative computer-assisted navigation, beginning with a brief

introduction of frame-based stereotaxis and subsequently exploring the development of frameless

techniques and their extension to spinal applications. Section 2.1 concludes by delineating the

clinical rationale as well as limits to adoption of spinal navigation techniques. Section 2.2 briefly

summarizes current imaging and registration techniques for contemporary navigation systems,

including their relative merits and drawbacks. Section 2.3 describes how navigation systems are

currently evaluated in the literature. Finally, Section 2.4 outlines the specific hypotheses and

objectives addressed by the original research in this thesis.

2.1 Evolution of Computer-Assisted Navigation

Navigation, the “process or activity of accurately ascertaining one’s position and planning and

following a route” as defined by the Oxford English Dictionary, began in its earliest forms

through nautical charts and instruments used by sailors for guidance. In surgery, maintaining the

complex 3D relationships between anatomical targets and instruments is paramount to safe and

effective interventions. The earliest surgical navigation systems were developed in neurosurgery,

to correlate external cranial anatomy to underlying internal structures intra-operatively. In 1908

Horsley and Clarke coined the term ‘stereotactic’ in describing a novel device allowing the

placement of intracranial electrodes into precise targets in an animal model, using a rigid frame

(Figure 2-1).(E. A. C. Pereira, Green, Nandi, & Aziz, 2008)

3

The first human clinical application of frame-based stereotaxy was by Spiegel and Wycis in the

1940s, using a Cartesian coordinate system.(Spiegel, Wycis, Marks, & Lee, 1947) Subsequent

development for use with emerging cross-sectional imaging modalities, including computed

tomography (CT) and magnetic resonance imaging (MRI), led to the introduction of rigid frame

and arc localization systems including the eponymous Leksell frame, Brown-Roberts-Wells

(BRW) and Cosman-Roberts-Wells (CRW) systems, and the lesser-used Zamorano-Dujovny and

Patil frames.(Patil, 1984; T. Roberts, 1998; L. Zamorano, 1999) In each of these systems, the

rigid frames are affixed to the skull using pins under local anesthesia, to establish a fixed

relationship between the patient’s skull and the frame ± arc localizer, allowing the rapid and

accurate targeting of intracranial structures due to the known and constant relationship between

skull anatomy and frame system.

Figure 2-1. The earliest frame-based stereotaxy. Clarke and Horsley’s primate stereotactic apparatus. Reprinted

from Pereiera et al., Stereotactic Neurosurgery in the United Kingdom: The Hundred Years from Horsley to Hariz.

Neurosurgery 2008;63(3):594-607, by permission of Oxford University Press.

4

2.1.1 History of Frameless Stereotaxy

With rapid advances in imaging and computing power over the past three decades, frameless

stereotaxy, also termed image-guided surgery (IGS), neuronavigation, or computer-assisted

navigation (CAN), was pioneered again first in a neurosurgical context. While frame-based

stereotaxy is still used for biopsies and implantation of depth electrodes for neuronal recording,

stimulation or ablation, the development and iterative improvement in frameless stereotaxy has

allowed the extension of stereotactic guidance to craniotomies for a variety of indications, as

well as to spinal procedures, the focus of this thesis. The use of CAN is particularly critical in

neurosurgical applications, as delicate neuronal tissues limit the corridors of direct visualization

available in many other surgical disciplines.

Frameless stereotaxy, by virtue of its lack of a rigid mechanical linkage between the patient

anatomic space and the instrument space, as accomplished by frame ± arc localizers, therefore

requires the matching of patient and device (or image) spaces, a process termed registration. The

first clinical application of frameless stereotaxy was by Friets and Roberts et al. in the 1980s,

where an operating microscope with ultrasonic emitters was placed in an operating room (OR)

with surrounding microphones outside the operating field to localize the position of the

microscope in relation to the patient, allowing the injection of a target point on cross-sectional

imaging into the microscope oculars (Figure 2-2).(Friets, Strohbehn, Hatch, & Roberts, 1989;

David W. Roberts, Strohbehn, Hatch, Murray, & Kettenberger, 1986; D W Roberts, Hartov,

Kennedy, Miga, & Paulsen, 1998) Groundwork for subsequent integration of cross-sectional

imaging into the patient anatomic space, a pre-requisite for modern CAN, was laid by the work

of Kelly and others, by reconstructing volumetric data from CT and later MRI into 3D

space.(Kelly, 1990) The first frameless CAN devices, tracking pointers initially using ultrasonic

emitters and subsequently magnetic sources and infra-red (IR) light-emitting diodes (LEDs),

were developed in the late 1980s and early 1990s for intracranial and otolaryngologic

applications, first with arm-based systems and subsequently with armless devices.(Kato et al.,

1991; Kosugi et al., 1988; Mösges & Schlöndorff, 1988; Reinges, Spetzger, Rohde, Adams, &

Gilsbach, 1998; H. Reinhardt, Meyer, & Amrein, 1988; Watanabe, Mayanagi, Kosugi, Manaka,

5

& Takakura, 1991; Watanabe, Watanabe, Manaka, Mayanagi, & Takakura, 1987; L. J.

Zamorano, Nolte, Kadi, & Jiang, 1993)

Figure 2-2. An early frameless stereotactic navigation system. Photograph of one of the first clinical frameless

stereotactic navigation systems, employing an operating microscope (top right) fitted with an array of spark-gap

ultrasonic emitters (top left) for tracking of the microscope position relative to the patient, to allow injection of a target

into the microscope oculars using a beam-splitting device (bottom right). Reprinted from Roberts et al., A Frameless

Stereotaxic Integration of Computerized Tomographic Imaging and the Operating Microscope. JNS 1986;65(4):545-9,

by permission of the JNS Publishing Group.

2.1.2 History of Spinal Computer-Assisted Navigation

Prior to the advent of CAN, intra-operative navigation in the spine was typically performed using

a combination of anatomic knowledge as well as radiographic feedback from serial XR (X-rays)

6

or fluoroscopy. Additional feedback on the integrity of adjacent neural elements was, and

continues to be, obtained with the use of various electromyography (EMG) and direct

stimulation-based neuromonitoring techniques.(Holly & Foley, 2003) While plain XR remains

useful for the initial localization of a skin incision or vertebral levels, it is associated with a

significant time lag particularly when digital radiograph processing units are unavailable, and

provides only a single temporal snapshot. Poor image quality due to metallic artifact, bony

obstruction or large patient body habitus, requires repeated XR and therefore increases this time

cost. C-arm fluoroscopy has therefore traditionally been the imaging modality of choice for

many spinal surgeons for intra-operative guidance. C-arms may provide a single XR snapshot for

incision and anatomic level localization, and may also acquire continuous images to allow for

real-time localization of instruments in the operative field. However, this practice is associated

with significant occupational radiation exposure, particularly to the surgical team. Extensive

investigation has been performed on the cumulative radiation dose from C-arm fluoroscopy to

various parts of the surgeon’s body, at various positions around the operative table (i.e. on the

side of the detector vs. emitter), with varying patient body habitus, and with the duration of

fluoroscopy.(Mroz, Abdullah, Steinmetz, Klineberg, & Lieberman, 2011; Mulconrey, 2016;

Rampersaud, Foley, Shen, Williams, & Solomito, 2000; H. E. Smith, Welsch, Sasso, & Vaccaro,

2008) Moreover, standard C-arm fluoroscopy only provides a single in-plane view, with multiple

planes possible only with the introduction of a second orthogonally-positioned C-arm or by

moving the single C-arm back and forth to the required planes, a cumbersome and

ergonomically-disruptive exercise (Figure 2-3).(Tjardes et al., 2010; Xu et al., 2014)

7

Figure 2-3. Intra-operative biplane fluoroscopy. Typical operating room setup when two C-arm units are required

concurrently for biplane (antero-posterior and lateral) views for spinal instrumentation guidance. Reprinted from Xu et

al., A Method of Percutaneous Vertebroplasty Under the Guidance of Two C-Arm Fluoroscopes. Pak J Med Sci

2014;30(2):335-8, under the Open Access Creative Commons Attribution License 3.0.

Extension of computer-assisted navigation from cranial to spinal procedures was therefore a

natural target. The evolution from early arm-based CAN techniques to armless systems was

enabled by the development of dynamic reference frames (DRFs), consisting of an

electromagnetic coil or active or passive IR-LED arrays affixed to rigid bony anatomy. This

allowed the tracking of instruments in the patient space without rigid anatomic fixation, as is

typically accomplished in cranial neurosurgery with the use of rigid head fixation devices such as

the Mayfield or Sugita clamps, but is not feasible in spinal approaches.(Grunert, Darabi,

Espinosa, & Filippi, 2003) Kalfas et al. were the first to adapt frameless stereotaxy for clinical

use in the spine in the mid-1990s, using a wand fitted with ultrasonic emitters allowing tracking

using sonic digitizers placed around the operating field, registered to a pre-operatively-acquired

volumetric CT dataset (Figure 2-4).(Kalfas et al., 1995; Murphy, McKenzie, Kormos, & Kalfas,

8

1994) Subsequent development in the late 1990s expanded the scope of spinal CAN to include

updating of imaging in real-time intra-operatively using C-arm fluoroscopy, termed ‘virtual

fluoroscopy’.(Foley, Simon, & Rampersaud, 2001; T.-S. Fu et al., 2004) Unfortunately, virtual

fluoroscopy systems remained limited to 2D projection images, without true multiplanar views in

the axial, sagittal and coronal planes.(Helm, Teichman, Hartmann, & Simon, 2015) 3D CAN

systems, based initially on pre-operative CT imaging and subsequently on intra-operative mobile

CT as as well as isocentric C-arm fluoroscopy, therefore arose to the forefront starting in the

early 2000s.(Euler, Heining, Fischer, Pfeifer, & Mutschler, 2002; Nolte et al., 2000; Waschke et

al., 2013) These early imaging devices remained limited by poor image quality and cumbersome

workflow. A major step forward in spinal intra-operative imaging and navigation was taken in

2006, with the introduction of the O-Arm™ by Breakaway Imaging (now Medtronic), allowing

360° cone-beam CT-quality imaging with a breakable gantry facilitating movement around the

operating table, and automatic registration to the patient spinal anatomy.(Helm et al., 2015) A

full discussion of contemporary spinal CAN imaging and registration techniques is presented in

Section 2.2 of this chapter.

9

Figure 2-4. The first spinal intra-operative navigation system. Photograph of the acoustic frameless stereotactic

navigation system devised by Kalfas et al. for spinal pedicle screw guidance. The black sonic digitizer is mounted on

a platform next to the operating table. Reprinted from Kalfas et al., Application of Frameless Stereotaxy to Pedicle

Screw Fixation of the Spine. JNS 1995;83(4):641-7, by permission of the JNS Publishing Group.

2.1.3 Current Applications of Spinal Computer-Assisted Navigation

The first spinal CAN systems were used to guide the placement of lumbar pedicle

instrumentation.(Kalfas et al., 1995; Murphy et al., 1994) In the two decades following,

instrumentation placement remains the primary application of contemporary spinal image-

guidance systems, with a body of literature encompassing over 10,000 pooled pedicle

screws.(Overley, Cho, Mehta, & Arnold, 2017) CAN systems have been used to guide pedicle

screws from the atlantoaxial (C1-C2) and subaxial cervical spine,(Shimokawa & Takami, 2016b;

J. D. Smith, Jack, Harn, Bertsch, & Arnold, 2016) down to the sacrum and pelvis.(Ray,

Ravindra, Schmidt, & Dailey, 2013; J. H. Shin, Hoh, & Kalfas, 2012) While there is literature to

suggest that the freehand placement of standard posterior thoracolumbar and sacral pedicle

10

screws may be safe in highly-trained hands, CAN systems have expanded the accessibility of

accurate and safe placement of instrumentation at these levels, particularly in revision and

deformity-correction cases where typical anatomic landmarks are distorded, as discussed in

greater detail in Section 2.1.3.1 of this chapter.(Fridley, Fahim, Navarro, Wolinsky, & Omeis,

2014; Y. J. Kim, Lenke, Bridwell, Cho, & Riew, 2004) CAN has also facilitated novel

instrumentation approaches that are otherwise feasible only with repeated fluoroscopy, including

odontoid screw placement at C2,(Pisapia et al., 2017) oblique prepsoas and extreme lateral

transpsoas approaches to the lumbar spine,(DiGiorgio, Edwards, Virk, Mummaneni, & Chou,

2017; Joseph, Smith, Patel, & Park, 2016) as well as percutaneous and minimally-invasive

instrumentation at all spinal levels.(T. T. Kim, Drazin, Shweikeh, Pashman, & Johnson, 2014; T.

T. Kim, Johnson, Pashman, & Drazin, 2016; Komatsubara, Tokioka, Sugimoto, & Ozaki, 2016;

Nakashima, Sato, Ando, Inoh, & Nakamura, 2009)

The utility of modern CAN techniques has expanded from instrumentation to guidance and

confirmation of the extent of decompression. Navigation guidance for anterior transoral

approaches to the craniocervical junction, for inflammatory and neoplastic etiologies, has been

reported as early as 2003 by Vougioukas et al..(Vougioukas, Hubbe, Schipper, & Spetzger,

2003) More recently, anterior CAN-guided subaxial cervical transcorporal tunnel approaches to

treat focal pathology underlying cervical myelopathy, have been reported.(Quillo-Olvera, Lin,

Suen, Jo, & Kim, 2017) Ligamentous decompression with MIS epiduroscopic laser ablation in

the lumbar spine may also be guided by modern CAN techniques.(Jeon et al., 2015) Osteotomies

for correction of spinal alignment, with or without instrumentation, may also be guided by CAN

techniques in order to precisely plan, pre-operatively, and subsequently execute, intra-

operatively, the specific bony extirpations required to achieve a desired alignment.(Metz &

Burch, 2008) In an oncologic context, the extent of osseous and soft-tissue tumour

decompression may be guided and confirmed by intra-operative CAN, particularly when coupled

with CT/MRI fusion techniques and intra-operative imaging.(Bandiera et al., 2013) CAN

guidance may facilitate less invasive ‘separation surgery’ for metastatic epidural disease,

whereby a transpedicular approach is used to resect sufficient tumour lateral and ventral to the

spinal cord to allow safe high-dose fractionated radiation therapy with minimal

neurotoxicity.(Nasser et al., 2018) At the extremes of minimally-invasive surgery, CAN may

11

guide percutaneous catheters for laser interstitial thermal therapy (LITT), with the purpose of

ablating epidural tumour similar to ‘separation surgery’.(Tatsui et al., 2017) By merging pre-

operative MRI and intra-operative 3D fluoroscopic images, CAN may also be facilitate the

resection of intradural tumours, by minimizing the extent of soft-tissue and bony exposure and,

for intrinsic spinal cord tumours, centering the tumour to more precisely localize the midline

myelotomy.(Stefini, Peron, Mandelli, Bianchini, & Roccucci, 2017)

While infusion and neuromodulatory therapies for spinal chronic pain conditions are typically

performed safely and easily freehand, such as with the implantation of dorsal root ganglion

stimulation electrodes, CAN may be useful in cases of severely distorted or disrupted anatomy,

such as in one series of intrathecal baclofen pump catheter implantation in cerebral palsy patients

with severe neuromuscular scoliosis.(Robinson et al., 2017)

Finally, CAN may play a significant role in trainee surgeon education. While expert spine

surgeons are able to place instrumentation safely freehand or with fluoroscopic guidance, the

real-time visualization and verification of anatomic landmarks and proposed trajectories that is

afforded by intra-operative CAN may be a useful adjunctive pedagogic tool. To date, seven

studies have reported on the use of CAN for surgical resident and clinical fellow education, all in

the context of ex-vivo virtual reality (VR) or cadaveric or phantom simulations.(Gasco et al.,

2014; Michael B. Gottschalk, Yoon, Park, Rhee, & Mitchell, 2015; Lorias-Espinoza, Carranza,

de León, Escamirosa, & Martinez, 2016; Luciano et al., 2011; Podolsky et al., 2010; Rambani,

Ward, & Viant, 2014; Sundar et al., 2016) The consensus from these studies, through self-

reported surveys, is that CAN simulation is a useful exercise for training novice learners.

Objective improvement, however, in simulation performance such as the placement of

instrumentation, with CAN guidance, remains less well established. Gasco et al., Rambani et al.,

and Sundar et al. found significant improvements in simulation performance for screw placement

with CAN-based simulation training, while Gottschalk et al. found improvement only in screw

trajectory but not in entry point placement, and Podolsky et al. found no improvement in

radiographic screw accuracy.

12

2.1.3.1 Rationale for Spinal Computer-Assisted Navigation

CAN was applied to spinal procedures initially for the guidance of lumbar pedicle screws.

Instrumentation guidance remains the primary application for CAN by most spinal surgeons,

with multiple systematic reviews and meta-analyses reporting on the radiographic accuracy of

pedicle screws in the cervical, thoracic and lumbosacral spine, and in multiple clinical contexts

including minimally-invasive percutaneous instrumentation as well as in adolescent idiopathic

scoliosis patients.(L. P. Amiot, Lang, Putzier, Zippel, & Labelle, 2000; Austin C Bourgeois et

al., 2015; Chan, Parent, Narvacan, San, & Lou, 2017; Du et al., 2018; Gelalis et al., 2012;

Luther, Iorgulescu, Geannette, Gebhard, Saleh, Tsiouris, & Härtl, 2015; Mason et al., 2014; B. J.

Shin, James, Njoku, Hartl, & Härtl, 2012; N. F. Tian et al., 2011; Verma, Krishan, Haendlmayer,

& Mohsen, 2010) Comparison of radiographic accuracy of pedicle screw placement between

navigated and freehand, or conventional fluoroscopy, technqiues was made most recently in a

meta-analysis by Mason et al..(Mason et al., 2014) This analysis found an overall radiographic

accuracy rate, across all spinal regions, of 68.1% for freehand techniques, vs. 84.3% for 2D

navigation and 95.5% for 3D navigation, with all 3D techniques pooled (isocentric fluoroscopy,

cone-beam CT, fan-beam CT). Primary studies included in this meta-analysis are shown in Table

2-1, and a stratification of accuracy by spinal region is shown in Figure 2-5. Further stratification

of navigated pedicle screw accuracy was made most recently by Du et al., in a meta-analysis

comparing specifically 3D fan-beam CT vs. 3D isocentric fluoroscopy (Table 2-2).(Du et al.,

2018) Interestingly, although the diagnostic accuracy of isocentric fluoroscopy for intra-

operative pedicle breach identification has compared favourably to post-operative CT in prior

studies,(Qureshi, Lu, McAnany, & Baird, 2014) the analysis by Du et al. found greater

radiographic screw accuracy with isocentric fluoroscopy rather than CT-based navigation. This

has potential implications from a hospital/departmental purchasing perspective, whereby less-

costly isocentric fluoroscopy units may become more attractive in the context of spinal

navigation. Within CT-based systems, there does not appear to be a significant difference in

accuracy between systems registering to pre- vs. intra-operatively-acquired CT, however in

current paradigms the intra-operative CT-based systems register significantly faster due to

13

automatic registration protocols.(Francesco Costa et al., 2011) Nooh et al. report that differences

in accuracy may exist even across manufacturers of CAN devices employing similar registration

and imaging modalities.(Nooh et al., 2017)

Figure 2-5. Radiographic accuracy of pedicle screws. Boxplots comparing the accuracy of pedicle screws across

all regions (A), and specifically in the thoracic spine (B) and lumbosacral spine (C). placed using

freehand/conventional fluoroscopy guidance vs. 2D navigation vs. 3D navigation. Boxes represent the interquartile

range; black lines within boxes represent the mean screw accuracy; error bars represent minimum and maximum

values. Reprinted from Mason et al., The Accuracy of Pedicle Screw Placement Using Intraoperative Image

Guidance Systems. JNS: Spine 2014;20(2):196-203, by permission of the JNS Publishing Group.

14

Table 2-1. Studies of pedicle screw accuracy. Articles are stratified by spine region and insertion technique.

Reprinted from Mason et al., The Accuracy of Pedicle Screw Placement Using Intraoperative Image Guidance

Systems. JNS: Spine 2014;20(2):196-203, by permission of JNS Publishing Group.

15

16

Table 2-2. Studies of pedicle screw misplacement. Articles are stratified by insertion technique, specifically with

distinction among 3D CT vs. fluoroscopic guidance. Reprinted from Du et al., Accuracy of Pedicle Screw Insertion

Among 3 Image-Guided Navigation Systems: Systematic Review and Meta-Analysis. World Neurosurg 2018;109:24-

30, by permission of Elsevier.

17

As the benefits of CAN are most evident in MIS and deformity-correcting procedures, where

anatomic landmarks are less readily identifiable, the advantage of CAN in potentially reducing

intra-operative fluoroscopy and its associated radiation cost in MIS procedures, has also come

under significant investigation. Intra-operative fluoroscopy, the current gold-standard for the

evaluation of real-time instrument positioning and spinal alignment, is associated with an

average dose to the surgeon of 53.3 mrem/min at the torso in one study, greater in the hand and

less in the neck, with variation in dose based on distance from the beam source and patient body

habitus.(Rampersaud et al., 2000) A multitude of subsequent studies has demonstrated reduced

occupational radiation dose, i.e. to OR personnel, with 3D fluoroscopy-based navigation,(Foley

et al., 2001; Izadpanah, Konrad, Südkamp, & Oberst, 2009; Schafer et al., 2011) as well as with

intra-op CBCT,(Abdullah et al., 2012; Bandela et al., 2013; Mendelsohn et al., 2016) relative to

standard C-arm fluoroscopy. However, while CAN reduces the radiation exposure to surgical

and OR personnel, it does appear that this is more a result of shifting the burden of radiation to

the patient rather than a reduction in overall radiation exposure. Early spinal CAN systems

registered to pre-operative CT imaging, with the radiation cost to the patient of dedicated spinal

imaging exceeding that of any other non-spinal musculoskeletal CT imaging by 10-12

fold.(Biswas et al., 2009) With the development of more advanced CAN techniques registered to

intra-operative imaging, the argument has been made that intra-operative 3D fluoroscopy or

CBCT can both guide instrumentation as well as provide post-implantation imaging to check

hardware accuracy, as a replacement for the otherwise obligate post-operative CT scan.

However, particularly with larger patients, with longer instrumentation constructs, or with any

inadvertent shifting of the DRF intra-operatively or other source of navigation error, multiple

intra-operative imaging sequences may be required. Lange et al. have estimated that 3 or more

intra-operative O-Arm imaging cycles, at standard manufacturer-recommended dosing, results in

patient radiation exposure equivalent to that one of standard abdominal CT scan.(Lange et al.,

2013) Therefore, while CAN techniques may reduce occupational radiation exposure for OR

personnel, particularly in traditionally fluoroscopy-heavy procedures including MIS and

deformity corrections, the burden of radiation exposure remains, and in the current paradigm of

CAN techniques is shifted to the patient rather than eliminated entirely.(Bandela et al., 2013)

18

With an increasing focus on value-based health care and efficiency optimization, CAN

techniques have also been purported to improve surgical temporal workflow thereby reducing

costly OR time.(G. Fan et al., 2017) Prolonged operative times have been associated with

increased blood loss and more frequent infectious and ischemic complications,(Baig et al., 2007)

though certainly there remains significant equipoise on this point in the literature, and the

majority of surgical morbidity likely remains secondary to patient comorbidities and the treated

pathology rather than operative time alone.(Fogarty, Khan, Ashall, & Leonard, 1999) While

perhaps not dramatically reducing operative times compared to traditional fluoroscopy-guided or

freehand techniques, the use of CAN appears to be at least time-equivalent. In a comparative

study of O-Arm (3D cone-beam CT) vs. fluoroscopy guidance for MIS lateral interbody lumbar

fusions, Zhang et al. demonstrated a statistically-insignificant increase in operative time with

CAN guidance.(Y.-H. Zhang, White, Potts, Mobasser, & Chou, 2017) However, Sasso et al.

demonstrated a statistically-significant time savings in posterior L5-S1 fusions with 3D

fluoroscopy vs. serial XR.(Sasso & Garrido, 2007) In a cadaveric setting, Webb et al. found total

operative time-equivalence for CAN-guided lateral interbody thoracolumbar fusion vs.

fluoroscopy.(Webb, Regev, Garfin, & Kim, 2010) In larger in-vivo comparative studies, both

Rajasekaran et al. and Tabaraee et al. found time-equivalence for 3D CBCT-based navigation vs.

fluoroscopy for the placement of posterior thoracolumbar pedicle screws.(Rajasekaran,

Vidyadhara, Ramesh, & Shetty, 2007; Tabaraee et al., 2013) While a temporal efficiency benefit

to CAN has yet to be demonstrated with current paradigms of navigation, there does appear to be

a significant learning curve, with increased operative times early in the curve followed by time

equivalence or even modest savings once sufficient familiarity has been achieved. While no

specific number of cases to achieve ‘competence’ has been postulated in the literature, a

significant improvement in radiographic instrumentation accuracy was observed by Wood et al.

after 50 cases of CT CAN-guided MIS lumbar pedicle screw placement.(Wood & McMillen,

2014) Ryang et al. demonstrated substantial and statistically-significant continual improvements

in both temporal efficiency and radiographic instrumentation accuracy with 3D-fluoroscopy

guided open thoracolumbar pedicle screw placement.(Ryang et al., 2015) In fact, the learning

curve in Ryang et al.’s study extended to the radiology technicians operating the 3D-fluoroscopy

CAN system, with continual improvements in scan time over the duration of the study. It

therefore appears from the body of literature that while the current paradigm of spinal CAN does

not offer significant workflow improvements relative to traditional fluoroscopy, time-

19

equivalence can typically be achieved following a significant learning curve for both surgeons

and involved OR personnel.

In part from purported time savings, and in greater part from a potential reduction in

complications and subsequent reoperations from misplaced instrumentation, an argument in

favour of spinal CAN usage has been made from an economic and cost-effectiveness

perspective. The literature on this subject is only recently beginning to expand, partly because

comparative data on clinical complications and reoperation rates from CAN vs. traditionally-

guided instrumentation has required long-term follow-up for adequate analysis. In the earliest

economic analysis of CAN guidance, Watkins et al. found a non-statistically-significant

reduction in revision surgeries for misplaced hardware with 3D-fluoroscopy guidance (0.2%, vs.

3% with traditional fluoroscopy), with an associated cost of revision surgery of USD $23,762

assuming a hospital stay of two nights.(Watkins, Gupta, & Watkins, 2010) Their navigation

system of choice, a 3D-fluoroscopy unit, had an upfront cost of USD $475,000, not including

annualized maintenance costs. In more recent studies, Hodges et al. approximated a 1% rate of

revision surgery for thoracolumbar pedicle screws placed with traditional C-arm fluoroscopy, vs.

0% with O-Arm CBCT guidance, at an average revision surgery cost of $17,650.(Hodges, Eck,

& Newton, 2012) Sanborn et al. concluded that intra-operative O-Arm CBCT imaging was a

cost-effective alternative to neuromonitoring or post-operative CT scanning for the confirmation

of screw placement, albeit with a flawed analysis that accounted only for personnel costs of the

imaging or monitoring techniques, and therefore attributed a cost of zero to O-Arm imaging.(M

R Sanborn et al., 2012) Costa et al. compared OR costs of instrumented fusion procedures using

a pre-operative CT-based CAN device vs. an O-Arm CBCT-based device, and concluded a non-

significant cost savings of only 3.8% with CBCT due entirely to an average time savings of 27

minutes using intra-operative imaging as a result of the automated registration protocol, with no

difference in clinical complications.(F Costa et al., 2014) In the most thorough analysis to date,

Dea et al. performed a retrospective comparative study of a prospectively-maintained cohort of

patients undergoing posterior spinal instrumentation with either O-Arm CBCT-CAN or standard

C-arm fluoroscopy. They concluded a cost of reoperation of CAD $12,618, and a statistically-

significant reduction in revision surgery rate of 5.2% with CAN guidance, thereby concluding

cost-effectiveness of the CAN technique if more than 254 instrumented cases per year are

20

performed at a given institution.(Dea et al., 2016) In this study, as a result of higher revision

surgery costs, cost-effectiveness in the United States was achieved at a fewer number of cases,

168 per year. Recent data supports improved short-term clinical outcomes with CAN usage, with

reduced 30-day reoperation rates for hardware malposition-related neurovascular complications

as well as wound infections.(Fichtner et al., 2017; Luther, Iorgulescu, Geannette, Gebhard,

Saleh, Tsiouris, & Härtl, 2015; Xiao et al., 2017) When long-term complications of misplaced

hardware, including poor osseous fusion and construct loading leading to junctional failure, are

taken into account, the economic argument in favour of CAN likely becomes more

robust.(Acikbas, Arslan, Tuncer, Matge, & Muciejczak, 2003)

Despite the increasing range of applications for spinal CAN described in the literature,

summarized in Section 2.1.3, adoption of CAN among spinal surgeons remains limited, without

establishment of the technology as standard of care.(Schröder & Wassmann, 2006) In the only

study to date quantifying the current state of navigation usage, Hartl et al. surveyed a worldwide

population of 3348 spinal surgeons, predominantly based in Europe, Latin America and the Asia

Pacific region, and found a worldwide CAN usage rate of only 11%.(Hartl et al., 2013) By

contrast, 78% of surgeons in the same survey reported using fluoroscopy as their primary method

of intra-operative image guidance. In separate surveys by Hartl et al. and Choo et al., the

predominant barriers to spinal CAN adoption were a definitive lack of evidence supporting

improved accuracy, workflow disruption primarily from cumbersome registration protocols, high

capital costs, increased radiation exposure to either the patient and/or OR personnel, and steep

learning curves.(Choo, Regev, Garfin, & Kim, 2008; Hartl et al., 2013) With a body of literature

reporting the safe and accurate placement of thoracic pedicle screws with freehand technique in

highly-experienced hands,(Y. J. Kim et al., 2004) and lack of definitive clinical benefit and

complication reduction with the use of CAN, albeit in short-term follow-up,(Wagner et al., 2017)

it is unsurprising that significant barriers remain to the widespread of adoption of CAN.

21

2.2 Registration, Imaging and Actuation Techniques in Spinal Computer-Assisted Navigation

The basic tenet of intra-operative computer-assisted image guidance is real-time correlation of

cross-sectional imaging data to patient anatomy, to provide surgeons with a view of structures

that cannot otherwise be visualized directly. A unifying requirement for any frameless

stereotactic navigation technique, in the spine or elsewhere, is registration of the imaging and

patient spaces. The imaging dataset to be registered to, and the technique for registration itself,

vary widely among various CAN techniques. A brief summary of imaging and registration

techniques in contemporary CAN systems is presented in this section.

2.2.1 2D Navigation

While the first published spinal CAN system provided 3D navigation based on a preoperative CT

(see Section 2.1.2),(Kalfas et al., 1995) the prevalence and relative compactness of C-arm

fluoroscopy units rendered 2D navigation, or ‘virtual fluoroscopy’, the next step in evolution.

Imaging in 2D navigation is performed using a standard C-arm fluoroscope modified with an

attached calibration target. In typical workflow, a dynamic reference frame (DRF) is affixed to

rigid patient anatomy, and XR images are taken with the C-arm in the planes desired for

navigation (typically a cross-table lateral for sagittal views, and an antero-posterior (AP) view).

As most ‘virtual fluoroscopy’ systems rely on optical instrument tracking (discussed further in

Section 2.2.3), a separate IR camera tracks the relative position of the C-arm (specifically the

attached calibration target) and patient-mounted DRF during XR imaging, and automatically

computes the transformation matrix required to register patient and image spaces. The position of

tracked instruments can then be overlaid on the multiplanar XR images to allow for real-time

navigated surgery with only the single fluoroscope(Figure 2-6).(Foley et al., 2001)

Unfortunately, as their name belies, 2D navigation systems are hampered by their inability to

display reconstructed axial views, leaving surgeons to mentally reconstruct the imaged planes

into a 3D structure. Moreover, as with all plain radiographs, the quality of navigation images is

22

entirely dependent on the quality of the initial acquisition XR, leaving much to be desired in

obese or osteopenic patients.(Helm et al., 2015; Holly & Foley, 2003) As 2D navigation

techniques have largely fallen out of favour due to their lack of multiplanar (MPR)

reconstruction, the remainder of this thesis will focus fully on 3D CAN technqiues.

Figure 2-6. ‘Virtual’ (2D) fluoroscopy. Navigation screenshot of an odontoid screw guided by virtual fluoroscopy.

Navigated instrument trajectory (grey + white lines) is overlaid on lateral (left) and antero-posterior (right) XR views.

Reprinted from Holly et al., Intraoperative Spinal Navigation. Spine 2003;28(15S):S54-61, by permission of Wolters

Kluwer Health, Inc.

23

2.2.2 3D Navigation

By definition, 3D CAN techniques provide full multiplanar reconstruction of cross-sectional

imaging, displaying the relevant anatomy in axial, sagittal and coronal planes and, depending on

the software package used, in a 3D reconstruction. All current spinal 3D CAN techniques are

reliant on a DRF for maintaining image-to-patient registration and instrument tracking. The

imaging modalities and techniques used to register this imaging to real-world anatomy vary

among systems, and are discussed further in this section.

2.2.2.1 Imaging Techniques

Imaging for navigation in 3D CAN techniques may be acquired either pre- or intra-operatively.

Among systems registering to pre-operative imaging, the most common imaging dataset is spiral

CT, best if acquired at high-resolution thin slices of thickness <2mm.(Herz, Franz, Giacomuzzi,

Bale, & Krismer, 2003) The typical workflow for pre-operative CT-based techniques involves

transferring of Digital Imaging and Communications in Medicine (DICOM) files to the CAN

workstation pre-operatively. A DRF is affixed to the patient, and manual registration of the pre-

operative CT dataset to patient anatomy is then performed using one of three techniques,

described in greater detail in Section 2.2.2.2. Following successful image-to-patient registration,

MPR views of the registered anatomy are displayed on a screen, and image-guided surgery may

proceed. Pre-operative CAN systems remain of value as they obviate the need for bulky intra-

operative imaging devices, particularly in the case of intra-operative CT scanners, that are costly

to both acquire and maintain, often require specially-trained personnel to operate, and image at

lower resolution than the fan-beam medical-grade scanners used for pre-operative imaging.

However, the manual registration protocol of current CAN systems registering to pre-operative

imaging significantly increases operative times,(Parker et al., 2011) and necessitates a pre-

operative CT scan with its associated cost and patient radiation exposure.(A C Bourgeois,

Faulkner, Pasciak, & Bradley, 2015; Holly & Foley, 2003) The accuracy of the initial

registration is also highly operator-dependent, as the quality of selected points for paired-point

24

matching can significantly impact the robustness of the resultant transformation matrix.(Tamura

et al., 2005) Moreover, as these systems rely on an imaging dataset acquired with the patient in a

supine position, whereas most navigated spinal procedures are performed in the prone position,

intervertebral spinal mobility due to positioning may result in significant navigation inaccuracy,

particularly when operating at levels distant to which the DRF is affixed.(A C Bourgeois et al.,

2015; Holly & Foley, 2003; Kalfas et al., 1995; Ringel, Villard, Ryang, & Meyer, 2014) The

lack of associated intra-operative imaging also eliminates the ability to assess instrumentation

placement or spinal alignment without a separate intra-operative imaging device, or a dedicated

post-operative CT scan with its associated cost and patient radiation exposure.

In an effort to overcome some of these drawbacks associated with registration to pre-operative

imaging, 3D CAN systems with intra-operative imaging devices were developed. 3D intra-

operative imaging techniques include isocentric fluoroscopy, or ‘3D fluoroscopy’, cone-beam

CT (CBCT), and fan-beam CT (FBCT)(Figure 2-7). Isocentric fluoroscopy devices employ a C-

arm which is motorized to automatically rotate a fixed amount (typically 190°), centred about a

point in the spine chosen by the operator (hence ‘isocentric’). A DRF is affixed to patient

anatomy, as with all modern CAN techniques, and imaging proceeds with subsequent automatic

transferring of images to the CAN workstation and image-to-patient registration. The first CBCT

device, the O-Arm™ (Medtronic Sofamor Danek; Memphis, TN, USA), was introduced in 2006

and employs a similar fluoroscopy unit with flat-panel detector as isocentric fluoroscopy

devices.(Helm et al., 2015) The O-Arm is differentiated from isocentric fluoroscopy by its ability

to rotate a full 360°, optimizing volume sampling and minimizing reconstruction artifacts due to

limited projection views that are prominent with isocentric fluoroscopy devices. This is

permitted by its toroid form factor, with a breakable gantry allowing lateral access to the

operating table. DRF placement and automatic image transfer and registration otherwise

proceeds similar to isocentric fluoroscopy devices. The latest evolution in intra-operative

imaging devices is mobile multi-row fan-beam CT imagers, more similar in design to the fixed

scanners found in radiology departments than to the O-Arm.(Helm et al., 2015) These devices,

examples of which include the BodyTom™ (Samsung Electronics America; Ridgefield Park, NJ,

USA) and Airo™ (Brainlab AG; Munich, Germany), offer significantly better contrast resolution

than flat-panel cone-beam devices (isocentric fluoroscopy and CBCT) with comparable imaging

25

times. With a pre-calibrated relationship between CT gantry and operating table (and therefore

patient) position, image transfer and registration are automatic as with isocentric fluoroscopy and

CBCT devices. However, FBCT systems are associated with significantly greater capital cost,

due in part to the requirement for a dedicated operating room with proprietary attached operating

table to facilitate ingress/egress from the CT gantry. Without flat-panel detectors as with

standard C-arm fluoroscopes, FBCT devices also lack real-time fluoroscopy capabilities that are

useful for initial incision and vertebral level localization.(A C Bourgeois et al., 2015; Helm et al.,

2015) The adoption of FBCT systems has therefore been limited largely to highly-specialized

academic institutions, with an unclear role in the future of spinal CAN. Nonetheless, each of the

three intra-operative imaging technqiues has advanced spinal CAN, by improving workflow

through automated registration protocols, and providing real-time feedback on intra-operative

spinal alignment and hardware placement through repeat imaging cycles.

There is ample evidence in the literature to suggest that all intra-operative imaging techniques

reduce cumulative radiation dose to the surgeon and OR staff, as these personnel are typically

able to leave the OR while navigation and verification scans are being performed via automated

motorized actuation. Villard et al. demonstrated that the use of isocentric fluoroscopy, to guide

lumbar pedicle instrumentation as well as verify hardware position post-implantation intra-

operatively, reduces surgeon radiation exposure by almost 10-fold relative to standard

fluoroscopy.(Villard et al., 2014) They also found that the cumulative radiation dose to the

patient was halved, largely due to avoidance of a post-operative CT to verify screw position. In

more recent studies of CBCT systems, Costa et al. found an intra-operative radiation dose to

surgical staff that was essentially negligible, however with increased intra-operative exposure to

the patient relative to standard fluoroscopy.(Francesco Costa et al., 2016) Similarly, Mendelsohn

et al. found increased patient radiation exposure by 2.77 fold with CBCT navigation relative to

literature values for fluoroscopy-guided thoracolumbar instrumentation, however with negligible

surgeon radiation exposure.(Mendelsohn et al., 2016) Interestingly, they also found no difference

in the need for post-operative XR or CT in patients who had undergone a navigated vs. non-

navigated procedure, although their institution did not routinely perform intra-operative post-

implantation verification CBCT scans, which might otherwise have reduced the need for post-

operative scanning in the navigated cohort. Nonetheless, while all intra-operative imaging

26

devices reduce radiation exposure to OR personnel, the burden of exposure is shifted somewhat

to patients, to a significantly greater extent for CBCT/FBCT systems than with isocentric

fluoroscopy, with the tradeoff of improved image quality and potentially navigation accuracy

with CT systems.

27

Figure 2-7. Intra-operative imaging technqiues for 3D CAN. Photographs demonstrating the intra-operative setup

of mobile cone-beam CT (top left), isocentric fluoroscopy (top right), and fan-beam multidetector CT (bottom). Each

technique is associated with significant bulk and cumbersome additional draping requirements for the patient and/or

the imaging device. Top images reprinted from Bourgeois et al., The Evolution of Image-Guided Lumbosacral Spine

Surgery. Ann Trans Med 2015;3(5):69, by permission of the Society for Translational Medicine. Bottom image

reprinted from Tormenti et al., Intraoperative Computed Tomography Image-Guided Navigation for Posterior

Thoracolumbar Spinal Instrumentation in Spinal Deformity Surgery. Neurosurg Focus 2010;28(3):E11, by permission

of the JNS Publishing Group.

28

2.2.2.2 Registration Techniques

A fundamental requirement for any frameless stereotaxis is to identify a fixed relationship

between patient and image spaces, a process termed registration. A transformation must be

computed, which allows the mapping of any point in anatomical (real-world) space to its

corresponding point in image space, thereby allowing instrument tracking in both

environments.(Grunert et al., 2003) Broadly, the transformation (φ) linking corresponding points

or surfaces in imaging and patient spaces consists of a linear translation (�⃗�) and a rotational

matrix (B), which will align every point in the patient space (𝑄𝑖⃗⃗ ⃗⃗ ) to a corresponding point in the

imaging space (𝑃𝑖⃗⃗⃗) (Equation 1) (Eggers, Mühling, & Marmulla, 2006)

Equation 1: 𝜑(�⃗⃗�𝑖) = 𝐵 ∙ �⃗⃗�𝑖 + �⃗� = �⃗⃗�𝑖

Multiple techniques for image-to-patient registration have been proposed and iteratively refined

over the past three decades, and are described in greater detail in this section. Early CAN

techniques relied on paired-point matching, surface mapping, or a hybrid of these two techniques

for registration. The advent of intra-operative imaging suites, including isocentric fluoroscopy,

CBCT and FBCT, have allowed for automatic registration protocols. Regardless of registration

technique, in the current paradigm all techniques of spinal CAN first require a DRF to be rigidly

affixed to patient anatomy in the operative position, followed by execution of the relevant

registration process or intra-operative imaging with automatic registration. A given

transformation linking patient and imaging spaces would remain valid only while the patient

remained in the initial position; any movement of the patient or operating table following

registration would render the initial transformation obsolete, and a repeat calibration would have

to be performed. The presence of a patient- or operating table-affixed DRF therefore allows

some patient motion to be compensated for, with only movement relative to the DRF

unaccounted for by the initial registration transformation.

29

2.2.2.2.1 Paired-Point Matching

Paired-point transformation represents the earliest technique used to register imaging and patient

spaces, and was employed in the first cranial and spinal CAN systems.(Kalfas et al., 1995) Points

in the image space, a minimum of three, are matched to corresponding readily-identifiable points

in the patient space.(Eggers et al., 2006) Points in patient space may be anatomic bony or skin-

surface landmarks, adhesive skin-surface fiducials, or internal bone-affixed fiducials (Figure 2-

8). In spinal surgery, anatomic bony landmarks are most commonly used for paired-point

registration, due to the mobility and lack of adhesion of skin fiducials for posterior approaches,

in contrast to the relative immobility of the scalp for skin-adhesive fiducials in cranial

applications. Where skin-surface fiducials have been employed in spinal navigation, registration

errors have been unacceptably large, up to 2 cm at the level of the disc space.(Roessler et al.,

1997) Bone-implanted fiducials are demonstrably superior in registration accuracy to anatomic

or skin-marker fiducials, however are rarely used in spinal surgery due to their

invasiveness.(Mascott et al., 2006)

30

Figure 2-8. Paired-point image-to-patient registration. Demonstration of paired-point techniques for image-to-

patient registration in cranial neuronavigation. Points in the image space (bottom row) may be registered to

corresponding points in the patient anatomic space (top row), chosen using anatomic surface landmarks (A), skin-

adhesive fiducials (B), bone-implanted cranial fiducials (C), or manual surface mapping (D). Reprinted from Mascott

et al., Quantification of True In-Vivo (Application) Accuracy in Cranial Image-Guided Surgery: Influence of Mode of

Patient Registration. Neurosurgery 2006;59(1):ONS146-56, by permission from Oxford University Press.

The basic equation for a transformation matrix (T) linking the patient space (X) and image space

(X*) can be expressed in matrix notation (Equation 2)(Helm et al., 2015).

Equation 2: (

𝑥1 𝑦1 𝑧1

𝑥2 𝑦2 𝑧2

𝑥3 𝑦3 𝑧3

) ∗ (

𝑡11 𝑡12 𝑡13

𝑡21 𝑡22 𝑡23

𝑡31 𝑡32 𝑡33

) = (

𝑥1∗ 𝑦1

∗ 𝑧1∗

𝑥2∗ 𝑦2

∗ 𝑧2∗

𝑥3∗ 𝑦3

∗ 𝑧3∗)

The matrix (X) consists of the (x,y,z) coordinates of each of the three fiducial markers in the

patient space, while the matrix (X*) consists of the (x*, y*, z*) coordinates of the corresponding

points in the image space. The transformation matrix (T) consists of 9 parameters t which are to

(X) (T) (X*)

31

be calculated in the registration process. Using matrix multiplication, the registration protocol

must solve a set of linear equations to compute each variable of t (Equation 3)(Helm et al.,

2015).

{

𝑥1𝑡11 + 𝑦1𝑡21 + 𝑧1𝑡31 = 𝑥1∗

𝑥2𝑡11 + 𝑦2𝑡21 + 𝑧2𝑡31 = 𝑥2∗

𝑥3𝑡11 + 𝑦3𝑡21 + 𝑧3𝑡31 = 𝑥3∗

{

𝑥1𝑡12 + 𝑦1𝑡22 + 𝑧1𝑡32 = 𝑦1∗

𝑥2𝑡12 + 𝑦2𝑡22 + 𝑧2𝑡32 = 𝑦2∗

𝑥3𝑡12 + 𝑦3𝑡22 + 𝑧3𝑡32 = 𝑦3∗

{

𝑥1𝑡13 + 𝑦1𝑡23 + 𝑧1𝑡33 = 𝑧1∗

𝑥2𝑡13 + 𝑦2𝑡23 + 𝑧2𝑡33 = 𝑧2∗

𝑥3𝑡13 + 𝑦3𝑡23 + 𝑧3𝑡33 = 𝑧3∗

Once the transformation matrix (T), with each value for its component variables t, has been

computed in the registration protocol, the matrix (T) may be applied to any point in the patient

space (x, y, z) to map it to its corresponding point in the image space (x*, y*, z*), allowing real-

time image guidance. A minimum of three fiducial markers are required for this paired-point

registration, though certainly more may be used, with subsequent mathematics to use either the

best three fiducials which minimize the root-mean-square (RMS) error between the patient and

image spaces, or to use all fiducials and reduce the resulting overestimated linear equation

system to a set of three new computed coordinates.

In practice, while paired-point registration works well in principle with readily-identifiable

corresponding landmarks in both the patient and image spaces, it is time consuming intra-

operatively and often tedious if the desired anatomic landmarks cannot be localized precisely,

Equation 3:

32

and prone to error whenever fiducials other than radiolucent implanted markers are used,

impractical in most common procedures.(Mascott et al., 2006)

2.2.2.2.2 Surface Contour Matching

An alternative method of manual registration, attempting to overcome some of the drawbacks of

paired-point methodologies, is surface-based registration, described first by Pelizzari et al. in

1987.(Pelizzari & Chen, 1987) Fundamentally, surface registration techniques attempt to align

two surfaces, assumed to be rigid bodies, by iteratively applying scaling, translational and

rotational transformations until a particular distance or other error metric is minimized or

otherwise optimized.(Eggers et al., 2006; Helm et al., 2015) Scaling transformations are

represented by simple multiplication of an original surface, described by (X), by a scaling factor

(m), to generate the appropriately scaled surface (X*)(Equation 4).

Equation 4: 𝑋∗ = 𝑚(𝑋)

Translation in 3D space is defined by a vector transformation of every point in the original

surface, with coordinates (x, y, z), by a vector V consisting of three coordinates defining the

magnitude and direction of the translation (a, b, c), resulting in a surface at a novel position, with

coordinates for each point represented by (x*, y*, z*)(Equation 5).

Equation 5: (𝑥, 𝑦, 𝑧) + �⃗⃗�(𝑎, 𝑏, 𝑐) = (𝑥∗, 𝑦∗, 𝑧∗)

33

Rotational transformations may be described either by 3 (3x3) Euler matrices using Euler angles

corresponding to each of the 3 axes in Cartesian space, or by a single (3x3) matrix of unit

quaternions.(Helm et al., 2015; Stéphane Lavallee, 1996)

Surfaces in the imaging space are typically isolated from cross-sectional imaging by contouring

and thresholding algorithms, which are beyond the scope of this discussion. Surfaces in the

patient space may be acquired by a number of differing techniques. The earliest methods

acquired surfaces point by point using a tracked tool, a highly time-intensive and laborious

exercise (Figure 2-9).(Eggers et al., 2006) Laser contouring devices may also be used to scan

surfaces and are often used in commercial applications, however in their current form are

typically less accurate than marker-based point-matching techniques.(Schlaier, Warnat, &

Brawanski, 2002) Automated optical surface scanning via deformation of a projected structured-

light pattern may also be applied towards acquiring depth information in the patient

space.(Hoppe, Däuber, Kübler, Raczkowsky, & Wörn, 2002) This technique is the basis for

optical topographic imaging (OTI), described in greater detail in Section 2.2.2.2.5.

34

Figure 2-9. Manual surface mapping. Navigation display screenshot of a typical registration procedure using a

tracked pointer (blue) to manually select individual points (green dots) over the posterior osseous elements of a

lumbar vertebra, to generate a rudimentary surface map for subsequent image-to-patient registration. Reprinted from

Costa et al., Computed Tomography-Based Image-Guided System in Spinal Surgery: State of the Art Through 10

Years of Experience. Neurosurgery 2015;11(Suppl 2):59-67, by permission of Oxford University Press.

Registration of the surfaces in the imaging and patient spaces requires minimization of some

distance or error function between the two, with iterative applications of translational, scaling

and rotational transformations. Common error metrics include the Euclidean surface distance,

voxel congruence, information entropy, and mutual information functions.(S Lavallee, Szelisky,

& Brunie, 1996; van Herk & Kooy, 1994; Zeilenhofer et al., 1997) The most widespread and

best-established mathematical realizations in surgical navigation systems are variants of the

Iterative Closest-Point (ICP) algorithm, first described by Besi and Mackay and separately by

Chen and Medioni in the early 1990s (Figure 2-10).(Besi & Mckay, 1992; Y. Chen & Medioni,

1991) The original ICP algorithm minimized the point-to-point RMS distance error between the

two surfaces; subsequent variants have minimized point-to-plane RMS error or a combination of

the two. Any iterative technique is prone to failure by falling into local minima, that is, settling

on a solution for aligning the two surfaces that is relatively appropriate by some parameters but

is not the best possible match.(Helm et al., 2015) ICP algorithms in particular are also prone to

35

numerous other pitfalls, including failed registration from poor initial pose estimation due to

error in localizing fiducials or due to soft-tissue deformation,(Clements, Chapman, & Dawant,

2008; Maurer, Aboutanos, Dawant, Maciunas, & Fitzpatrick, 1996; Xin & Pu, 2010)

susceptibility to mismatched outliers,(Pomerleau, Colas, Siegwart, & Magnenat, 2013) and

inability to account for scaling differences between the initial point set alignments.(Ying, Peng,

Du, & Qiao, 2009) This has resulted in hundreds of variants of the original ICP algorithm

published in the past two decades.(Pomerleau et al., 2013) An outstanding pitfall of ICP variants

that remains unsolved is that of geometric congruence, that is, failure of the algorithm to

converge on a solution, or falling into local minima, in the presence of significant geometric

symmetry (congruence) in any number of configurations.(Armesto, Minguez, & Montesano,

2010; Gelfand, Ikemoto, Rusinkiewicz, & Levoy, 2003; Pottmann & Hofer, 2003) While

multiple proposed solutions to this error mechanism have been proposed, none have the required

accuracy or temporal efficiency to be feasible for intra-operative navigation.(Armesto et al.,

2010; Berner, Bokeloh, Wand, Schilling, & Seidel, 2008) Navigation error from ICP non-

convergence in geometrically-congruent surfaces therefore remains an ongoing potential pitfall.

36

Figure 2-10. Iterative Closest-Point registration. Simplified illustration of the iterative closest-point (ICP) method of

reconciling and aligning two surfaces or lines (blue and red). A set of points is chosen along each surface/line, and

the target surface/line (blue) iteratively scaled, translated and rotated until a cumulative distance error metric between

the point sets (black lines) is minimized. Reprinted from Smistad et al., Medical Image Segmentation on GPUs – A

Comprehensive Review. Med Image Anal 2015;20(1):1-18, under the Open Access Creative Commons Attribution

License 3.0.

2.2.2.2.3 Hybrid Matching

The accuracy of surface scanning techniques is known to diminish with distance from the

mapped surface.(Maurer, Jr. et al., 1995) A hybrid registration technique incorporating surface

mapping for initial registration, followed by refinement with paired-point matching using deeper

anatomic fiducials, has been proposed by Maurer et al.. Deep point-matching has been described

using a standard optically-tracked pointer, or with one-dimensional ultrasonic sensors applied

percutaneously to reduce invasiveness.(Schmerber et al., 1997) As navigation accuracy at

significant depth is most relevant in cranial procedures, hybrid tehcniques have not been widely

explored in the spine, and will not be discussed in any greater detail in this chapter.

37

2.2.2.2.4 Automatic Registration

The development of sophisticated intra-operative imaging devices, coupled with the tedious and

time-consuming nature of manual registration procedures, has driven significant improvements

in automatic image transfer and registration techniques. In the context of spinal surgery, the first

automatic algorithms were developed to allow registration of patient anatomy to a pre-operative

CT using multiple intra-operative C-arm XR images (typically one lateral and multiple oblique

views).(Nolte et al., 2000) Fundamentally, all automatic registration algorithms employ a known

relationship between the intra-operative imaging device and the output images, based on

manufacturer/laboratory calibration of the imaging device, to allow subsequent automatic

registration of the imaged patient anatomy to the imaging space dataset. Nolte et al. applied

multiple 2D XR images for automatic registration to a 3D pre-operatively-acquired CT dataset.

In their system, 2D to 3D transformation is accomplished by laboratory calibration of the C-arm

using a calibration plate with a grid of metallic markers. Using a linear cone beam projection

model, the virtual projection of a tracked instrument in the image-intensifier (II) coordinate

system can be transformed into the C-arm image coordinate system (Figure 2-11). This

calibration is accurate for a given C-arm in a specific position; as the C-arm position changes

intra-operatively however, deformation of the C-arm frame itself may lead to errors as a result of

millimetric movement of the XR source relative to the II detector. Therefore, the C-arm itself

also requires a calibration set of LEDs to be mounted to it to control the deformation in real-time

via a spatial calibration correction function.(Nolte et al., 2000)

Similar techniques of automatic registration are applied to contemporary intra-operative imaging

devices, including CBCT and FBCT systems. With FBCT systems the operating table is fixed to

the CT toroid, allowing for factory calibration of the relationship between the CT imaging plane

and the operating table, with real-time adjustments made if the gantry angle is altered for the

intra-operative scan. This relationship is updated intra-operatively using a patient-affixed DRF

and an LED tracking array mounted to the scanner itself. Automatic registration techniques with

modern intra-operative imaging systems have been demonstrated to be more time-efficient than

existing paradigms of registration to pre-operative imaging,(T. Kotani et al., 2014) however still

38

requiring on average 8-9 minutes for a complete registration cycle, from initial clamping of the

DRF to being ready to navigate.(Eric W. Nottmeier & Crosby, 2009) Moreover, these

registrations remain accurate only for the patient and DRF position at the time of intra-operative

imaging; any subsequent manipulation or deformation of either patient anatomy or the DRF

necessitates a repeat scan, with its associated time and patient radiation cost, as no rapid and

radiation-free technique of updating a registration exists to date.

Figure 2-11. Components and coordinate systems of automatic registration techniques. Illustration of a

representative setup (left) for automatic registration to intra-operatively-acquired fluoroscopic images. Local

coordinate systems (COSs) for each of the image source (here, image-intensifier (II)), surgical object/dynamic

reference frame (SO), tool (T) and C-arm image (CI) are shown. Automatic registration necessitates

laboratory/factory calibration to model the virtual projection of an imaged instrument from the II coordinate system to

the CI coordinate system (right). Reprinted from Nolte et al., A New Approach to Computer-Aided Spine Surgery:

Fluoroscopy-Based Surgical Navigation. Eur Spine J 2000;9(Suppl 1):S078-88, by permission of Springer Nature.

2.2.2.2.5 Optical Topographic Imaging

Despite workflow improvements afforded by automatic registration to intra-operatively-acquired

imaging, the radiation exposure to patients and OR personnel with intra-operative imaging

devices remains of concern. Conversely, while this radiation exposure is minimized by

registration to pre-operative CT or MRI with conventional techniques, these remain so

39

cumbersome and time-consuming as to deter the widespread adoption of navigation for spinal

procedures.(E W Nottmeier, 2012) Intra-operative stereoscopic optical imaging, or computer

stereovision, is one technique that has been explored to allow radiation-free registration of a pre-

operative imaging dataset to an optically-acquired surface map of intra-operative anatomy. In

neurosurgical navigation applications, stereovision was first explored for the automated

resolution of brain shift for updating of registrations for intra-cranial navigation.(DeLorenzo et

al., 2010; Paul, Morandi, & Jannin, 2009; Sun et al., 2005) Applications for open posterior spinal

surgery were investigated more recently in the mid-2010s.(Ji et al., 2015)

In isolation, passive stereovision with visible-wavelength cameras is able to generate 3D spatial

information, i.e. depth, from two 2D images. This may be accomplished through one of two

mechanisms, distinctly or in combination, based on the modes of information provided by

reconstructed stereoscopic images: texture intensity and 3D geometry. 3D spatial reconstruction

from two 2D images taken contemporaneously by two cameras spaced a known distance apart, is

accomplished by first eliminating lens distortion from each 2D image based on known attributes

of each optical setup, to allow simplification to an ideal pinhole projection. Subsequently, the

two 2D images are rectified into a common image plane, and the positions of a given element in

each image compared and triangulated to compute the position of the element in 3D space

relative to the camera positions (Figure 2-12).(Sun et al., 2005) Passive stereovision requires

imaged points to be distinctly visible in both 2D images. It therefore encounters significant

difficulty when imaging highly reflective or smooth surfaces, or in poor ambient lighting

conditions.(C. Chen & Zheng, 1995)

40

Figure 2-12. Passive stereovision and correspondence. Illustration depicting the ‘correspondence problem’ in

passive stereovision, modelled using ideal pinhole cameras (Cl and Cr). The image of a single point (X, Y, Z) is

projected onto the image planes of both cameras, (xl, yl) and (xr, yr), respectively. Triangulation of the location of (X,

Y, Z) in 3D space requires rectification of both image planes (solid lines, left) onto a plane (solid lines, right)

coincident with the epipolar lines (dotted lines, right) from both image projections. Reprinted from Sun et al.,

Stereopsis-Guided Brain Shift Compensation. IEEE Transactions on Medical Imaging 2005;24(8):1039-52, by

permission of © 2005 IEEE.

Active 3D imaging, or ranging technqiues, involve the addition of a surface-scanning projection

in addition to optical cameras, to identify the 3D geometry of a scanned surface.(Geng, 2011)

The fundamental principles of triangulation for spatial reconstruction apply equally to both

active and passive stereovision, however the features extracted from the images acquired by each

camera are created or enhanced by the addition of an active surface-scanning projection, to

minimize the difficulties faced by passive systems with textureless surfaces.(C. Chen & Zheng,

1995) Common active projection techniques include time-of-flight techniques, laser range

scanning, which may be coupled to stereo or monocular vision systems, as well as structured-

light scanning.(Mirota, Ishii, & Hager, 2011) Structured light involves the illumination of a

surface with a 2D spatially-varying pattern of known periodicity and intensity, made possible by

the development of digital micromirror devices. Monocular or stereovision imaging is then

applied to acquire a 2D image of the scene under structured light illumination. The deformation

of the projected structured-light pattern by a surface with varying 3D geometry, is then employed

to extract depth information from the surface, allowing the generation of a 3D surface map of the

scanned scene (Figure 2-13).(Geng, 2011) Techniques for structured-light spatial and temporal

variation have been studied extensively. A detailed examination of projection and coding

41

techniques is beyond the scope of this thesis. Broadly, structured-light projection patterns may be

classified as single-shot or sequential (multi-shot), requiring spatial or temporal multiplexing,

respectively. While sequential projection patterns may provide more accurate surface mapping

due to a greater number of reconstructed points, they are often unsuitable for moving targets as a

snapshot of the surface at a single point in time cannot be acquired.(Salvi, Fernandez, Pribanic,

& Llado, 2010) A simplified summary and classification of structured-light illumination patterns

is shown in Figure 2-14. Numerous studies of projection patterns and coding have been

published over the past two decades, aiming to strike a balance between resolution (point cloud

density of the reconstructed surface), noise, spatial accuracy, ability to code for coloured

surfaces, handling of dynamic surfaces, and computational time. Sequential projection patterns

have been demonstrated to have greater resolution and therefore spatial accuracy,(Pribanić,

Džapo, & Salvi, 2009; Salvi et al., 2010) whereas binary single-shot spatial multiplexing patterns

have the poorest spatial resolution.(Carrihill & Hummel, 1985; Salvi et al., 2010) Conversely,

the computational power and time required for sequential temporal multiplexing patterns is

significantly greater, due to the need to compute vastly greater numbers of 3D points. Color

calibration requires additional computational time.(Salvi et al., 2010) The use of stereovision

rather than a single camera partly minimizes the computational load for decoding depth

information from the structured light illumination pattern.(Mirota et al., 2011) The optimal

technique for medical/surgical applications therefore depends on the type of tissue being imaged,

i.e. whether it is relatively static or dynamic, as well as the accessibility of the tissue to imaging

devices, i.e. whether endoscopic or open approaches are required.(Schmalz, Forster, Schick, &

Angelopoulou, 2012) With advances in modern digital light projection technology as well as

increases in computing power afforded by graphics processing units (GPUs), sequential

projection techniques have been applied even to dynamic tissues including the myocardium and

bioprosthetic cardiac valves, at accuracies on the order of <100 µm.(Laughner, Zhang, Li, Shao,

& Efimov, 2012)

42

Figure 2-13. Structured light 3D scanning. Illustration of structured light illumination-based 3D surface mapping. A

monocular setup is depicted, with a structured light projection of a light pattern of known pattern and periodicity onto

the 3D object in the scene. The deformation of the reflected light pattern is seen by the camera, allowing computation

of a surface map based on known parameters of the optical setup, including the distance between camera and

projector (B) and incident angles (α, θ). Reprinted from Geng et al., Structured-Light 3D Surface Imaging: A Tutorial.

Adv Opt Photonics 2011;3(2):128-60, under Open Access by permission of the © 2011 Optical Society of America.

Figure 2-14. Structured light illumination patterns. A summary classification of structured light illumination

patterns described to date. Reprinted from Geng et al., Structured-Light 3D Surface Imaging: A Tutorial. Adv Opt

Photonics 2011;3(2):128-60, under Open Access by permission of the © 2011 Optical Society of America.

43

Computer vision-based image-guidance techniques have been developed in medicine for

numerous applications, most prominently facial prosthetics, joint arthroplasty, as well as

endoscopic and laparoscopic applications for rhinoscopy, bronchoscopy and intra-abdominal

surgery.(Helferty & Higgins, 2001; Keller & Ackerman, 2000; Mirota et al., 2011) Applications

of stereovision to surgical navigation have been explored more recently, predominantly with

passive stereovision in the context of cranial neurosurgery for the compensation of intra-

operative brain shift following craniotomy.(DeLorenzo et al., 2010; Paul et al., 2009; Sun et al.,

2005) Further advances have led to automated segmentation and registration protocols to update

registrations to pre-operative MRI.(X. Fan, Ji, Hartov, Roberts, & Paulsen, 2012, 2014; Ji, Fan,

Roberts, Hartov, & Paulsen, 2014) Stereovision for spinal procedures has been explored in only

one study to date, with a passive technique described by Ji et al., employing readily-available

stereoscopically-aligned grayscale cameras mounted rigidly to a surgical microscope.(Ji et al.,

2015) In clinical in-vivo testing in 8 patients, Ji et al. demonstrated a reconstruction accuracy of

stereovision-acquired surface maps of 2.21 mm, far greater than the <100 µm accuracies

described with active surface-scanning techniques.(Laughner et al., 2012) Registration accuracy

to a standard optically-tracked navigation system was 1.43 mm, within the described tolerances

for modern neuronavigation systems, typically considered to be <2 mm. The mean computational

time for stereovision-based reconstruction and registration was 95.8 seconds. While passive

stereovision represents a novel, contactless and radiation-free mechanism for intra-operative

image-to-patient registration of spinal anatomy, its clinical accuracy remains unproven, and

workflow improvements perhaps not sufficiently significant to promote widespread adoption

among spinal surgeons.

Our laboratory has developed a technique for active structured light illumination-based surface

mapping, coupled with thresholding algorithms to appropriately isolate scanned bony anatomy.

This technique may therefore be applied to registration of optically-imaged anatomy to a CT

dataset. We have termed this combination of structured light-based surface scanning and

automated segmentation and thresholding, optical topographic imaging (OTI). For the remainder

of this thesis, the term OTI will be used to refer to active computer stereovision for image-to-

patient registration.

44

2.2.3 Instrument Tracking and Actuation

Regardless of the method by which image-to-patient registration is achieved, all contemporary

intra-operative navigation suites aim to subsequently track instruments in the merged coordinate

system, to facilitate a given surgical maneuver. Instrument tracking may be accomplished

through a number of techniques; while a detailed discussion of tracking methodologies is beyond

the scope of this thesis, the topic merits some discussion as tool tracking itself is associated with

a quantifiable error, termed ‘jitter’, which contributes to the final application accuracy of a

navigation system.(Khadem et al., 2000) Furthermore, the process of intra-operative instrument

calibration and tracking contributes significantly to the overall usability and workflow of any

CAN system; inadvertent bumping of the DRF required for optical instrument tracking, for

instance, is a commonly cited pain point in current CAN workflows.(Choo et al., 2008)

Instruments in the earliest frameless CAN systems were tracked by mechanical articulated arms,

in which multi-directional position sensors at each articulation enabled computation of the

location and orientation of the instrument at the end effector of the arm, in all 6 possible degrees

of freedom (x, y, z for position, and roll, pitch, yaw for orientation).(Alshail et al., 1998; Carney,

Patel, Baldwin, Coakham, & Sandeman, 1996; Guthrie & Adler, 1992) While accurate, these

systems were bulky to position and operate, difficult to modify to track multiple instruments, and

were unable to track the motion of anything not directly attached to and calibrated for the arm.

The initial era of tracking systems devoid of mechanical linkages began with microscopes and

tools equipped with ultrasonic spark-gap emitters, whose pose and position were computed based

on an array of acoustic recorders situated around the operative field (Figures 2-2 and 2-4).(Kalfas

et al., 1995; H. F. Reinhardt, Horstmann, & Gratzl, 1993) Due to relative inaccuracy and

sensitivity to ambient noise from other operating room equipment as well as to air temperature,

acoustic tracking was largely abandoned in favour of optical or electromagnetic tracking, the two

primary paradigms in current use.(Tatar et al., 2002)

45

Contemporary optical tracking systems (OTS) rely on an infra-red camera typically mounted on

a mobile platform in the operating room. In a passive OTS, an IR emitter is also located on the

camera unit, and the reflection of the IR light from reflective markers on the tracked instruments

and DRF are used to triangulate the pose and position of each tool. In an active OTS, IR LEDs

are situated on both the DRF and each tracked instrument, with the IR light detected by the

mobile camera unit and used to calculate the pose and position of each tool (Figure 2-15).(N. D.

Glossop, 2009) Both active and passive OTS devices are currently available commercially. There

has been some suggestion, particularly among early-generation technologies, that active OTS

provides lower tracking error and greater consistency than passive systems; Khadem et al.

compared both active and passive OTS units from the same manufacturer, and found RMS jitter

of 0.058 ± 0.037 mm with active OTS and 0.115 ± 0.075 mm with passive.(Khadem et al., 2000)

These differences have been mitigated in modern devices largely through more powerful IR

emitters and improved filtering techniques for the IR cameras; current optical systems are able to

track individual markers with an accuracy of approximately 0.25 mm, and instrument tips at

accuracies of 1-2 mm.(Wiles, Thompson, & Frantz, 2004) However, OTS tool tracking relies on

a pre-calibrated relationship between the IR tracker array mounted on an instrument (either

passive reflective markers, or active LEDs) and the instrument tip, hence is unable to track

needles and other non-rigid tools. More importantly, and of greater relevance in day-to-day use,

accurate and real-time optical tracking necessitates constant line-of-sight between the IR camera

± emitter platform, and the DRF as well as the tracking array on each monitored instrument.

Clutter in the surgical field, and even the physical position of OR personnel and the hand

position of the tool operator, can greatly influence optical instrument tracking. Moreover, the

relative positioning of the DRF, tracker arrays on each tool, and the camera unit can influence

the tracking error of the system; Khadem et al. demonstrated that the majority of OTS tracking

error arises in the z-axis, that is, pointing directly away from the IR camera. Finally, the number

of tracker markers (reflective spheres or active LEDs) visible to the IR camera, as well as the

distance from the tool tip to the centroid of its tracker array, also influence tracking error; Wiles

et al. demonstrated a doubling of RMS tracking error with an increase in the tool tip distance of

100 mm.(Wiles et al., 2004) It is also known that optical instrument tracking accuracy in-vivo

degrades over time as well as with increasing distance from the DRF.(Quiñones-Hinojosa,

Robert Kolen, Jun, Rosenberg, & Weinstein, 2006) Operator-dependent mechanisms to reduce

optical tracking error therefore include situating the IR cameras as close to the surgical field as

46

possible, and moreover aligning the camera z-axis with the direction in which clinical accuracy is

least important, as well as ensuring that as many markers as possible are visible to the camera

when affixing the DRF and tracking a given surgical instrument.

Figure 2-15. Optical instrument tracking systems. Photographs of passive (left) and active (right) optical tracking

systems. In passive OTS, reflective spheres on both the reference frame (B) and instrument (C) are used for tool

tracking. In active OTS, LEDs on the reference frame and tracker are used for the same purpose, with a minimum of

three visible to the camera system (extreme right, yellow lines) for accurate tracking. Left image reprinted from Kral et

al., Comparison of Optical and Electromagnetic Tracking for Navigated Lateral Skull Base Surgery. Int J Med

Robotics Comp Assist Surg 2013;9(2):247-52, by permission of John Wiley and Sons. Right images reprinted from

Glossop et al., Advantages of Optical Compared with Electromagnetic Tracking. JBJS 2009;91(Suppl 1):23-8, by

permission of Wolters Kluwer Health, Inc.

Electromagnetic (EM) tracking systems were developed earlier than OTS technologies, however

have come into widespread clinical use only recently due in part to their lack of reliance on line-

of-sight as with optical systems. EM systems are composed of an electromagnetic field generator

or emitter, typically a bulky device positioned near the operative field in non-sterile fashion, a

DRF and a tracked instrument. Both the DRF and instrument contain sensor coils in which a

voltage is produced within the electromagnetic field generated by the emitter; the magnitude and

direction of the voltage generated in these cylindrical coils are used to compute the relative

position and two-dimensional orientation of the instrument (pitch, yaw). Computation of the

third dimension of orientation (roll, around the sensor coil’s longitudinal axis) requires a

combination of coils oriented orthogonally.(Milne, Chess, Johnson, & King, 1996) The primary

advantage of EM tracking is that the coil sensors are small, on the order of <1 mm in diameter

and <10 mm in length (Figure 2-16). They may therefore be embedded directly into instrument

47

tips, including rigid and flexible needles, catheters and endoscopes; as the instrument tip is

tracked directly given that no line-of-sight to an external detector is required, this enables the

tracking of flexible instruments within tissue cavities where line-of-sight is unobtainable.(N. D.

Glossop, 2009) However, EM tracking, particularly in older generation units, has been hindered

by significant interference from surrounding ferromagnetic materials, including most standard

surgical instruments and operating tables. EM tracking, as with OTS, relies on the presence of a

fixed DRF for relative tracking of instruments, and therefore is prone to the same disruptions as

OTS from inadvertent manipulation or displacement of the DRF. Accuracy degradation over

time and distance from the DRF, as has been demonstrated with OTS, has not however been

shown comparably in EM systems. Nonetheless, the accuracy of EM tracking has typically been

and continues to be slightly worse than that achieved by OTS, with tracking accuracy of an

individual sensor of approximately 0.9 mm and tool-tip accuracy of 1.5-2.5 mm, as reported in

the datasheets of an EM tracking device by a manufacturer also producing OTS systems.(NDI,

2018a) In a clinical comparative study of EM vs OTS tracking in skull-base neurosurgical

applications, Kral et al. found tracking accuracy of 0.99 mm with EM tracking, vs. 0.22 mm with

OTS.(Kral, Puschban, Riechelmann, & Freysinger, 2013) However, in applications where

needles or other fine-diameter catheters are tracked, part of the accuracy deficit of EM trackers is

mitigated by their ability to track the tool tip directly, as opposed to requiring an optical tracking

array at some distance from the tool tip, which itself introduces errors.

48

Figure 2-16. Electromagnetic instrument tracking. Photographs of a representative setup (left) with an

electromagnetic field generator (A) and reference coil (C) affixed rigidly into a skull. Representative photograph of a

tracked biopsy needle stylet (right), with sensor coil (green, circled in yellow) embedded at the tip of the instrument.

Left image reprinted from Kral et al., Comparison of Optical and Electromagnetic Tracking for Navigated Lateral Skull

Base Surgery. Int J Med Robotics Comp Assist Surg 2013;9(2):247-52, by permission of John Wiley and Sons. Right

image reprinted from Glossop et al., Advantages of Optical Compared with Electromagnetic Tracking. JBJS

2009;91(Suppl 1):23-8, by permission of Wolters Kluwer Health, Inc.

In all current navigation techniques, with instruments tracked either optoelectronically or

electromagnetically, the surgical instruments are typically actuated or manipulated freehand by

the surgeon, with continuous visual feedback from a display placing the tracked instrument in the

shared coordinate space with the registered imaging dataset. Initial CAN suites tracked only a

pointer or similar probe-type instrument; in the context of spinal surgery, the surgeon would then

be required to manually follow the trajectory planned with the navigated pointer using untracked

instruments for pedicle cannulation, tract preparation and screw placement. As a result, some

error of the final screw placement could result from slight deviations in manually-actuated

trajectory or entry point from that planned with the tracked pointer non-contemporaneously.

Subsequent generations of CAN devices have spawned instrument sets to match evolving

capabilities of modern trackers and intra-operative imaging devices, with dedicated tracked

instruments for pedicle cannulation, tract tapping as well as screw placement. This may

potentially minimize the final application error of navigated screw placement, however still

relies on freehand actuation of the navigated instrument by the operator, with continuous visual

feedback from a navigation display. The latest advance in instrument actuation, in an effort to

minimize the component of application error resulting from freehand manipulation of a tracked

49

instrument, is robotic guidance. Multiple systems have been developed and are commercially

available for spinal applications, all of which rely on optical IR tracking similar to freehand OTS

techniques. A mobile platform with a robotically-actuated arm is situated adjacent to the patient

and, using an affixed optical tracking array, is registered into the same coordinate system as the

patient anatomy and imaging dataset. The robotic end-effector, which may include a guide for

instruments for pedicle drilling/cannulation, tract preparation and screw placement, is

subsequently moved into the appropriate trajectory automatically with navigation guidance

(Figure 2-17).(Overley et al., 2017) 25 series studying the accuracy of spinal instrumentation

placement with various robotic guidance systems have been published to date, demonstrating

radiographic accuracy rates of 85.0-100%.(Joseph, Smith, Liu, & Park, 2017) Comparative

studies have demonstrated significant improvement in radiographic accuracy, as well as decrease

in intra-operative patient and surgeon radiation exposure, with robotic guidance vs. freehand or

fluoroscopically-guided instrumentation placement. However, only one study to date has

compared robotically-actuated to freehand-actuated navigated pedicle screw placement. In a

three-armed prospective trial of freehand vs. freehand-navigated vs. robotic-navigated

thoracolumbar pedicle screw placement, Roser et al. demonstrated no significant difference in

screw placement accuracy with standard navigation or robotic guidance, but with significantly

greater pre-operative preparation time and greater intra-operative radiation dose with robotic vs.

standard navigation (Table 2-3).(Roser, Tatagiba, & Maier, 2013) While robotic guidance may

represent the next step in evolution of intra-operative CAN systems, particularly as integration

with intra-operative imaging devices improves, in their current form there does not appear to be

any discernible benefit relative to freehand-actuated navigation techniques.

50

Figure 2-17. Robotic instrumentation guidance. Illustration of the Rosa© robotic guidance system (Medtech

S.A./Zimmer Biomet) with end-effector sleeve for pedicle cannulation. Reprinted from Overley et al., Navigation and

Robotics in Spinal Surgery: Where Are We Now? Neurosurgery 2017;80(3S):S86-99, by permission of Oxford

University Press.

Table 2-3. Intraoperative outcomes with robotic guidance. Operative times and intra-operative radiation time and

exposure are compared among freehand, freehand-navigation and robotic-navigation techniques for thoracolumbar

pedicle screw insertion. Reprinted from Roser et al., Spinal Robotics: Current Applications and Future Perspectives.

Neurosurgery 2013;72(Suppl 1):12-18, by permission of Oxford University Press.

2.3 Evaluation of Navigation Accuracy

Classically, Fitzpatrick et al. have classified errors in frameless stereotactic navigation into three

categories, the fiducial localization error (FLE), fiducial registration error (FRE), and target

registration error (TRE)(Figure 2-18).(Fitzpatrick, West, & Maurer, 1998) FLE represents the

error in localizing the points marked for registration in the patient space, which depends on the

51

error in the OTS or EM tracking system, the number and arrangement of fiducials, and the spatial

resolution and geometric accuracy of the imaging dataset, which is known to be greater for CT

than MRI.(M. N. Wang & Song, 2011) As discussed in Section 2.2.3 for instance, the tracking

error of EM systems is known to be greater than for OTS. FRE is the error in matching

corresponding fiducials in the patient and imaging spaces, represented by a distance metric

which is typically reported by commercial CAN systems as the ‘registration error’, somewhat

misleadingly. The true navigation error at a given target, however, is represented by the TRE, the

most clinically-relevant metric for end-users and also the most variable, as it differs for unique

targets. For paired-point registration technqiues, it is well documented that TRE increases with

distance from the centroid of the selected fiducial markers, as well as in areas of lower fiducial

concentration.(Fitzpatrick et al., 1998) While registration accuracy, or more appropriately FRE,

has been described for various surface-scanning techniques as well as differing algorithms for

registering two surfaces (see Section 2.2.2.2.2), the characteristics of TRE in surface-mapping

techniques in an in-vivo setting have not yet been elucidated. As unique errors arise in differing

techniques of patient and image space acquisition and registration, quantification of these errors

remains critical in the development of novel registration and neuronavigation techniques.

52

Figure 2-18. Classification of errors in frameless stereotactic navigation. Illustration depicting the classically-

described registration errors in frameless stereotaxis. (A) arrows indicate fiducial localization error (FLE). (B) arrows

indicate fiducial registration error (FRE). (C) arrows indicate target registration error (TRE) for the target, represented

by a square. Reprinted from Eggers et al., Image-to-Patient Registration Techniques in Head Surgery. Int J Oral

Maxillofac Surg 2006;35(12):12-5, by permission of Elsevier.

While TRE is the most clinically-relevant of the classic registration errors described by

Fitzpatrick et al., the concept of application accuracy is perhaps even more relevant to end-users,

as it accounts not only for errors inherent in the registration process and instrument tracking (as

with TRE), but also in how the tracked tools are applied to perform a given surgical maneuver.

In spinal procedures, in earlier generations of CAN a tracked pointer was used to visualize the

approximate ideal trajectory and entry point, which subsequently were followed manually with

untracked drills, pedicle finders and screwdrivers. Later generations of spinal CAN instruments

53

added tracked drills/awls, pedicle finders and screwdrivers. Nonetheless, the ultimate aim of

CAN guidance in this context is to place instrumentation accurately, hence the application

accuracy reflects how closely the final screw position approximates its intended navigated

position. As spine instrumentation is readily visualized on post-implantation XR or CT imaging,

final screw positions may therefore be compared readily to planned trajectories on intra-

operative navigation, as a measure of application accuracy. The radiographic grading systems

used in the literature to quantify pedicle screw accuracy, as a measure of application accuracy,

are highly heterogeneous. Some measures quantify only the magnitude of breach of the pedicle

wall, others assess the position of the screw tip alone without regard for the shaft position, and

only some identify the important of directionality of breach rather than magnitude alone.(Aoude

et al., 2015; Gertzbein & Robbins, 1990; Güven, Yalçin, Karahan, & Sevinç, 1994; Heary, Bono,

& Black, 2004; O’Brien et al., 2000) Quantification of absolute navigation application accuracy

in a standardized, reproducible fashion, is essential for the valid comparison of novel

neuronavigation techniques, and remains a knowledge gap in the current literature.

2.4 Thesis Aims and Hypotheses

Taken together, the body of literature suggests that surgical spinal pathologies, from

predominantly degenerative and neoplastic etiologies, constitute a significant and growing health

care burden, and that computer-assisted navigation techniques have an important and burgeoning

role in a variety of procedures to treat these ailments. While there is substantial evidence

supporting increased radiographic accuracy with spinal CAN, significant heterogeneity within

this literature results in unclear practical benefits from the perspective of the end-user,

particularly given that data on clinically-relevant outcomes in navigated spinal procedures is only

recently beginning to emerge. Inconsistencies in literature reporting standards render the results

of comparative studies on various CAN techniques onerous to interpret. As data on complication

avoidance with CAN emerges, however, cost-utility arguments for spinal CAN can subsequently

be investigated appropriately. A multitude of 3D CAN techniques exist currently, each with their

relative merits and drawbacks. Universal to essentially all current CAN methodologies is a

disruption in spatial and temporal workflow, significant upfront and ongoing capital costs, and a

54

considerable mental cost in the form of steep learning curves. These current hindrances to

adoption lead us to the overarching question of whether the capital and intellectual investment in

CAN is worthwhile. As with any medical technology, there may exist certain demographics

which are more suited to, or better able to maximize the benefits of, spinal navigation techniques.

However, these current usage patterns of spinal CAN remain undefined. Moreover, concerns

over workflow disruption and temporal efficiency remain unaddressed, and potential pitfalls and

application errors associated with CAN, which constitute part of their learning curve, remain

poorly transparent to the end-user. Each of these issues may contribute to a lack of adoption of

these technologies in key demographics. With optical topographic imaging (OTI), the potential

exists for disruption of the current paradigm of spinal navigation, with significant improvements

in spatial and temporal workflow that may alleviate some of the roadblocks to adoption. These

improvements must be demonstrated in clinical practice, however, and the remaining hindrances

affecting all spinal CAN techniques concomitantly addressed.

With this in mind, this thesis aims to test the following hypotheses:

1) Neuronavigation for spinal procedures, using optical topographic imaging (OTI), is at

least comparably accurate to and faster than existing neuronavigation techniques, across

all spinal regions, and in all high-yield applications for current navigation paradigms

2) Registration and navigation errors associated with OTI may be different in character and

magnitude from those associated with typical paired-point or automatic registration

These hypotheses will be addressed in the following 5 research objectives:

1) To evaluate current trainee and surgeon practice patterns with spinal CAN

2) To assess current methods of evaluating navigation error in spinal surgery, including

clinical, radiographic and quantitative metrics of navigation accuracy

55

3) To quantify the accuracy of OTI-CAN in the thoracolumbar spine, for both open and

minimally-invasive (MIS) approaches, and assess the impact of OTI-CAN on intra-

operative workflow

4) To quantify the accuracy of OTI-CAN in the mobile cervical spine, and assess the impact

of OTI-CAN on intra-operative workflow

5) To identify and characterize mechanisms of error in surface-based registration for intra-

operative CAN, and identify modifiable and non-modifiable anatomical and

computational predictors of increased navigation error

56

Chapter 3 Spatio-Temporal Trends in Spinal CAN Implementation

Preamble

This chapter is modified from the following:

Guha D, Moghaddamjou A, Jiwani ZH, Alotaibi NM, Fehlings MG, Mainprize TG, Yee A,

Yang VXD (2017). Utilization of spinal intra-operative three-dimensional navigation by

Canadian surgeons and trainees: a population based time trend study. Manuscript in submission.

57

3.1 Abstract

Computer-assisted navigation (CAN) is a useful adjunct to improve the accuracy of spinal

instrumentation as well as bony and soft-tissue resection. However, the widespread adoption of

CAN by spinal surgeons has been limited due partly to lack of training, high capital costs, and

workflow hindrances. The purpose of this study is to characterize the spatiotemporal use of

spinal CAN in a single-payer health care system and assess the impact of intra-operative CAN

use on trainee proficiency. We retrospectively reviewed a prospectively-maintained provincial

database of patients undergoing spinal instrumentation or percutaneous

vertebroplasty/kyphoplasty. Data was collected on treated pathology, spine region, surgical

approach, institution type, surgeon specialty, the use of 2D or 3D-CAN, and revision surgeries

within 2 years of the index procedure. Predictors of CAN usage as well as revision surgery were

identified. Trainee comfort with CAN and its impact on technical proficiency were assessed

using an electronic questionnaire distributed to all Canadian orthopedic surgical and

neurosurgical trainees across 15 nationwide training programs. 16.8% of instrumented fusions in

our provincial cohort were CAN-guided, predominantly by 3D-CAN. Navigation was employed

more frequently in academic institutions (15.9% vs. 12.3%, p<0.001) and by neurosurgeons

more than orthopedic surgeons (21.0% vs. 12.4%, p<0.001). Revision surgery was required in

6.4% of patients undergoing instrumented fusion, more frequently for trauma and deformity

cases, for cases performed at academic centers, and for cases performed without CAN guidance.

34.1% of residents reported being fully comfortable in the setup and use of spinal CAN, greater

for neurosurgical than orthopedic surgical trainees (48.1% vs. 11.8%, p=0.008). The use of CAN

for thoracic instrumentation increased the mean self-reported proficiency rank across all trainees

by 11.0% (p=0.036), with orthopedic residents also reporting an increase in mean proficiency

rank of 18.0% for atlantoaxial instrumentation (p=0.014) with CAN guidance. In current

practice, spinal CAN is employed most frequently by neurosurgeons and in academic centers.

The use of CAN is associated with a significant decrease in associated revision surgeries. Most

spine surgical trainees are not fully comfortable with the setup and use of intra-operative CAN,

but do report an increase in technical comfort with CAN guidance particularly for thoracic

instrumentation. Increased education in spinal CAN starting at the trainee level, particularly for

orthopedic surgery, may improve adoption.

58

3.2 Introduction

Spinal instrumentation is performed routinely for internal stabilization to promote osseous fusion

in traumatic, degenerative, metabolic and neoplastic spinal pathologies. With an aging

population, the North American burden particularly of degenerative and osteoporotic spinal

injuries is increasing, with tremendous societal and economic costs.(Baaj et al., 2010; Burge et

al., 2007; Cadarette & Burden, 2011; Martin et al., 2009) Instrumentation misplacement can

result, acutely, in injury to adjacent neurovascular structures and, in the longer term, to hardware

failure and non-union from poor load-bearing properties.(Acikbas et al., 2003; Xiao et al., 2017)

The placement of spinal instrumentation is traditionally performed freehand, or with guidance

from intra-operative X-rays resulting in significant radiation exposure to operating room (OR)

personnel.(Nelson, Monazzam, Kim, Seibert, & Klineberg, 2014; Villard et al., 2014) Three-

dimensional computer-assisted navigation (CAN), standard of care in cranial neurosurgery for

subsurface localization, has been shown to improve the accuracy of screw placement and reduce

surgeon radiation exposure, across all spinal levels.(T. S. Fu et al., 2008; G. Y. Lee, Massicotte,

& Rampersaud, 2007; Mason et al., 2014; Mirza et al., 2003; Nelson et al., 2014) Recent

evidence supports improved short and long-term clinical outcomes with the use of spinal CAN,

with reduced reoperation for hardware malposition-related complications as well as wound

infections.(Fichtner et al., 2017; Luther, Iorgulescu, Geannette, Gebhard, Saleh, Tsiouris, & Ha,

2015; Xiao et al., 2017) CAN usage is also cost-effective in high-volume centres.(Dea et al.,

2015; Matthew R Sanborn et al., 2012) However, CAN is used routinely by only 10-15% of

spinal surgeons.(Choo et al., 2008; Hartl et al., 2013; Schröder & Wassmann, 2006) A

worldwide survey of spinal surgeons, representing predominantly Europe, Asia and Latin

America, revealed multiple barriers to CAN adoption, principally cost, lack of training, and

unproven clinical benefit.(Hartl et al., 2013) The potential benefit of intra-operative CAN for

trainee education is also poorly represented in assessments of spinal CAN utility.(Gasco et al.,

2014; M B Gottschalk, Yoon, Park, Rhee, & Mitchell, 2015; Lorias-Espinoza et al., 2016;

Luciano et al., 2011; Podolsky et al., 2010; Rambani et al., 2014; Sundar et al., 2016)

59

Given differences in health care economics in Canada relative to the United States and Europe,

with potentially different barriers to CAN adoption, in the current study we characterize spinal

CAN utilization across a cohort of Canadian institutions. We also explore the utility of intra-

operative spinal CAN for trainee education.

60

3.3 Methods

3.3.1 Study Design

Assessment of spatiotemporal trends in spinal CAN utilization was performed by retrospective

review of a prospectively maintained provincial database of diagnostic and fee codes. The utility

of spinal CAN for trainee education was explored through an online survey, administered to a

nationwide cohort of neurosurgical and orthopedic surgical residents and clinical spine fellows.

3.3.2 Database – Patient Selection

The Ontario Health Insurance Plan (OHIP) database was searched through the Institute of

Clinical and Evaluative Sciences (ICES), at Sunnybrook Health Sciences Centre, for records

from 1 January 2005 to 31 December 2014 (REB# 380-2015). Patients meeting the following

criteria were included: ≥18 years of age; undergoing instrumented spinal fusion from either an

anterior or posterior approach at any spinal region; or undergoing percutaneous or open

vertebroplasty or kyphoplasty. Patients undergoing non-instrumented spinal fusion, or spinal

decompression without instrumentation, were excluded.

To detect a difference in the rate of revision surgery between navigated and non-navigated cases,

with literature estimates of 1% revision for navigated screws and 5-6% for non-navigated screws,

at α=0.05 and β=0.2, 376 patients were required in each group.(Dea et al., 2015). All sample size

calculations were performed using the ‘pwr’ package in R (Version 3.4.1; R Project for

Statistical Computing).

61

3.3.3 Database – Data Extraction

All data were extracted from the OHIP database by ICES analysts. Revision procedures were

identified by a combination of International Classification of Diseases, Ninth Revision, Clinical

Modification (ICD-9-CM) code, as well as date. Revision procedures were limited to within 2

years of the index procedure, to attempt to better capture revisions due to acute complications or

progressive hardware failure rather than ongoing degenerative processes.

Procedures were classified by pathology as trauma, degenerative, deformity, infection, tumor,

and vertebroplasty/kyphoplasty, based on a combination of OHIP fee and ICD-9-CM codes.

Within each pathology, procedures were subclassified by spine region

(cervical/thoracic/lumbosacral) and surgical approach (anterior/posterior), using a combination

of fee and ICD-9-CM codes. The use of two-dimensional (2D) or three-dimensional (3D) spinal

CAN for each procedure was identified using fee codes (E379/E378). A full description of

coding for data extraction is given in Supplemental – Section 3.7

For each identified procedure, the following demographic data were extracted: patient age,

gender, institution type (academic/rural), and surgeon specialty (orthopedic

surgery/neurosurgery).

3.3.4 Database – Statistical Analysis

Univariate comparison of categorical variables, including the proportion of procedures

undertaken with 2D or 3D CAN, were performed using Pearson Chi-squared or Fisher Exact

tests, depending on data distribution, with computation of Pearson’s correlation coefficients.

Continuous variables were compared using independent-samples t-tests or Mann-Whitney-U

62

tests, depending on data distribution. Predictors of CAN usage as well as revision surgical

procedures were explored using binary multiple logistic regression modelling, as well as

hierarchical mixed-effects logistic regression to account for surgeon specialty and institution

type as random effects. Significance levels for all tests were set at <0.05. All statistical analyses

were performed in SAS (Version 9.3; SAS Institute Inc., Cary, NC, USA).

3.3.5 Online Survey

The utility of spinal CAN for Canadian surgical trainees was assessed using a 22-item

anonymized online questionnaire distributed through GoogleForms (Supplemental - Section 3.8).

The survey was disseminated in September 2015 by email to 241 orthopedic surgical and

neurosurgical residents across 15 Canadian training programs, as well as 31 clinical adult and

pediatric spine fellows. A follow-up request for completion was emailed at 1 month; responses

were collected for a total of 4 months.

Responses to questions with multiple-choice ordinal options were converted to ordinal numerical

variables for analysis. All other responses were converted to nominal categorical variables.

Comparisons between categorical variables were made using Pearson Chi-squared tests or Fisher

Exact tests, depending on data distribution. Comparisons between multiple proportions were

made using partitioned Chi-squared analyses with Bonferroni correction. User comfort with

instrumentation techniques with versus without navigation guidance was assessed using

Wilcoxon signed-rank tests. Statistical analyses for the online survey were performed in SPSS

(version 21; IBM, Chicago, IL, USA).

63

3.4 Results

3.4.1 Spatio-Temporal Trends in Spinal CAN Usage

4607 cases of spinal instrumentation were identified in the OHIP database from 2005-2014,

35.8% with temporary percutaneous instrumentation (vertebroplasty/kyphoplasty) and the

remainder with permanent hardware for fusion (Figure 3-1). 45.9 % of cases were performed at

an academic institution, with 67.7% of instrumented fusions performed by orthopedic surgeons

and the remainder by neurosurgeons.

Figure 3-1. Cohort demographics. Pie chart demonstrating the demographics of the prospectively-monitored cohort

of patients undergoing spinal instrumentation, stratified by pathology.

64

Intra-operative computer-assisted navigation was employed in 14.0% of cases, and 16.8% of

instrumented fusions. Navigated cases were guided predominantly by 3D-CAN, with 27.1%

employing 2D-CAN. In this cohort, CAN was employed most frequently for trauma (41.8%,

with 32.1% 3D-CAN), followed by degenerative pathologies (19.8%, with 94.6% 3D-CAN) and

deformity corrections (2.5%, with 66.7% 3D-CAN). CAN was employed in only 0.5% of

vertebroplasties/kyphoplasties. In univariate analysis, CAN was employed more frequently in

academic institutions (15.9% vs 12.3%, p<0.001), and by neurosurgeons more than orthopedic

surgeons (21.0% vs 12.4%, p<0.001). Temporal trends in CAN usage are shown in Figure 3-2.

65

Figure 3-2. Temporal trends in spinal CAN usage. Histograms demonstrating temporal trends in spinal CAN usage

for a provincial cohort of patients undergoing spinal instrumentation, overall (A), and stratified by pathology (B),

surgeon specialty (C), 2D vs. 3D-CAN (D), and by institution type (E).

66

In hierarchical logistic regression, accounting for patient age, gender, pathology, and surgical

approach as fixed effects, and individual institutions and surgeons as random effects, surgeon

specialty and institution type were not independently associated with increased CAN usage. The

intraclass correlation coefficients for individual institutions and surgeons were 24% and 64%,

respectively. That is, the majority of variation in CAN usage is based on hospital and surgeon

individual preference.

3.4.2 Impact of CAN Usage on Revision Surgery Rates

In our cohort, revision surgery was required in 6.4% of patients undergoing instrumented fusion,

at an average (95%-CI) of 15.55 months (13.16-17.94 months) after the index procedure. In

univariate analyses, revisions were required more frequently for trauma and deformity cases, for

cases performed at academic centers and by orthopedic surgeons, and for cases performed

without CAN guidance (Table 3-1).

67

Table 3-1. Univariate analysis with revision surgery as outcome.

OR = odds ratio for repeat surgery. CI = confidence interval. (*) denotes significance at p<0.05.

Variables Repeat Surgery (%) OR (95%-CI) p-value

Yes No

CAN Use

Yes 0.023 13.6 0.48 (0.31-0.76) 0.001*

No 5.9 80.0

Surgeon

Specialty

Neurosurgery 1.5 29.3

0.55 (0.37-0.81) 0.002* Orthopedic

Surgery 5.4 56.9

Institution Type

Academic 3.4 42.3

1.37 (1.07-1.75) 0.011* Non-

Academic 3.0 51.3

Pathology

Degenerative 2.1 51.5

- - Deformity 4.4 37.1

Trauma 0.6 4.2

68

In hierarchical logistic regression, trauma and deformity pathologies remained independently

associated with increased revision. Intraclass correlation coefficients for individual institutions

and surgeons, as random effects, were 1% for both, indicating only 1% of the variation in

revision surgery rate was related to the individual institution and surgeon.

3.4.3 Survey of Surgical Trainees – Demographics

Of 272 residents and clinical spine fellows polled, complete responses were obtained from 60,

for a response rate of 22.1% (Figure 3-3). Orthopedic surgery and neurosurgery were represented

equally at 50% each. Respondents were located predominantly in Ontario (55.0%), followed by

Quebec (20.0%) and Alberta (10.0%); the remaining respondents were located in British

Columbia, Saskatchewan, Manitoba, Nova Scotia and Newfoundland.

Among surgical residents, the average time spent on a dedicated spine service was 5.50 ± 6.71

months, significantly greater for neurosurgery (7.37 ± 8.62 months) than orthopedics (3.30 ±

1.82 months)(p=0.024).

69

Figure 3-3. Survey demographics. Pie chart demonstrating the demographics of a surveyed national cohort of

neurosurgical and orthopedic surgical trainees, stratified by training level.

3.4.4 Utilization of CAN by Trainees

73.3% of trainees identified CAN as being available at their institution. Across all case types,

CAN was used >40% of the time by only 34.1% of respondents, with no differences between

surgical specialties.

In subgroup analyses looking at open fusions, minimally-invasive (MIS) fusions, deformity

corrections and revision fusions, CAN was used in >40% of cases by 38.6%, 36.6%, 32.1% and

70

38.6% of respondents, respectively, with no differences between surgical specialties (Figure 3-

4). In partitioned Chi-squared analysis, there was no significant difference in CAN usage

between case types.

Figure 3-4. Trainee-reported CAN usage. Histograms depicting trainee-reported CAN usage at their respective

institutions, for open instrumented fusions (A), minimally-invasive instrumented fusions (B), deformity corrections (C),

and revision instrumented fusions (D), stratified by trainee specialty.

71

34.1% of residents identified as being fully capable in the setup and intra-operative use of the

CAN system available at their institution, either independently or with minimal supervision,

significantly greater among neurosurgical than orthopedic trainees (48.1% vs. 11.8%, p=0.008).

Instruction on CAN setup/use was provided by surgical faculty for 75.0% of respondents, by

CAN product representatives for 52.3%, by fellows for 22.7%, by senior residents for 20.5%,

and by self-teaching for 22.7%.

3.4.5 Impact of CAN on Trainee Proficiency

Self-reported trainee proficiency with instrumentation in the atlantoaxial, subaxial cervical,

thoracic and lumbosacral spine was compared with and without CAN guidance (Appendix A,

Questions #13-20). An 11.0% increase in mean proficiency rank (2.93 vs 2.64, p=0.036) was

seen for thoracic pedicle screws with vs. without CAN guidance, across all respondents (Figure

3-5).

72

Figure 3-5. Trainee proficiency in CAN. Histogram demonstrating mean self-reported proficiency rank of trainees

for the placement of atlantoaxial, subaxial cervical, thoracic and lumbosacral instrumentation, with vs. without CAN

guidance. Proficiency was self-reported as 1 = not at all competent, 2 = somewhat competent, requiring extensive

supervision; 3 = very competent, with supervision; 4 = fully independent, without supervision. (*) denotes significant

difference at p<0.05.

When stratified by specialty, neurosurgical residents reported improved but statistically

insignificant gains in proficiency with CAN guidance for thoracic instrumentation (2.85 vs 2.59,

p=0.198), whereas orthopedic surgical residents reported an 18.0% increase in mean proficiency

rank with atlantoaxial instrumentation (2.29 vs 1.94, p=0.014) as well as a 12.9% increase in

mean proficiency with thoracic instrumentation, just missing statistical significance (3.06 vs

2.71, p=0.058).

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3.5 Discussion

The adoption of spinal CAN remains limited by steep learning curves with potentially prolonged

operating times initially, and significant workflow disturbances primarily from registration

protocols.(Choo et al., 2008; Hartl et al., 2013; Ryang et al., 2015; Wood & McMillen, 2014)

Increasing the uptake of a technology proven to improve accuracy and patient outcomes requires

an understanding of current practice patterns and barriers to adoption. To our knowledge, our

study represents the first to explore the use of spinal CAN in a single-payer health care system.

We show here that spinal CAN is used predominantly by neurosurgeons and in academic

institutions. This conclusion is unsurprising given that intra-operative frameless stereotactic

CAN was developed originally for intracranial tumor localization.(David W. Roberts et al.,

1986) Two-year revision surgery rates were increased for trauma and deformity cases, for those

performed by orthopedic surgeons, at academic institutions, and when performed without CAN

guidance. Increased revisions for trauma and deformity cases, particularly at academic centers,

are to be expected given their relatively greater complexity. Only pathology remained

independently associated with revision in hierarchical mixed-effects modelling, with minimal

variability due to institutions and providers, suggesting additional variables, including case

complexity, operative time, and patient-level co-morbidities, as potential contributors which

were unaccounted for in our models.

While surgical technique, beyond anterior vs. posterior approaches, was not captured in the

database review, our online survey of Canadian surgical trainees revealed that CAN was used

equally for open fusions, MIS fusions, revision fusions and deformity cases. This contrasts with

the trend seen in the United States, where CAN appears to be used most often in high-volume

MIS practices, and may reflect a lack of deployment of CAN, in Canada, in the settings in which

it is clinically most useful.(Hartl et al., 2013) Conversely, the relative deficiency of CAN in MIS

and deformity procedures in Canada may reflect a relatively lower volume of these cases overall,

74

due in part to prolonged operating times and increased OR radiation exposure with MIS cases

compared to equivalent open procedures.(Bindal et al., 2008; Funao et al., 2014) Both issues are

addressed by current and emerging CAN techniques; willingness of institutions and practitioners

to adopt CAN technology may encourage safer, more efficient, and less invasive spinal

procedures for patients.(Jakubovic et al., 2016)

Real-time CAN feedback on anatomic landmark identification may also be beneficial for trainees

in learning spinal anatomy and nuances of instrumentation. A review of the literature revealed 7

studies assessing the utility of CAN for trainee education, all in an ex-vivo virtual-reality or

cadaveric/phantom setting.(Gasco et al., 2014; Michael B. Gottschalk et al., 2015; Lorias-

Espinoza et al., 2016; Luciano et al., 2011; Podolsky et al., 2010; Rambani et al., 2014; Sundar et

al., 2016) To our knowledge, our study represents the first to explore the utility of CAN intra-

operatively for trainee comfort and proficiency in placing instrumentation.

In our online survey, only one-third of residents reported being fully capable of setting up and

using a CAN system without or with minimal supervision, greater among neurosurgical than

orthopedic surgical trainees. The lack of comfort in CAN use among residents overall is reflected

in the similar lack of comfort and training for current faculty, one of the major barriers to

adoption that may be addressable through improved practical education at the trainee level.(Choo

et al., 2008; Hartl et al., 2013) The relative lack of comfort with CAN for orthopedic surgical

trainees compared to their neurosurgical counterparts may be due in part to a lack of familiarity

with CAN from non-spinal procedures, as well as significantly less time spent on a dedicated

spine service. However, the intra-operative use of CAN appears to improve the self-reported

proficiency of all trainees, in fact to a greater degree for orthopedic surgical trainees. This is in

keeping with the findings of ex-vivo laboratory studies.(Sundar et al., 2016) Given that

orthopedic surgeons performed most of the instrumented spinal fusions in our retrospectively-

reviewed Ontario cohort, it may be prudent to increase education in spinal CAN techniques at the

trainee level, to improve adoption particularly within the orthopedics community and thereby

maximize the potential benefits of CAN. Most respondents in our cohort reported being

instructed on CAN use by their attendings; improvement in trainee CAN education thus requires

75

increased adoption amongst faculty, by addressing known concerns with CAN such as workflow

and registration hindrances.(Choo et al., 2008; Hartl et al., 2013; Jakubovic et al., 2016) As

trainees and future faculty increase familiarity with CAN techniques and maximize their

benefits, the cost-effectiveness of CAN, currently greatest in high-volume academic centers, may

well trickle down to community institutions where a greater number of patients are treated.

Our retrospective database review is subject to the typical limitations of using an administrative

database, including inconsistent coding particularly for pathology, and for the indication for

revision surgery. Case complexity was not captured in the administrative database. Traumatic

pathologies were heavily under-represented in this dataset, at <5% of all cases. Our electronic

survey of surgical trainees captured self-reported proficiency in spinal instrumentation. Future

studies may assess the impact of intra-operative CAN on trainee proficiency with more objective

metrics, such as quantitative screw accuracy and/or time required per screw.

76

3.6 Conclusions

In a large provincial cohort, intra-operative navigation was employed for less than one-fifth of

instrumented spinal fusions, more frequently by neurosurgeons than orthopedic spinal surgeons,

and more often in academic than community institutions. The use of spinal CAN was associated

with a significant reduction in revision surgery. At a trainee level, almost two-thirds of

orthopedic surgical and neurosurgical trainees are not fully comfortable with the setup and use of

CAN. The use of CAN improves self-reported trainee proficiency in placing thoracic

instrumentation. Increasing practical education in spinal CAN from a trainee stage, particularly

in orthopedic surgery, may increase adoption and maximize the benefits of CAN for the greatest

population of patients.

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3.7 Supplemental | Diagnostic and Fee Coding

The following combination of fee (OHIP) and diagnostic (ICD-9-CM) codes were applied to flag

patients stratified by pathology (trauma, degenerative, deformity, infection, tumour,

vertebroplasty/kyphoplasty), spine region (cervical, thoracic, lumbosacral), and approach

(anterior, posterior):

Trauma:

- Cervical:

o Anterior:

▪ OHIP code: N573/N516/E365

▪ Flag if one of the following ICD-9-CM code is associated with the records

above: 805.0x/805.1x/806.0x/806.1x/952.0x

o Posterior:

▪ OHIP code: (N572 + E370) OR (N572 + E384):


above: 805.0x/805.1x/806.0x/806.1x/952.0x

- Thoracic:

o Anterior:

▪ OHIP code: N517/N518/E365:


above: 805.2/805.3/806.2x/806.3x/952.1x

o Posterior:

▪ OHIP code: (N572 + E370):


above: 805.2/805.3/806.2x/806.3x/952.1x

- Lumbosacral:

o Anterior:

▪ OHIP code: N559/N580/E365:


above:

805.4/805.5/805.6/805.7/806.4/806.5/806.6x/806.7x/952.2/952.3/952.4

o Posterior:

▪ OHIP code: (N572 + E370):


above:

805.4/805.5/805.6/805.7/806.4/806.5/806.6x/806.7x/952.2/952.3/952.4

78

Degenerative:

- Cervical:

o Anterior:

▪ OHIP code: N573/N516/N526/E365:


above: 721.0/721.1/722.0/722.4/723.x/722.71/722.81/722.91

o Posterior:

▪ OHIP code: N532/N515/N513/N528/E370/E384:


above: 721.0/721.1/722.0/722.4/723.x/722.71/722.81/722.91

- Thoracic:

o Anterior:

▪ OHIP code: N517/N518/E365:


above: 721.2/721.41/722.51/722.11/722.72/722.82/722.92/724.01/724.1

o Posterior:

▪ OHIP code: N513/N515/N526/E370:


above: 721.2/721.41/722.51/722.11/722.72/722.82/722.92/724.01/724.1

- Lumbosacral:

o Anterior:

▪ OHIP code: N559/N580/E365:


above:

721.3/721.42/722.1/722.52/722.83/722.10/722.73/722.93/724.02/724.03/7

24.2/724.3/724.6/724.7

o Posterior:

▪ OHIP code: N513/N582/N533/N526/E370/E387/E372:


above:

721.3/721.42/722.1/722.52/722.83/722.10/722.73/722.93/724.02/724.03/7

24.2/724.3/724.6/724.7

- Unspecified:

o OHIP code: N532/N515/N513/N526/N528/N533/N582/E370/E384/E387/E372:

o Flag if one of the following ICD-9-CM code is associated with the records above:

714.x/720.x/721.6/721.7/721.9x/722.2/722.6/722.7

Deformity:

o Anterior:

▪ OHIP code: N539:


above: 737.x

o Posterior:

▪ OHIP code: N540:

79


above: 737.x

o Unspecified osteotomy:

▪ OHIP code: (N574/N575/N576 + E370):


above: 737.x

Infection:

- By OHIP code only (please provide primary ICD-9 code for manual confirmation)

- OHIP code: (N548/N549/N550 + E370)

Tumour:

- by OHIP code only (please provide primary ICD-9 code for manual confirmation)

- OHIP code: (N560/N561/N553/N554/E386 + E370), WITHOUT any combination from

the ‘Infection’ label (above)(b/c the E386 code covers both tumour and infection)

Vertebroplasty/Kyphoplasty:

- by OHIP code only (please provide primary ICD-9 code for manual confirmation)

- OHIP code: N570/N583/E388/E392

Other:

- OHIP code E370, plus:

- ICD-9-CM code: 336.x/741.x

Navigation usage was identified by OHIP code, flagged as either 2D-CAN (E379) or 3D-CAN

(E378). Revision fusions were identified by OHIP code, flagged by the billing of E375 for the

same patient ID within 2 years of the index procedure.

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3.8 Supplemental | Online Survey

1) What is your current training level?

a. PGY-1

b. PGY-2

c. PGY-3

d. PGY-4

e. PGY-5

f. PGY-6

g. Clinical spine fellow

2) Which residency program are you enrolled in/have you completed?

a. Neurosurgery

b. Orthopedic surgery

3) In which province are you currently training?

a. British Columbia

b. Alberta

c. Saskatchewan

d. Manitoba

e. Ontario

f. Quebec

g. Nova Scotia

h. Newfoundland

4) How many months of residency did you spend on a full-time spine service?

5) Do you have computer-assisted 3D navigation capabilities for spinal surgery at your

institution? If ‘yes’, please complete the remaining questions.

a. Yes

b. No

6) In what percent of overall spine cases at your institution is computer-assisted 3D

navigation utilized?

a. 0-20%

b. 21-40%

c. 41-60%

d. 61-80%

e. 81-100%

81

7) In what percent of open spinal fusions at your institution is computer-assisted 3D


a. 0-20%

b. 21-40%

c. 41-60%

d. 61-80%

e. 81-100%

8) In what percent of minimally-invasive spinal fusion cases at your institution is computer-

assisted 3D navigation utilized?

a. 0-20%

b. 21-40%

c. 41-60%

d. 61-80%

e. 81-100%

f. N/A (minimally-invasive cases not performed at this institution)

9) In what percent of deformity correction cases at your institution is computer-assisted 3D


a. 0-20%

b. 21-40%

c. 41-60%

d. 61-80%

e. 81-100%

f. N/A (deformity cases not performed at this institution)

10) In what percent of revision spinal fusion cases at your institution is computer-assisted 3D


a. 0-20%

b. 21-40%

c. 41-60%

d. 61-80%

e. 81-100%

11) From which sources do you receive your primary teaching/instruction on the use of

computer-assisted 3D navigation in spinal surgery? Select all that apply.

a. Senior resident

b. Clinical spine fellow

c. Staff surgeon

d. Navigation company/product representatives

e. Self-taught

f. Other: __________

82

12) Please rate your current level of comfort in the setup and intraoperative use of the

computer-assisted 3D navigation available at your institution, for spinal surgery.

(includes transferring of images, intraoperative registration, navigation)

a. Fully able to set up and use, independently

b. Fully able to set up and use, with supervision

c. Able to set up and use most intraoperative features, with supervision

d. Able to perform minimal setup and use some intraoperative features, with

extensive supervision

e. No concept of setup or intraoperative use

13) Please rate your confidence, at your current level of training, in placing atlantoaxial

screws (C1 and/or C2) WITHOUT computer-assisted 3D navigation (X-ray/fluoroscopy

may be used, if required).

a. Fully independent, without supervision

b. Very competent, with supervision

c. Somewhat competent, requiring extensive supervision

d. Not at all competent

14) Please rate your confidence, at your current level of training, in placing atlantoaxial

screws (C1 and/or C2) WITH computer-assisted 3D navigation.





15) Please rate your confidence, at your current level of training, in placing subaxial cervical-

spine screws (C3-7) WITHOUT computer-assisted 3D navigation (X-ray/fluoroscopy

may be used, if required).





16) Please rate your confidence, at your current level of training, in placing subaxial cervical-

spine screws (C3-7) WITH computer-assisted 3D navigation.





83

17) Please rate your confidence, at your current level of training, in placing thoracic-spine

pedicle screws WITHOUT computer-assisted 3D navigation (X-ray/fluoroscopy may be

used, if required).





18) Please rate your confidence, at your current level of training, in placing thoracic-spine

pedicle screws WITH computer-assisted 3D navigation.





19) Please rate your confidence, at your current level of training, in placing lumbosacral

pedicle screws WITHOUT computer-assisted 3D navigation (X-ray/fluoroscopy may be

used, if required).





20) Please rate your confidence, at your current level of training, in placing lumbosacral

pedicle screws WITH computer-assisted 3D navigation.





21) How has your use of computer-assisted 3D navigation, for spinal surgery, changed your

likelihood of pursuing specialized (i.e. fellowship) spine training?

a. Significantly increased

b. Slightly increased

c. Unchanged

d. Slightly decreased

e. Significantly decreased

22) How has your use of computer-assisted 3D navigation, for spinal surgery, changed your

likelihood of incorporating spine cases into your fuure practice?

a. Significantly increased

b. Slightly increased

c. Unchanged

d. Slightly decreased

e. Significantly decreased

84

Chapter 4 Correlation Between Clinical and Absolute Engineering Accuracy

in Spinal Computer-Assisted Navigation

Preamble


Guha D, Jakubovic R, Gupta S, Alotaibi NM, Cadotte D, da Costa LB, George R, Heyn C,

Howard P, Kapadia A, Klostranec JM, Phan N, Tan G, Mainprize TG, Yee A, Yang VXD.

Spinal intra-operative three-dimensional navigation: correlation between clinical and absolute

engineering accuracy. Spine J 2017;17(4):489-98.

85

4.1 Abstract

Spinal intra-operative computer-assisted navigation (CAN) may guide pedicle screw placement.

CAN techniques have been reported to reduce pedicle screw breach rates across all spinal levels.

However, definitions of screw breach vary widely across studies, if reported at all. The absolute

quantitative error of spinal navigation systems is theoretically a more precise and generalizable

metric of navigation accuracy. It has also been computed variably, and reported in fewer than a

quarter of clinical studies of CAN-guided pedicle screw accuracy. Here, we aim to characterize

the correlation between clinical pedicle screw accuracy, based on post-operative imaging, and

absolute quantitative navigation accuracy. We reviewed a prospectively-collected series of 209

pedicle screws placed in 30 patients with CAN guidance. Each screw was graded clinically by

multiple independent raters using the Heary and 2mm classifications. Clinical grades were

dichotomized per convention. The absolute accuracy of each screw was quantified by the

translational and angular error in each of the axial and sagittal planes. Acceptable screw accuracy

was achieved for significantly fewer screws based on 2mm grade vs. Heary grade (92.6% vs.

95.1%, p = 0.036), particularly in the lumbar spine. Inter-rater agreement was good for the Heary

classification and moderate for the 2mm grade, significantly greater among radiologists than

surgeon raters. Mean absolute translational/angular accuracies were 1.75 mm/3.13 and 1.20

mm/3.64 in the axial and sagittal planes, respectively. There was no correlation between clinical

and absolute navigation accuracy, as surgeons appear to compensate for navigation registration

error. Future studies of navigation accuracy should report absolute translational and angular

errors. Clinical screw grades based on post-operative imaging may be more reliable if performed

in multiple by radiologist raters.

86

4.2 Introduction

Intra-operative three-dimensional computer-assisted navigation (CAN) is used routinely in

cranial neurosurgery for the localization of subsurface structures. While not employed as

frequently, navigation for spinal procedures may guide implant placement and bony

decompression, particularly in minimally-invasive and complex deformity-correcting procedures

where anatomic landmarks are less readily identifiable.(Bandiera et al., 2013; Hartl et al., 2013;

Sakai et al., 2008)

Modern spinal CAN techniques employ two-dimensional (2D) guidance using “virtual”

fluoroscopy, or three-dimensional (3D) guidance based on either pre-operative or intra-operative

computed tomography (CT) imaging, as discussed in Chapter 2.(A C Bourgeois et al., 2015) The

accuracy of spinal navigation systems is most easily studied for pedicle screw placement, as

instrumentation is reliably localized on post-operative imaging. CAN techniques have been

widely reported to reduce pedicle screw breach rates, from 12-40% under freehand or

fluoroscopic guidance to under 5% with 3D CAN.(L. P. Amiot et al., 2000; M Bydon et al.,

2014; Castro et al., 1996; Eric W Nottmeier, Seemer, & Young, 2009; Rajasekaran et al., 2007;

B. J. Shin et al., 2012; Y Wang et al., 2013) Improved instrumentation accuracy is seen across all

3D CAN techniques, in each of the cervical, thoracic, lumbar and sacral regions.(Barsa, Frőhlich,

Šercl, Buchvald, & Suchomel, 2016; Austin C Bourgeois et al., 2015; Hecht et al., 2010; Mason

et al., 2014; N. F. Tian et al., 2011)

The clinical accuracy of spinal CAN for pedicle screw placement is variably reported. Up to half

of studies assessing pedicle screw accuracy do not define methods of determining screw

‘breach’, and no consistent grading system is used by those that do.(Aoude et al., 2015; B. J.

Shin et al., 2012) The absolute accuracy of spinal navigation systems, quantified most commonly

by the target registration error (TRE), has been reported to varying extent in fewer than ten

human clinical studies since 2000, while more than forty studies on CAN-guided pedicle screw

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placement have been published in the same period.(Belmont, Klemme, Dhawan, & Polly, 2001;

Haberland, Ebmeier, Grunewald, Hliscs, & Kalff, 2000; Kleck et al., 2016; Y. Kotani et al.,

2007; Mason et al., 2014; Mathew, Mok, & Goulet, 2013; Oertel, Hobart, Stein, Schreiber, &

Scharbrodt, 2011; Scheufler, Franke, Eckardt, & Dohmen, 2011a, 2011b; N. F. Tian et al., 2011)

Unsurprisingly, the absolute accuracy requirements of spinal CAN systems, and their

relationship to radiographic screw position and clinical outcomes, remain poorly

defined.(Rampersaud, Simon, & Foley, 2001)

Here, we therefore review a prospectively-collected series of 209 pedicle screws placed with 3D

CAN guidance, with clinical accuracy grading using two established scoring systems, as well as

quantitation of absolute translational and angular navigation accuracy, to identify any correlation

between clinical and engineering accuracies.

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4.3 Methods

4.3.1 Patient Selection

Thirty patients enrolled in a prospective comparative trial of our research group’s optical

topographic 3D CAN system (Chapter 5) against two commercially-available 3D CAN systems,

were retrospectively reviewed. All patients underwent posterior cervical/thoracic/lumbar/sacral

instrumented fusion with pedicle screw constructs, with or without decompression, for

predominantly traumatic, degenerative, or neoplastic pathologies. Procedures were performed at

Sunnybrook Health Sciences Centre by a single surgeon (VXDY), with or without trainee

assistance, from May 2014 – February 2015.

4.3.2 Intra-Operative Navigation

All screws were placed with 3D CAN guidance using either the NAV3/3i (Stryker; Portage, MI,

USA), registered to pre-operative imaging with point-matching followed by surface refinement,

or the StealthStation S7 (Medtronic Sofamor Danek; Memphis, TN, USA), registered to intra-

operative imaging using the O-Arm™ (Medtronic). Pre-operative CT scans were performed at a

slice thickness of 0.625mm, on a GE LightSpeed VCT scanner. All patients underwent post-

operative CT imaging of the instrumented region, using the same scanner at a slice thickness of

0.625mm.

4.3.3 Clinical Grading

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Clinical grading of pedicle screw accuracy was performed on post-operative CT imaging using

two established methods, the Heary and 2mm classifications.(Belmont et al., 2001; Heary et al.,

2004) Summaries of each scoring system are shown in Tables 4-1 and 4-2, respectively. Heary

grading was performed for all screws independently by one neurosurgeon (DC), two orthopedic

surgeons (RG, GT) and two radiologists (CH, PH). 2mm grading was performed for all screws

independently by two neurosurgeons (NMA, DG) and two radiologists (AK, JMK). Reviewers

for each scoring system were blinded to the results of the other. Clinical grades were

dichotomized as acceptable (Heary grade ≤ 2, or 2mm grade ≤ 2) or poor (Heary grade >2, or

2mm grade >2), as has been previously reported.(Aoude et al., 2015; Belmont et al., 2001; Heary

et al., 2004)

Table 4-1. Heary Classification for pedicle screw placement.

Grade Definition

1 Shaft + tip contained entirely within pedicle

2 Shaft violates lateral pedicle, but tip entirely contained within vertebral body

3 Tip penetrates anterior or lateral vertebral body

4 Shaft breaches medial or inferior pedicle wall

5 Tip or shaft violates pedicle or vertebral body, and endangers spinal cord, nerve

root(s) or great vessels, requiring immediate revision

Table 4-2. 2mm classification for pedicle screw placement.

Grade Definition

1 Shaft contained entirely within pedicle

2 Shaft violates pedicle cortical wall by <2mm

3 Shaft violates pedicle cortical wall by 2-4mm

4 Shaft violates pedicle cortical wall by >4mm

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4.3.4 Quantitative Navigation Application Accuracy

Absolute navigation accuracy was measured by comparing the final screw position, on post-

operative CT imaging, to a screenshot of the planned screw entry point and trajectory, as seen by

the navigation system intra-operatively. Translational and angular deviation from the planned

entry point and trajectory were than quantified, in both the axial and sagittal planes, using

multiplanar reformatting of both pre- and post-operative CT imaging. The method of absolute

navigation error quantification is adapted from those described by Mathew et al. and Kotani et al.

(Figure 4-1).(Y. Kotani et al., 2007; Mathew et al., 2013) In the axial plane, positive translational

deviations denote a lateral deflection of the entry point, and positive angular deviations denote a

more lateral trajectory. In the sagittal plane, positive translational deviations denote a superior

deflection of the entry point, and positive angular deviations denote a more cranial trajectory.

All image processing and measurements were performed using an Osirix 64-bit workstation

(version 10.9.5; PIXMEO SARL, Switzerland).

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Figure 4-1. Quantification of navigation application accuracy. Measurement of absolute navigation accuracy, in

the axial (A+C) and sagittal (B+D) planes. Comparison is made between intra-operative navigation screenshots of

planned entry points and trajectories (A+B), to final screw positions on post-operative CT (C+D). Reference lines

(dashed) are drawn, in the axial plane, in the mid-sagittal line (bisecting the vertebral body, spinal canal and spinous

process) and, in the sagittal plane, along the inferior endplate. Translational error is computed as (d1-d); angular error

is computed as (1-).

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4.3.5 Statistical Analysis

Inter-rater reliabilities (IRR) of Heary and 2mm grades were computed using two-way

consistency average-measures intraclass correlation coefficients (ICC), appropriate for a fully-

crossed design.(Shrout & Fleiss, 1979) As an approximation, ICC values between 0.60 and 0.74

were reflective of moderate agreement, 0.75 to 0.89 good agreement, and 0.90-1.00 excellent

agreement.(Cicchetti, 1994) Frequencies of categorical data were analysed using Fisher’s exact

tests. For paired categorical data, including the frequencies of poor-grade screws on both Heary

and 2mm grading scales, McNemar-Bowker tests of marginal homogeneity were used.

Correlation of clinical grading with absolute navigation errors were performed by independent-

samples t-tests as well as generalized linear regression models. Regression models were first

tested for nonlinearity with three cubic splines, with subsequent elimination of all nonsignificant

nonlinear terms from the final model. Significance levels for all tests were set at < 0.05.

All statistical analyses were performed in SPSS Statistics (version 21; IBM, Chicago, IL, USA).

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4.4 Results

A total of 209 pedicle screws from 30 patients were included in our analysis. 3 screws were

placed in the cervical spine (all at C7), 138 in the thoracic spine, 64 in the lumbar spine, and 4 in

the sacrum (all at S1).

4.4.1 Clinical Accuracy

Of 209 screws, with 932 combined Heary grades from five independent reviewers, 95.1% were

rated as acceptable. On the 2mm grading scale, from four independent reviewers assessing the

same dataset, significantly fewer screws were rated as acceptable, at 92.6%. These differences

did not persist when thoracic screws were analyzed independently, however the Heary grading

system was significantly more generous in the lumbar spine. Cervical and sacral screws were not

analyzed independently due to inadequate sample size. A summary of clinical grades is presented

in Table 4-3.

The intraclass correlation coefficient, a measure of inter-rater reliability, was 0.763 (95%-CI

0.665-0.809) for Heary grade, 0.428 among the three surgeon raters and 0.781 among the two

radiologist raters. For the 2mm grade, overall ICC was 0.611 (95%-CI 0.517-0.690), 0.21 among

the two surgeon raters and 0.678 among the two radiologist raters.

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Table 4-3. Clinicoradiographic grades of 209 pedicle screws.

Heary Grade

(# of ratings)

2mm Grade

(# of ratings)

Difference

(absolute value)

Significance

All screws

Acceptable 886 (95.1%) 774 (92.6%) 2.48% p = 0.036*

Poor 46 (4.9%) 62 (7.4%)

Thoracic screws

Acceptable 584 (93.4%) 509 (92.2%) 1.23% p = 0.43

Poor 41 (6.6%) 43 (7.8%)

Lumbar screws

Acceptable 272 (98.6%) 240 (93.8%) 4.80% p = 0.005*

Poor 4 (1.4%) 16 (6.2%)

(*) denotes significance at < 0.05

4.4.2 Absolute Application Accuracy

The absolute translational and angular errors of all screws in our cohort, in both axial and sagittal

planes, are shown in Figure 4-2. For all screws, axial and sagittal translational errors were (1.8 ±

3.6 mm) and (1.2 ± 1.1 mm), respectively, while axial and sagittal angular errors were (3.1 ±

2.9) and (3.6 ± 3.4), respectively (mean ± SD). Axial angular errors were greater in the lumbar

vs. thoracic spine (mean 3.7 vs. 2.6, respectively; p = 0.018); all other errors were equivalent

across spinal regions.

4.4.3 Clinical-Engineering Correlation

In a generalized linear regression model, there was no correlation between any absolute

navigation error parameter, and the mean Heary grade across all raters (Figure 4-3). Comparison

of absolute navigation errors between ‘poor’ and ‘acceptable’ dichotomized Heary grades also

revealed no significant differences. Similarly, no correlation was observed between absolute

navigation errors and the mean 2mm grade (Figure 4-3). Comparison of absolute navigation

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errors between ‘poor’ and ‘acceptable’ dichotomized 2mm grades revealed no significant

differences.

Figure 4-2. Absolute navigation application accuracy for 209 pedicle screws. Standard boxplots demonstrating

the translational (A) and angular (B) absolute navigation errors in axial and sagittal planes. Boxplot height

corresponds to the interquartile range (IQR), whiskers correspond to 1.5xIQR, circular points are those outside

1.5xIQR, asterisked points are those outside 3xIQR.

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Figure 4-3. Correlation between absolute navigation application error and clinicoradiographic grade.

Scatterplots of mean Heary grade (top row) and mean 2mm grade (bottom row), vs. each of axial and sagittal

translational and angular errors. No correlation is seen between Heary grade or 2mm grade, and any absolute

navigation error parameter.

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4.4.4 Surgeon Compensation for Navigation Error

We theorized that the lack of observed correlation between clinical screw grade and absolute

navigation accuracy may be due, in part, to surgeon compensation for perceived misalignment of

virtual and anatomic intended screw entry points, based on surgeon visualization and knowledge

of anatomic landmarks. For instance, a ‘perfect’ entry point as shown by the navigation system,

that is felt by the surgeon to be excessively lateral based on anatomic knowledge, may lead the

surgeon to compensate by medializing their screw trajectory (Figure 4-4). In this situation, the

signed axial translational error is expected to be positive, with a corresponding negative axial

angular error.

Linear regression models were therefore generated between signed translational and angular

errors, in the axial and sagittal planes, respectively. Negative linear correlations were observed

for both axial (p < 0.001) and sagittal (p < 0.001) errors, suggestive of surgeon compensation

occurring in both planes, greater in the axial than sagittal plane (Figure 4-5).

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Figure 4-4. Potential mechanism for surgeon compensation. Visualization of potential mechanism of surgeon

compensation for perceived navigation registration error. In the axial plane (A), a lateral translational error may lead

the surgeon to medialize the screw trajectory, leading to inversely-signed translational and angular errors (d2-d1 > 0;

2-1 < 0). In the sagittal plane (B), a rostral translational error may lead the surgeon to direct the screw more

caudally, with similar inversely-signed translational and angular errors.

Figure 4-5. Correlation between translational and angular navigation errors. Scatterplots of angular vs.

translational errors in the axial (A) and sagittal (B) planes. Least-squares regression lines (solid) and 95% confidence

intervals (dashed) are shown, along with regression coefficients (ß).

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4.5 Discussion

The primary purported benefit of CAN for spinal procedures is improved instrumentation

accuracy and, in theory, minimization of complications from breached screws. Clinical sequelae

of screw breach include, acutely, neurologic and vascular injury and, in the longer term,

pseudoarthrosis due to poor osseous purchase and load-bearing.(Verma et al., 2010) As CAN

techniques evolve, from 2D-fluoro to 3D-fluoro to intra-operative CT-based registration, from

surgeon-manipulated to robotically-actuated instruments, the body of literature on navigation

accuracy is rapidly expanding. In an era of fiscally-responsible health care, the cost-effectiveness

of various CAN techniques, in relation to their purported accuracy, is also being explored.(Dea et

al., 2015; Watkins et al., 2010)

The grading systems used to quantify navigation accuracy for pedicle screw insertion remain

highly heterogeneous.(Aoude et al., 2015) Some, such as the 2mm grading system, are based on

screw shaft relation to the pedicle wall alone, while others, such as the Heary classification,

include the relation of the screw tip to the vertebral body.(Gertzbein & Robbins, 1990; Heary et

al., 2004) Similarly, while most scales quantify only the amount of pedicle wall breach, others

have demonstrated the importance of directionality, with lateral breaches less likely to be

clinically significant.(Güven et al., 1994; O’Brien et al., 2000) As assessments of in vivo screw

accuracy are based on post-operative CT imaging, metallic artefact may also contribute to the

reliability of accuracy ratings, though the type of screw material has been shown not to impact

inter-rater reliability.(Lavelle et al., 2014)

The commonest grading system in current use is the 2mm classification, with variations in the

grade cutoffs ranging from >4mm breach to >6mm breach for Grade IV screws.(Gertzbein &

Robbins, 1990; Neo, Sakamoto, Fujibayashi, & Nakamura, 2005) The increasingly-popular

Heary classification accounts for tip position relative to the vertebral body, and emphasizes

medial/inferior breaches over less clinically-relevant lateral breaches, regardless of magnitude. In

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our series, the Heary classification was significantly more conservative than the 2mm grade in

identifying poorly-placed screws, but only among lumbar pedicle screws. While the Heary grade

was developed for thoracic pedicle screws and has not formally been validated in the lumbar

spine, it can reasonably be expected to perform similarly as its emphasis is to prioritize breaches

more likely to be symptomatic.(Heary et al., 2004) Anterior, medial and inferior perforations are

given greater weight in the Heary classification due to risk of injury to the

esophagus/trachea/lungs, spinal cord and nerve roots, respectively, in the thoracic spine. In the

lumbar spine, perforations in similar directions may injure the iliac vessels/bowel, conus

medullaris/cauda equina, and nerve roots, respectively. Therefore, the relatively aggressive

identification of poor-grade screws by the 2mm grading system in the lumbar spine, is likely

reflective of the larger pedicles and screw diameters relative to the thoracic spine. A breach of

2mm for a 7mm-diameter lumbar pedicle screw is far more likely to be tolerated by a surgeon

than the same 2mm breach for a 4.5mm-diameter thoracic screw. While the 2mm increment in

this classification is appropriately justified, and the grade cutoffs in our study are those used

most commonly in the literature, adjustment of grade cutoffs may be required across spinal

levels.(Abe et al., 2011; Belmont et al., 2001; Bledsoe, Fenton, Fogelson, & Nottmeier, 2009;

Cui et al., 2012; Gertzbein & Robbins, 1990; Kleck et al., 2016) For instance, ‘Grade IV: >4mm

breach’ may be appropriate for the thoracic spine, while ‘Grade IV: >6mm breach’ may be more

appropriate in the lumbar spine, as described originally by Gertzbein and Robbins.(Gertzbein &

Robbins, 1990)

Good inter-rater agreement was demonstrated in our series for the Heary classification, and

moderate agreement for the 2mm scale. For both scales, inter-rater reliability was significantly

higher amongst radiologists than surgeons. For the 2mm system, using the same grade cutoffs as

our study, ICC has been reported to vary from 0.45 to 0.69, in concordance with our results.(Bai

et al., 2013; Cho, Chan, Lee, & Lee, 2012; M.-C. Kim, Chung, Cho, Kim, & Chung, 2011;

Schizas, Thein, Kwiatkowski, & Kulik, 2012) To our knowledge, this is the first study reporting

on inter-rater reliability of the Heary classification. It is also the first to compare IRR between

radiologist and surgeon raters. Given the difficulty in distinguishing metallic screw artefact from

pedicle cortex on CT imaging, it is unsurprising that radiology-trained raters are more consistent.

It may therefore be prudent for future studies of navigation accuracy to employ multiple

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radiologists for rating clinical screw accuracy, rather than a single rater as has been done in more

than half of studies to date.(Aoude et al., 2015)

Absolute navigation accuracy, commonly quantified as the target registration error (TRE), likely

represents the most generalizable method of reporting navigation accuracy. The TRE of novel

navigation techniques is commonly quantified ex vivo using specialized fiducial-implanted

phantoms.(Koivukangas, Katisko, & Koivukangas, 2011; Uneri et al., 2015) However, in vivo

absolute navigation accuracy has been reported in only seven human clinical studies since

2000.(Haberland et al., 2000; Kleck et al., 2016; Y. Kotani et al., 2007; Mathew et al., 2013;

Oertel et al., 2011; Scheufler et al., 2011a, 2011b) Quantitation of absolute accuracy in these

studies is highly variable, with the majority reporting only angular error. Given that surgeons

employing CAN intra-operatively modulate both the position and angulation of their instruments,

in both the axial and sagittal planes based on in-plane views on the navigation display, error

tolerances in each of these parameters should be reported in future studies of navigation

accuracy.

We have shown here furthermore that clinical grading, on two commonly used scales, does not

correlate with absolute quantitative navigation accuracy. Using a novel technique of measuring

both translational and angular error in axial and sagittal planes, we have also demonstrated

quantitatively for the first time that surgeon compensation may lead to clinically-acceptable

screw placement despite navigation registration error. While the absolute accuracy requirements

for surgical navigation systems remain uncertain, in trained hands they are likely to be less

stringent than the submillimeter tolerances suggested by rigid mathematical models.(Rampersaud

et al., 2001) Conversely, while CAN is a useful intra-operative adjunct, it cannot and should not

replace dedicated subspecialty training, which affords the experience and anatomic knowledge

required to identify and compensate for navigation registration errors.

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Given the heterogeneity and inter-rater discordance in clinical grading scales, along with their

lack of correlation with engineering accuracy, reporting of absolute navigation accuracy along

with a summary of clinical sequelae may be a reasonable standard for future studies of

navigation accuracy. Acute neurovascular injury from breached screws are rare events, however,

and long-term pseudoarthrosis-related complications are difficult to attribute specifically to

breached screws.(Mason et al., 2014) Clinical screw grading based on post-operative imaging

will therefore continue to be of value in identifying breaches likely to cause significant sequelae.

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4.6 Conclusions

Radiographic grading scales of pedicle screw accuracy are highly heterogeneous, with variability

in performance across spinal levels, as well as in inter-rater reliability. Correlation between

clinical screw grade and absolute navigation accuracy is poor, in part due to surgeon

compensation for navigation error. Future studies of navigation accuracy should therefore report

absolute translational and angular navigation accuracy, along with relevant clinical sequelae of

any placed screws. If used, clinical screw grades based on post-operative imaging should ideally

be generalizable, validated, and include the directionality of breach, and may be more reliable if

performed in multiple by radiologist raters. Navigation systems are not intended to replace

quality surgical training, which affords the experience and anatomic knowledge required to

identify and compensate for navigation errors.

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Chapter 5 Optical Topographic Imaging with Efficient Registration to CT for

Spinal Intra-Operative Three-Dimensional Navigation

Preamble


Jakubovic R*, Guha D*, Gupta S, Lu M, Jivraj J, Standish BA, Leung MK, Mariampillai A, Lee

K, Siegler P, Skowron P, Farooq H, Nguyen N, Alarcon J, Deorajh R, Ramjist J, Ford M,

Howard P, Phan N, da Costa L, Heyn C, Tan G, George R, Cadotte DW, Mainprize TG, Yee A,

Yang VXD. High speed, high density intraoperative 3D optical topographical imaging with

efficient registration to MRI and CT for craniospinal surgical navigation. Manuscript in

submission.

*co-first authors

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5.1 Abstract

Intraoperative image-guided surgical navigation for craniospinal procedures has significantly

improved accuracy by providing an avenue for surgeons to visualize underlying internal

structures corresponding to exposed surface anatomy. Despite the obvious benefits of surgical

navigation, surgeon adoption remains relatively low due to long setup and registration times,

steep learning curves, high capital costs, and workflow disruptions. We introduce an

experimental navigation system utilizing optical topographic imaging (OTI) to acquire the 3D

surface anatomy of the surgical cavity, enabling visualization of internal structures relative to

exposed surface anatomy from registered pre-operative images. Our OTI approach includes near

instantaneous and accurate optical measurement of >250,000 surface points, computed at

>52,000 points-per-second for considerably faster patient registration than commercially

available benchmark computer-assisted navigation (CAN) systems, without compromising

spatial accuracy. Our experience of 162 pedicle screws placed with OTI-CAN demonstrated

significant workflow improvement relative to benchmark spinal computer-assisted navigation

systems, on the order of 6-20 fold faster, without compromising absolute application accuracy.

Our advancements provide the cornerstone for widespread adoption of image guidance

technologies for faster and safer surgeries without intraoperative CT or MR imaging. This work

represents a major workflow improvement for navigated spinal procedures, with readily-

conceivable extension to cranial as well as non-neurosurgical image-guided applications.

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5.2 Introduction

Intraoperative surgical navigation has become the standard-of-care in cranial neurosurgery for

the localization of subsurface structures, including neoplasms and vascular lesions, and for

targeting of electrical implants to specific nuclei. While not as ubiquitous, navigation for spinal

surgery has undergone significant evolution over the past decade. This technological

advancement has been driven by need, with 410,000 spinal fusion procedures performed in the

United States in 2008, a number expected to rise significantly over the next decades with an

aging population.(Ciol, Deyo, Howell, & Kreif, 1996; Rajaee, Bae, Kanim, & Delamarter, 2012)

While instrumentation is often used to facilitate osseous fusion, breach of screws outside the

intended trajectory occurs in 12-40% of screws (Figure 5-1).(Mohamad Bydon et al., 2014;

Castro et al., 1996; Rajasekaran et al., 2007; Yingsong Wang et al., 2013) This may result,

acutely, in neurovascular injury and, in the longer term, mechanical construct failure, causing

potentially life or limb-threatening complications which may require costly revision

surgery.(Verma et al., 2010; Watkins et al., 2010) Computer-assisted navigation has been

developed to improve the accuracy of screw placement at all spinal levels, reducing breach rates

to under ten percent (see Section 2.1.3.1).(L. P. Amiot et al., 2000; Arand, Schempf, Fleiter,

Kinzl, & Gebhard, 2006; Laine, Lund, Ylikoski, Lohikoski, & Schlenzka, 2000; G. Y. F. Lee,

Massicotte, & Raja Rampersaud, 2007; Eric W Nottmeier et al., 2009; Rajasekaran et al., 2007)

Navigation is also increasingly being applied to non-neurosurgical procedures, including hip and

knee arthroplasties, oral and maxillofacial reconstructions, delicate otologic drilling, and open

abdominal surgery.(L.-P. Amiot & Poulin, 2004) We demonstrate a new surgical navigation

technology, developed in our Biophotonics and Bioengineering Laboratory (BBL), using optical

topographical imaging (OTI) to create virtual 3D surfaces of open surgical cavities, allowing

surgeons to visualize internal structures relative to exposed surface anatomy (Figures 5-2, 5-3).

Our system completes full bony surface registration using graphics processing units (GPUs)

considerably faster than current systems, with comparable spatial resolution, sparing the patient

from additional radiation exposure, reducing operating room time and costs, and minimizing

disruption to surgical workflow.

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Despite the apparent benefit of spinal surgical navigation in reducing breach rates, adoption of

navigation as standard of care has been slow due to lengthy setup/registration times, steep

learning curves, and interruption of surgical workflow.(Wood & McMillen, 2014) Contemporary

benchmark navigation systems employ a paired-point registration protocol relying on surgeons to

drag a pointed probe across exposed bony anatomy to map to a pre-operative computed

tomography (CT) scan. These protocols have steep learning curves and take three to five-fold

longer per screw than traditional fluoroscopy, necessitating additional anesthetic and operating

room time.(Assaker, Reyns, Vinchon, Demondion, & Louis, 2001; Mirza et al., 2003; N. F. Tian

et al., 2011) Current registration protocols are unable to account for variances in spinal anatomy

due to changes in patient positioning from CT gantry to operating table, critical in trauma and

deformity cases. While this may be overcome with intra-operative 3D fluoroscopy or CT, it is at

the cost of significantly increased radiation to the patient, particularly with multilevel fusions,

and adds substantial setup time.(Manbachi, Cobbold, & Ginsberg, 2014; Nelson et al., 2014) Our

experimental navigation system confers significant benefit over currently available navigation

systems, implementing a simple point-picking protocol for initial approximate alignment

followed by rapid optical imaging registration to fuse the intra-operative surface anatomy with

the pre-operative CT. Rapid repeat registration allows for sequential segmental registration,

minimizing intersegmental deviation from pre-operative imaging to intra-operative positioning.

While we validate our system here for spinal navigation, the technology is immediately

applicable to cranial as well as non-neurosurgical navigation applications, with rapid repeat

registration lending itself well to future soft-tissue applications.

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Figure 5-1. Ideal thoracic pedicle screw placement. sIdeal thoracic pedicle screw entry point (dark red circle) and

trajectory (dashed red cylinder) in the coronal (A), axial (B) and sagittal (C) planes. Ideal entry point distance (d) and

trajectory angle () shown on axial and sagittal planes. Example of a misplaced thoracic pedicle screw via freehand

technique (D), Heary Grade V, with tip (arrowhead) abutting the aorta.

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5.3 Methods

5.3.1 OTI System Design

Our experimental OTI navigation system consists of a small camera gantry integrated with

surgical lighting. The camera gantry is composed of two grayscale visible-band cameras, a

digital micromirror pico-projector and an infra-red (IR) optical tracking system (Figure 5-2A-B).

The projector illuminates a structured light of known structure and periodicity (repeating bars)

sequentially, that is recorded by the cameras and used to reconstruct the 3D surface of the patient

(Figure 5-3A).(Geng, 2011) The patterns enable image correspondence between the stereo

cameras to be established, regardless of the natural texture of the scanned surface. With

calibrated cameras, the stereo images allow for 3D mapping of the surface correlating to the

various height disparities. The experimental navigation system registers the acquired 3D-point

cloud to pre-acquired imaging data (i.e. CT, MRI) using a surface registration algorithm that is

based on the iterative closest-point (ICP) algorithm (Figure 5-3C-D). The experimental

navigation system also utilizes a passive IR tracking system containing two IR cameras

surrounded by IR light-emitting diodes (LEDs) to illuminate the tracking volume. The IR system

is employed to track surgical tools using passive-reflective markers (Figure 5-3D).

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Figure 5-2. Clinical prototype of an experimental OTI navigation system. (A) Computer design model of the

surgical light head with embedded navigation. Designed to inconspicuously serve as traditional boom-supported

surgical light head comprised of 64 high intensity surgical light LEDs to provide standard lighting with minimal spectral

overlap with the navigation optics. Binocular infrared cameras utilizing provide real-time tracking of passive-reflective

markers mounted on surgical tools. A digital mirror device centered around binocular structured light cameras forming

an epipolar baseline provide intra-operative surface imaging for registration to the pre-operative images. Co-ordinates

of the tracked tools are easily matched to the acquired structured light surface image. (B) Computer design model of

the field of view of the infrared tracking volume (outer pyramid) and the structured light imaging volume (inner

pyramid). All measurements are in millimeters. (C) Prototype navigation system in clinical use. (D) Comparison of

total setup time (median and IQR) for cranial and spine applications of experimental (OTI) and benchmark navigation

systems.

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Figure 5-3. Optical topographic imaging (OTI) experimental navigation technique. (A) Structured light

illumination of the open surgical field. Structured light pattern deformations reflect height variations (along the optical

axis) of the surface. (B) Registered reconstructed surface data to pre-acquired imaging data with tool tracking

capabilities. (C) Grayscale stereoscopic cameras acquire surface images (left). Deformation of structured light

patterns are used to create 3D reconstructions and thresholded point-clouds representing the bony surface of the

spine (right). (D) Registration of the acquired 3D point cloud (left) to pre-acquired imaging data (right) using an

iterative closest point (ICP) algorithm based on a three point picking protocol for initial alignment.

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5.3.2 Specimen/Patient Selection

Pre-clinical technical development and validation was performed first ex-vivo in 6 adult human

formalin-fixed cadavers, and subsequently in-vivo in 10 anesthetized and ventilated adult swine.

All cadavers had no history of prior spinal surgery. All cadavers and porcine specimens

underwent pre- and post-operative helical CT imaging at 0.5 mm slice thickness for image-to-

patient registration (pre-operative) and quantitation of application accuracy (post-operative).

Institutional ethics and animal control board approvals were obtained (Mount Sinai Hospital

REB# 260-2011; Sunnybrook Health Sciences Centre AUP# 13-512).

Human clinical testing was performed in 92 adult patients, >18 years of age, without history of

prior spinal surgery. Institutional ethics board approval was obtained (Sunnybrook Health

Sciences Centre REB# 177-2013, 309-2014, 004-2015, 406-2015, 288-2016).

5.3.3 Pre-Clinical Testing

In all pre-clinical testing, both cadaveric and porcine, cadavers/specimens were positioned prone

on a standard operating table. Standard midline open posterior exposures were fashioned using a

combination of sharp dissection and, in-vivo, electrocautery. Exposures were performed to mimic

those typically required in open midline posterior instrumentation approaches clinically, that is,

with adequate exposure bilaterally of the medial half of the transverse process (thoracic spine)

and facet capsule (lumbar spine).

Ex-vivo cadaveric testing was employed for initial prototyping of an OTI-CAN system,

transitioning from stereocameras and a digital micromirror projector mounted on optical stages

to a unified design integrating all components into a surgical overhead lighting unit (Figure 5-2).

Subsequent in-vivo porcine work assessed interference between optical illumination for surgical

lighting and structured light illumination for OTI, as both are in the visible electromagnetic

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spectrum, for optimization and minimization. Porcine work was also required to establish the

required timing for 3D optical imaging, that is the time available to acquire a surface map of the

operative field within the limits of typical suctioning to clear pooling blood within the cavity.

Navigation application accuracy was also assessed first in in-vivo porcine trials, to include the

effect of ventilatory motion on final application accuracy. Following OTI registration to the

exposed posterior osseous spinal anatomy, OTI-CAN guidance was employed using a tracked

awl and gearshift probe for pedicle cannulation, followed by the placement of standard

appropriately-sized titanium thoracic and lumbar pedicle screws. All in-vivo procedures were

performed by a single surgeon (VXDY).

5.3.4 Human Clinical Testing

In-vivo human clinical validation was performed in a prospective comparative trial of OTI-CAN

against two benchmark spinal CAN systems, the NAV3/3i (Stryker; Portage, MI, USA) using a

paired-point and manual surface tracing registration protocol, and the StealthStation S7 coupled

with O-Arm for automatic registration to intra-operative imaging (Medtronic Sofamor Danek;

Memphis, TN, USA). Open posterior instrumented fusions were performed for stabilization

following either trauma, discoligamentous degeneration, or decompression of bony and epidural

tumor. All patients were positioned prone on a Wilson frame or four-post Jackson operating

table. Standard open midline exposures were performed similar to human cadaveric testing. All

in-vivo procedures were performed by a single surgeon (VXDY), with trainee assistance.

To maximize safety, human clinical trials began with a lead-in phase (Phase I) whereby OTI was

used to generate a 3D surface map of exposed anatomy and perform registration only, however

with instrumentation guidance by benchmark CAN or traditional freehand or fluoroscopic

techniques (Figure 5-4). This was followed by two validation phases: in the first (Phase II), both

a benchmark and an OTI-CAN system were available in the operating room, initially with

instrumentation guidance by benchmark CAN and measurement of established trajectories by

OTI, and subsequently with a cross-over whereby instrumentation guidance was by OTI-CAN;

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in the second (Phase III), OTI-CAN continued to be employed as the primary image-guidance,

but as a standalone device without secondary confirmation from a benchmark CAN system.

Figure 5-4. Flow diagram of OTI human clinical trials. Phase I: Lead-in phase, with screws placed using standard-

of-care freehand/fluoroscopic techniques or with image-guidance from benchmark CAN systems. OTI used to assess

feasibility of registration and subjectively verify accuracy, for further engineering refinement. Phase II: Initial validation

of OTI prototype system. In first half of phase II, instrumentation guided by benchmark CAN techniques with OTI used

for registration and verification of trajectory, with quantification of accuracy for verification. In second half of phase II,

instrumentation guided by OTI but with benchmark CAN used for verification of accuracy. Phase III: Final validation of

OTI prototype system, with OTI used as primary guidance system without benchmark CAN verification.

For OTI registrations, a custom dynamic reference frame (DRF) with passive-reflective optical

tracking array was clamped onto the spinous process of the level to be registered (‘segmental’

registration). Structured light illumination was then applied to generate a 3D surface of the

surgical cavity, which was automatically registered to the pre-operative CT dataset after

selection of three rough points on both hemilaminae and the spinous process of the vertebra to be

registered, to provide an initial alignment for the ICP algorithm (Figure 5-3). Following

registration, manual verification of registration accuracy was performed by dragging a tracked

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pointer/awl along known anatomic landmarks and assessing the quality of correlation in the

imaging space. If manual verification deemed the registration acceptable, navigation or trajectory

confirmation could proceed. If the registration was unacceptably inaccurate for navigation

purposes, as deemed by the operator, the registration protocol was repeated.

For registrations using either benchmark CAN suite, the respective proprietary DRF was affixed

segmentally to the spinous process of the level to be registered. Where the NAV3/3i was used,

points corresponding to readily-identifiable landmarks had been pre-selected on the pre-operative

CT dataset. These were matched intra-operatively using a tracked pointer to complete a paired-

point registration. The tracked pointer was then dragged along the exposed hemilaminae and

spinous process as a manual surface tracing for refinement of the registration, the accepted

practice for this class of device. Where the O-Arm was used, following DRF fixation the O-Arm

gantry was opened and the imaging device moved over the operating table in sterile fashion. An

intra-operative cone-beam CT scan was taken, and automatically registered to the patient

anatomy using the companion StealthStation S7 CAN system. Manual verification of registration

accuracy was performed for both benchmark systems in the same manner as for OTI-CAN, with

repetition of the respective registration sequences in cases of inaccurate or failed initial

registration.

For both benchmark and OTI-CAN systems, following successful registration a tracked awl and

gearshift probe were used to cannulate the target pedicle. A screenshot of the navigation

trajectory was taken with the gearshift probe in-situ at its deepest point in the cannulated pedicle

tract, to most accurately represent the planned navigation trajectory down the cannulated pedicle,

for assessments of navigation accuracy. For both benchmark and OTI systems, registration time

was recorded as the time from DRF fixation to completion of manual registration accuracy

verification, that is, being ready to navigate instrumentation. For procedures guided by the O-

Arm, this therefore included the time required for intra-operative imaging. Navigation accuracy

was graded both clinicoradiographically and quantitatively for screws placed with all systems

(see Sections 5.3.5 and 5.3.6).

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Prospective human trials were commenced with a goal of non-inferiority for quantitative

accuracy relative to benchmark CAN systems. While translational technical accuracy in a

laboratory phantom setting has been described for some current CAN systems including the O-

Arm cone-beam CT with StealthStation,(Koivukangas et al., 2011) translational application error

remains unknown, but is typically accepted to be ~2 mm. This is borne out by reported

translational application errors for cranial navigation systems, on the order of (2 ± 0.5) mm

(mean ± SD).(Steinmeier et al., 2000) Angular application error for thoracolumbar pedicle

screws has been reported in one study to be (2.8 ± 1.9)°,(Oertel et al., 2011) and in another to

range from (3.09 ± 2.12)° to (4.02 ± 2.63)°, depending on whether a navigated screwdriver was

used or not, respectively, to place instrumentation.(Kleck et al., 2016) Assuming a translational

error of (2.0 ± 0.5) mm and angular error of (3.0 ± 2.0)° for current spinal CAN systems, at α =

0.05 and β = 0.20 (i.e. power = 0.80), for two independent samples with continuous outcomes,

and a non-inferiority limit difference of 0.5 mm and 1.0°, 50 screws per group will be required.

All sample size calculations were performed in R (Version 3.4.1; R Project for Statistical

Computing).

5.3.5 Clinicoradiographic Accuracy Assessment

For all pedicle screws placed in pre-clinical porcine testing, as well as in Phase II + III of human

clinical in-vivo testing under both benchmark and OTI guidance, radiographic accuracy was

graded on post-operative CT imaging using the Heary classification (Table 4-1). Briefly, Grade I

denotes the screw is entirely contained within pedicle; Grade II the screw violates lateral pedicle

but screw tip is contained within the vertebral body; Grade III indicates the screw tip penetrates

anterior or lateral vertebral body; Grade IV indicates a medial or inferior breach of the pedicle;

Grade V involves a violation of the pedicle or vertebral body endangering the spinal cord, nerve

root, or great vessels. Heary grades were dichotomized per convention, into no breach/minor

breach (Heary Grade ≤ 2) vs. major breach (Heary Grade > 2). Radiographic grading was

performed independently by two neuroradiologists (CH, PH), three neurosurgeons (DWC, NP, &

LC) and two orthopedic spine surgeons (RG, GT). Any adverse clinical events intra or post-

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operatively, that is any neurovascular sequelae or any other instrumentation-related

complications, were also recorded.

5.3.6 Quantitative Application/Engineering Accuracy

For all pedicle screws placed in pre-clinical porcine testing, as well as in Phase II + III of human

clinical in-vivo testing under both benchmark and OTI guidance, pre- and post-operative CT

images were resliced to 0.3 mm thickness and dynamically resliced using multiplanar

reconstruction (MPR) corresponding to the axial and sagittal co-ordinates of the intra-operative

and post-operative screw trajectories. Deviations of each screw from planned navigation

trajectories were then computed by comparing the position of the screw on post-operative MPR

CT imaging, to a screenshot of the navigation trajectory taken intra-operatively. Deviations are

reported as the translational and angular errors in each of the axial and sagittal planes, based on

our reporting scheme devised in Chapter 4. Briefly, the distance from the axis of symmetry

perpendicular to the point of entry (translational error), as well as the angle between the screw

trajectory and the perpendicular distance of the entry point (angular error), were recorded. In the

axial plane, the axis of symmetry was the mid-vertebral line; in the sagittal plane, either the

superior or inferior endplate, consistent across levels (Figure 5-5). The entry point of the screw

was determined as the point where the center of the screw comes into contact with the vertebral

body. All measurements were performed using an OSIRIX 64-bit workstation (Version 10.9.5,

PIXMEO SARL, Switzerland).

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Figure 5-5. Quantification of absolute navigation application accuracy. Example shown of a patient with

hypoplastic pedicles at L2. (A) Intraoperative predicted screw trajectory (red) as visualized on a pre-operative axial

CT. (B) Postoperative actual screw trajectory (red) as visualized on a multiplanar reformatted post-operative CT. Axial

distances (d) were measured at 90° relative to mid-sagittal axis (green line). Angle (Ø) represents corresponding

trajectory angles. (C) Intra-operative predicted screw trajectory (red) as visualized on a pre-operative sagittal CT. (D)

Post-operative actual screw trajectory (red) as visualized on a multiplanar reformatted post-operative CT. Sagittal

distances (d) were measured at 90° relative to the inferior or superior endplate (green line). Angle (Ø) represents

corresponding trajectory angles. Errors in each plane were calculated as d1-d (translational) and Ø1-Ø (angular).

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For absolute navigation application accuracy, translational and angular deviation in the axial and

sagittal planes in the benchmark spine cohort were compared with the corresponding deviations

in the OTI navigation system cohort, in Phases II + III of human clinical trials. Statistical

analysis was not performed on the lead-in phase as the experimental navigation system was not

used for guidance. The distributions of translational and angular deviations were assessed and, as

an assumption of normality was not met, were compared using the non-parametric Kruskal-

Wallis one-way analysis of variance (ANOVA) test and visualized on Bland-Altman plots.

Predictors of increased navigation application error were identified and tested using a

generalized linear model, with age, gender, screw location, and guidance method (navigation,

fluoroscopy, or freehand) considered as covariates. For clinicoradiographic grading, Heary

grades were assessed for inter-rater reliability using the intra-class correlation coefficient, and

reliability of agreement measured using the Fleiss’ kappa test. A p-value of <0.05 was

considered statistically significant for all tests.

All statistical analyses were performed in SPSS Statistics (version 21; IBM, Chicago, IL, USA).

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5.4 Results

5.4.1 Pre-Clinical Validation

Ex-vivo feasibility of our experimental optical navigation technology was studied in 6 adult

human cadavers, resulting in the integrated design of navigation with surgical lighting. In- vivo

proof-of-principle validation of OTI was performed on 10 anaesthetized ventilated adult swine

models, where interference between optical illumination for surgical lighting and OTI, both in

the visible spectrum, were studied and minimized. In porcine testing, optical imaging of

subperiosteal dissection planes between soft and bony tissues, cluttered by bleeding and

carbonization effects from electrocautery using standard surgical techniques, was performed to

demonstrate pre-clinical applicability and refine software to appropriately segment osseous

anatomy and eliminate soft tissue using thresholding algorithms. Porcine testing also established

a specification for 3D imaging speed, of <0.5 seconds to acquire the entire operative field using

standard surgical suction to clear pooling blood.

To study navigation accuracy, 71 thoracic and lumbar pedicle screws were inserted and

quantified by comparing intra-operative trajectory data to true screw placement based on

postoperative CT imaging. Median (95%) translational and angular error of the experimental

navigation system in the adult swine model was 1.7 mm (5.1 mm) and 4.4° (13.0°) in the axial

plane, and 1.6 mm (7.8 mm) and 6.5° (17.8°) in the sagittal plane.

5.4.2 Human Clinical Validation

162 thoracolumbar pedicle screws were placed using OTI guidance in human in-vivo clinical

trials, along with 209 pedicle screws by benchmark CAN systems. In radiographic grading, 5.7%

of all navigated screws using either benchmark or OTI systems were graded as a major breach

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(Heary III-IV), with strong intraclass correlation (ICC 0.725; p < 0.001) and fair inter-rater

agreement (Fleiss’ Kappa (95% CI) 0.248 (0.243 – 0.254); p < 0.0001). There were no

significant differences in major screw breaches rates between OTI and combined benchmark

systems (6.8% vs. 5.3%, p = 0.99). No screws required revision either intra-operatively or

immediately post-operatively during the hospital course, and there were no neurovascular

complications from any instrumentation.

With respect to quantitative navigation application accuracy, median (95%) translational and

angular errors for benchmark systems were 1.1 mm (3.9 mm) and 2.4° (9.0°) in the axial plane,

and 0.8 mm (3.6 mm) and 2.6° (10.1°) in the sagittal plane. For OTI guided screws, median

(95%) translational and angular errors of 1.2 mm (3.4 mm) and 2.2° (8.1°) in the axial plane, and

1.1 mm (4.2 mm) and 2.3° (8.6°) in the sagittal plane, were obtained (Figure 5-6).

In early human clinical testing, in Phase I and the beginning of Phase II, relative displacement

drift between the stereo cameras over time was found to degrade navigation accuracy during the

validation phase, aggravated by the larger thermal expansion coefficients of the 3D printed

plastic material used in the prototype OTI system. An active calibration protocol was therefore

developed to account for camera drift in real-time, implemented after 50 screws had been placed

using OTI guidance. Without active calibration for spinal screw navigation (n=50), the median

(95%) translational and angular errors were 1.3 mm (3.0 mm) and 1.9° (7.6°) in the axial plane,

and 1.6 mm (4.4 mm) and 2.3° (8.2°) in the sagittal plane, vs. 1.0 mm (3.4 mm) and 2.3° (8.1°)

and 0.9 mm (2.6 mm) and 2.5° (7.8°) following implementation of active calibration (n=112).

These differences did not reach statistical significance.

Univariate analysis, accounting for age, gender, surgical navigation method (i.e. benchmark

navigation, experimental OTI navigation) and spine region (cervical, thoracic, lumbar, sacral),

identified age as a predictor for increased axial translational error, and sacral spine region as a

predictor of increased axial angular error vs. the thoracic and lumbar spine. Generalized linear

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regression confirmed sacral screw location as an independent predictor of increased axial angular

error (p = 0.009; Table 5-1) and advanced age as a predictor of increased axial translational error

(p = 0.005).

With respect to temporal efficiency and workflow, for spinal procedures, the time from DRF

fixation to registration completion with the OTI experimental navigation system was a median

(IQR) of 41s (25 – 68), vs. 258s (143 – 355) for paired-point based benchmark CAN (p < 0.001)

and 794s (609 – 1136) for intra-operative CBCT benchmark CAN (p = 0.001) (Figure 5-2D).

This suggests that intra-operative navigation confers significant accuracy benefit compared to

freehand and fluoroscopy techniques, and that the remarkable gains in surgical workflow

facilitated by the experimental OTI system do not come at the expense of surgical accuracy.

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Figure 5-6. Bland-Altman analysis comparing benchmark and OTI navigation accuracy. (Left) Correlation plots

with corresponding boxplots comparing predicted intra-operative screw trajectory with actual post-operative screw

trajectory for benchmark (blue) and experimental (red) navigation systems. (Right) Bland-Altman plots comparing

actual screw trajectory with translational and angular deviations for (A) axial translation, (B) sagittal translation, (C)

axial angle, (D) sagittal angle. No statistically significant differences were found.

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Table 5-1. Navigation error as a function of spine region and navigation technique. Generalized linear model of

navigation error vs. spine region and navigation method. No significant differences between experimental (OTI) and

benchmark techniques were found. (*) denotes significance at < 0.05

Clinical Variables Spine Region and Error

Median (95 percentile)

Navigation Method

Median (95 percentile)

Cervical

N = 10

Thoracic

N = 225

Lumbar

N = 130

Sacral

N = 6

P-Value Benchmark

N = 209

OTI

N = 162

P-Value

Axial Translational

Error (mm)

1.0 (1.7) 1.0 (3.7) 1.5 (3.9) 1.0 (1.9) 0.381 1.1 (3.9) 1.2 (3.4) 0.597

Axial Angular

Error (deg)

2.8 (4.3) 2.1 (8.1) 2.5 (9.5) 6.0 (11.6)* 0.009* 2.4 (9.0) 2.2 (8.1) 0.839

Sagittal

Translational

Error (mm)

1.3 (2.2) 0.9 (1.3) 1.3 (4.1) 0.8 (2.1) 0.437 0.8 (3.6) 1.1 (4.2) 0.214

Sagittal Angular

Error (deg)

1.6 (4.2) 2.6 (9.8) 2.5 (9.5) 3.9 (10.8) 0.485 2.6 (10.1) 2.3 (8.6) 0.492

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5.5 Discussion

This work represents a major shift in the current surgical paradigm through the introduction of

ultra-fast optical topographic imaging and registration. We have demonstrated the

implementation of an optical topographic imaging modality in spinal surgery, with thorough

clinical and engineering data analysis to ensure surgical accuracy. By using optical imaging

based surface point acquisition and GPU based parallel computing processing, we perform

registration of intra-operative anatomy to pre-operative CT imaging at speed orders of magnitude

faster than current paired-point or automatic registration-based navigation systems. The form

factor of our experimental surgical navigation system has been designed to integrate into the

existing operating room environment, with the benefit of performing imaging and registration

tasks considerably faster than existing technologies. We believe these significant innovations

eliminate the workflow restrictions that have traditionally led somes surgeons to forgo navigation

in favour of freehand approaches.

While the utility of the present study is apparent in the context of spinal procedures, the same

optical topographical imaging technology is suitable for a variety of applications. In particular,

rapid optical topographic imaging allows for frequent repeat registrations, minimizing the

significant target registration errors seen with existing neuronavigation technologies as a result of

progressive brain shift during the procedure.(V. M. Pereira et al., 2015) Frameless stereotactic

navigation is also employed routinely in otolaryngology, with growing applications in

orthopedic, abdominal, and craniomaxillofacial procedures.(L.-P. Amiot & Poulin, 2004;

Hassfeld & Mühling, 2001; Okamoto, Onda, Yanaga, Suzuki, & Hattori, 2015; Peterhans et al.,

2011) The utility of optical topographic imaging techniques is evident in these non-neurosurgical

applications, particularly in those with significant soft-tissue manipulation or deformation, where

rapid repeat registration is required to maintain accurate correlation to pre-operative imaging.

High frequency re-acquisition of intra-operative optical images also lends itself well to

augmented reality, with co-registered images overlaid onto operating microscopes or other

displays employed commonly in multiple surgical disciplines.

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The salient findings of our study are, first, that intra-operative navigation based on OTI is

accomplished significantly faster than existing technologies. These differences are particularly

pronounced when compared to newer-generation devices employing intra-operative cone-beam

CT scanning, relative to techniques requiring point-matching registration to pre-operative

imaging (Figure 5-2D). Such benefit was enabled by the efficient GPU algorithm, as

demonstrated by the computation time for optical image acquisition and registration at 5.07±1.83

seconds measured over 476 spinal registrations, each consisting of over 250,000 surface points,

with average throughput of over 52,000 points per second, representing an improvement on the

current clinical paradigm, whereby spatial accuracy is maintained while vastly improving

registration time and workflow. Second, for spinal procedures, absolute translational and angular

accuracy of intraoperative navigation is comparable to benchmark technologies (Figure 5-6).

The accuracy measured in this study is the total surgical application accuracy, encompassing

both the navigation system’s technical accuracy with the surgeon’s ability to utilize 3D

navigation data in placing pedicle screws, to which the ease of use of the surgical instruments,

the surgeon’s experience and anatomical knowledge also contribute.(Guha, Jakubovic, Gupta,

Alotaibi, et al., 2017)

The introduction of active calibration protocol is crucial as it facilitates rapid intra-operative re-

registration without exposing the patient to additional radiation. While the exact cause of camera

drift is unknown, several possible factors have been identified including thermal expansion of the

aluminum and acrylonitrile-butadiene-styrene camera housing due to heat produced by the LED

surgical lights, optical drift occurring within the cameras, and torsion stemming from the

structural design of the experimental system and various screw connection sites. These

inaccuracies, while still present in spine though without significant impact on final application

accuracy, are more prominent in cranial applications based on our preliminary unpublished

experiments, where a significant time lag between registration and screw placement exists,

during which surgical steps such as mechanical drilling, cutting, musculocutaneous flap traction,

and patient movement can all introduce navigation error due to relative displacement between

the patient's cranium and the navigation reference. The active calibration protocol obviates the

need for additional intra-operative imaging while maintaining the required surgical accuracy.

While the current analysis comprises the experience of a number of surgeons, the majority of

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screws navigated with the experimental system were either directly placed or supervised by one

surgeon (VXDY), representing a single-surgeon influence. Larger studies, involving multiple

surgeons, are therefore underway to fully evaluate the evolution of a novice user to a skilled

operator using the experimental navigation system, with multicenter studies representing the

subsequent logical progression.

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5.6 Conclusions

Optical topographic imaging is a novel and rapid technique for image-to-patient registration,

which has been applied here to intra-operative 3D spinal navigation. We demonstrate in pre-

clinical and human clinical testing that OTI is safe, feasible and comparably accurate to current

commercial navigation techniques for the image-guidance of spinal instrumentation. However,

OTI imparts no intra-operative radiation and permits rapid repeat registration, and does so with a

temporal workflow an order of magnitude faster than existing navigation techniques. OTI may

therefore obviate one of the primary roadblocks to routine usage of navigation for spinal

procedures, paving the way for greater adoption among spinal surgeons.

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Chapter 6 Optical Topographic Imaging for Spinal Intra-Operative Three-

Dimensional Navigation in Mini-Open Approaches

Preamble


Guha D, Jakubovic R, Alotaibi NM, Klostranec JM, Saini S, Deorajh R, Gupta S, Fehlings MG,

Mainprize TG, Yee A, Yang VXD. Optical topographic imaging for spinal intra-operative three-

dimensional navigation in mini-open approaches: a prospective cohort study of initial pre-

clinical and clinical feasibility. Manuscript in submission.

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6.1 Abstract

Computer-assisted three-dimensional navigation (CAN) may guide spinal instrumentation.

Optical topographic imaging (OTI) offers comparable accuracy and significantly faster

registration relative to current navigation systems, in open posterior thoracolumbar exposures

(Chapter 5). Here, we aim to validate the utility and accuracy of OTI in minimally-invasive

(MIS) spinal approaches. We conducted a prospective pre-clinical cadaveric and clinical cohort

study. Mini-open midline posterior exposures were performed in four human cadavers. Square

exposures of size 25, 30, 35, and 40mm were registered to preoperative CT imaging. Screw

tracts were fashioned using a tracked awl and probe, and instrumentation placed. Navigation data

were compared to screw positions on postoperative CT imaging, and absolute translational and

angular deviations computed. In-vivo validation was performed in eight patients, with mini-open

thoracolumbar exposures and percutaneous placement of navigated instrumentation. For 37

cadaveric screws, absolute translational errors were (1.8±1.4mm) and (1.8±1.5mm) in the axial

and sagittal planes, respectively; absolute angular deviations were (3.8±2.9) and (3.4±2.8),

respectively (mean±SD). The number of surface points registered by the navigation system, but

not exposure size, correlated positively with the likelihood of successful registration (OR=1.02,

95%-CI 1.009-1.024, p<0.001). 55 in-vivo thoracolumbar pedicle screws were analyzed. Overall

(mean±SD) axial and sagittal translational errors were (1.8±1.4 mm) and (2.7±2.3 mm), while

axial and sagittal angular errors were (3.6±2.9) and (4.6±3.4), respectively. There were no

radiographic breaches >2mm, nor any neurovascular complications. We conclude that OTI has

comparable accuracy for mini-open MIS exposures. The likelihood of successful registration is

affected more by the geometry of the exposure than its size.

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6.2 Introduction

Intra-operative three-dimensional computer-assisted navigation (CAN) has become standard-of-

care in cranial neurosurgery for the localization of subsurface anatomy. Spinal CAN may guide

instrumentation placement and tissue resection, however adoption has been limited by

cumbersome and lengthy registration protocols, workflow hindrances, steep learning curves and

high costs.(Choo et al., 2008; Hartl et al., 2013; Hecht et al., 2015; Rivkin & Yocom, 2014;

Ryang et al., 2015; Wood & McMillen, 2014)

The utility of CAN is most apparent in minimally-invasive surgery (MIS) and deformity-

correcting procedures, where anatomic landmarks are not directly visible or are significantly

distorted.(Bandiera et al., 2013; Choo et al., 2008; Hartl et al., 2013; Sakai et al., 2008) MIS

techniques, through mini-open, tubular and/or endoscopic approaches, have been shown to

shorten hospital length-of-stay, minimize intra-operative blood loss and improve short-term

patient-reported outcomes, with indeterminate impact on operative time and post-operative

complications, relative to comparable open spinal procedures.(Al-Khouja et al., 2015; Dea et al.,

2015; Goldstein, Macwan, Sundararajan, & Rampersaud, 2016; Hu, Tang, Wu, Zhang, & Ke,

2016; McAnany et al., 2015; Phan & Mobbs, 2016) However, MIS approaches have typically

been guided by intra-operative fluoroscopy or computed tomography. These techniques are

associated with substantial radiation exposure and workflow disruption (Chapter 2).(Francesco

Costa et al., 2016)

Optical topographic imaging (OTI) is a novel technique for 3D surface acquisition, patient-to-

image registration and intra-operative navigation, developed by our research group. OTI registers

significantly faster than current CAN systems with comparable accuracy and without intra-

operative radiation exposure (Chapter 5). This technology obviates many of the limitations of

current CAN techniques.(Choo et al., 2008; Hartl et al., 2013) In its current iteration, OTI

requires line-of-sight to exposed bony anatomy, to allow machine-vision cameras to generate a

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virtual 3D surface for patient-to-image registration. To date, OTI has been validated only in open

posterior thoracolumbar approaches (Chapter 5).

Here, we assess the ability of OTI to perform successful patient-to-image registration and

accurate intra-operative navigation in mini-open spinal procedures. We explore predictors of

successful registration and their correlation with quantitative navigation accuracy.

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6.3 Methods

Reporting of all methodology is performed in accordance with the criteria for STrengthening the

Reporting of OBservational studies in Epidemiology (STROBE – www.strobe-statement.org).


Pre-clinical validation was performed in four human cadavers. All cadavers underwent pre- and

post-operative helical CT imaging at 0.5mm slice thickness. Institutional ethics board approval

was obtained (Mount Sinai Hospital REB# 16-0051-E).

For human in-vivo clinical testing, eight patients without history of prior spinal surgery were

enrolled in an ongoing trial of OTI navigation at Sunnybrook Health Sciences Centre (REB#

309-2014 and 086-2015). All patients underwent pre- and post-operative helical CT imaging,

reformatted at 0.625mm slice thickness. To demonstrate non-inferiority of OTI-MIS vs. open

OTI navigation, given standard deviations of 0.6 mm and 1.0° for translational and angular error,

respectively (Chapter 5), and a non-inferiority limit difference of 0.5 mm and 1.0°, respectively,

at α = 0.05 and β = 0.20 (power = 0.80), a minimum sample size of 18 screws is required. All

sample size calculations were performed in R (Version 3.4.1; R Project for Statistical

Computing).

6.3.2 Surgical Technique

Cadavers were placed prone on a standard operating table, with head fixed in a Mayfield clamp.

Mini-open midline posterior exposures of the spinous process and medial bilateral hemilaminae

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were performed at T2, T6, T10 and L3 (Figure 6-1). A bladed self-retaining retractor was placed.

All cadaveric procedures were performed by a single surgeon (DG).

In human in-vivo clinical testing, posterior instrumented fusions were performed for stabilization

following either trauma, or decompression of bony and epidural tumor. All patients were

positioned prone on a Wilson frame. Mini-open midline exposures were performed for OTI

registration as well as laminectomy (Figure 6-2). All in-vivo procedures were performed by a

single surgeon (VXDY), with trainee assistance.

Figure 6-1. Cadaveric mini-open exposure. Representative cadaveric mini-open posterior midline exposure at T2.

Dynamic reference frame for OTI navigation is clamped to the T2 spinous process. Exposure size of 25x25 mm has

been simulated with sterile towels.

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Figure 6-2. In-vivo human clinical mini-open exposures. Mini-open posterior midline exposure at T8-9 for Patient

A (A), T10-11 for Patient B (B), and L2 for Patient C (C) in clinical in-vivo validation. Dynamic reference frame

(arrowhead) is clamped to an exposed spinous process in (A), (B) and (C). A tracked drill guide (arrow) and K-wires

were used for percutaneous placement of instrumentation (D).

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6.3.3 Registration and Intra-Operative Navigation

In cadaveric studies, the retractor width was increased serially to create square exposures of size

25x25, 30x30, 35x35, and 40x40 mm (Figure 6-1). At each level, the exposed anatomy at each

exposure size was registered to pre-operative CT using OTI. Technical details of OTI registration

are described fully in Chapter 5. Briefly, a structured-light pattern is projected onto the exposed

anatomy and recorded by stereoscopic cameras to reconstruct a 3D surface (Figure 6-3). This is

automatically aligned to pre-operative CT imaging using a registration algorithm in real-time.

Registration accuracy was verified manually by placing an optically-tracked awl on bony

landmarks and assessing correlation to the navigation display. Registration was deemed

successful if the OTI system captured sufficient anatomy for patient-to-image registration (≥100

surface points), and if manual verification by the operator demonstrated acceptable accuracy

using identifiable anatomic landmarks with visual and tactile feedback. The number and location

of surface points used by the OTI system for registration were also recorded. At each level, the

30x30 mm exposure was used to place instrumentation. A tracked awl and gearshift probe were

used to fashion pedicle screw tracts at each registered level. Cortical trajectory tracts were also

fashioned at L3. Titanium screws were placed at each level.

In human in-vivo studies, midline mini-open exposures were used for OTI registration, with

similar registration verification as in cadaveric specimens, especially using tactile feedback from

a tracked awl percutaneously on bony landmarks. Screw tracts were then fashioned

percutaneously using the tracked awl, gearshift probe and/or drill-guide, followed by placement

of Kirschner wires. Appropriately-sized cannulated titanium pedicle screws were placed

percutaneously over the Kirschner wires using a standard untracked screwdriver (Figure 6-2D).

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Figure 6-3. Prototype OTI configuration. Computer-assisted design model of OTI navigation unit integrated into

surgical light head. Structured-light projector (arrow), stereoscopic cameras for 3D surface mapping (arrowheads)

and infrared cameras for tool tracking (*) are shown.

6.3.4 Evaluation of Navigation Accuracy

Absolute quantitative navigation accuracy was measured by comparing the final screw position,

on post-operative CT imaging, to a screenshot of the planned screw trajectory on the navigation

system intra-operatively. Translational and angular deviations from the planned entry point and

trajectory were quantified, in the axial and sagittal planes, using multiplanar reformatting of both

pre- and post-operative CT imaging. The method of absolute navigation error quantification has

been described in Chapter 5 (Figure 5-5).

Radiographic accuracies of all in-vivo screws were graded independently by two radiologists

(JMK, SS), using both the 2mm and Heary classifications.(Heary et al., 2004; W. Zhang et al.,

2016) Screws were dichotomized as acceptable (2mm grade ≤2; Heary grade ≤2) or poor (2mm

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grade >2; Heary grade >2) per convention.(Guha, Jakubovic, Gupta, Alotaibi, et al., 2017; W.

Zhang et al., 2016)

All image processing and measurements were performed using an OsiriX 64-bit workstation

(version 10.9.5; PIXMEO SARL. Geneva, Switzerland).


Differences in absolute navigation errors between spinal levels were quantified with one-way

ANOVA, with Tukey’s Honest-Significant-Difference test for post-hoc comparisons. Correlation

between the likelihood of successful registration, and the number of surface points used for

patient-to-image registration as well as the size of the exposed anatomy, were performed using

multiple logistic regression models. Hierarchical mixed-effects general linear modelling was

employed to adjust for second-order differences between cadavers/patients, where required based

on univariate analyses. In-vivo MIS cases were matched 1:2 based on age/gender/spinal level,

and separately based on mean pedicle diameter, to patients who had undergone open

thoracolumbar instrumentation using OTI guidance in our prior trial (Chapter 5). Significance

levels for all tests were set at < 0.05.

All statistical analyses were performed in SPSS Statistics (version 21; IBM. Chicago, IL, USA).

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6.4 Results

For the four cadavers used in pre-clinical validation, mean age at death was 91.4 years (range 83-

96). 37 screws from the four cadavers were included in our analysis: 8 pedicle screws at T2, 10

at T6, 9 at T10, and 4 pedicle and 6 cortical screws at L3.

In-vivo clinical feasibility was assessed in eight patients, with mean age 57.2 years. 55

thoracolumbar pedicle screws placed with CAN from mini-open OTI registrations were

analyzed, bilaterally at T8-9 for Patient A, T6-11 for Patient B, T12-L4 for Patient C, T12-L1 for

Patient D, T11-L3 for Patient E, T10-L2 for Patients F and G, and T9-11 for Patient H. Mean (±

SD) pedicle diameter was 4.4 ± 3.5 mm.

6.4.1 Image-to-Patient Registration

In the cadaveric study, we systematically studied the attributes of successful registrations with

OTI and compared them to unsuccessful registrations. A total of 131 registration attempts were

made through mini-open exposures of varying sizes, with 71.8% verified by the operator as

successful based on correlation between imaging and anatomic bony landmarks (Table 6-1). The

likelihood of successful registration was greater at T2 than at any other tested spinal level

(OR=6.02, 95%-CI 1.47-24.63, p=0.013). The minimum tested exposure of 25x25 mm allowed

successful registration in 66.7% of attempts at T2, 80.0% of attempts at T6, 33.3% of attempts at

T10, and 25.0% of attempts at L3. Successful registration in more than 70% of attempts

necessitated a minimum exposure of 25x25 mm at T6, 30x30 mm at T2 and T10, and 35x35 mm

at L3. The mean wound depths at T2 (5.89 cm) and L3 (5.68 cm) were significantly greater than

at T6 (3.23 cm) or T10 (3.50 cm) (p<0.001), however wound depth did not correlate with the

likelihood of successful registration.

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Overall, (431 ± 235) surface points (mean ± SD) were used by the OTI system for patient-to-

image registration, (502 ± 231) points for successful registrations and (250 ± 120) for

unsuccessful registrations. Significantly fewer points were acquired and used by the system at

the smallest exposure of 25x25 mm (mean 303 points, p=0.039), with no significant differences

in the number of points registered at 30x30, 35x35 or 40x40 mm exposures (Figure 6-4).

In multiple logistic regression modelling, the number of surface points registered by the OTI

system correlated positively with the likelihood of successful registration, independent of spinal

level, exposure size and wound depth (OR=1.02, 95%-CI 1.009-1.024, p<0.001).

In human clinical testing, 9 registrations through MIS exposures were performed, 2 in Patient B

for the placement of T6-8 and T9-11 screws, respectively, and 1 registration each for all other

patients. All registrations were successful on the first attempt, using the representative exposures

demonstrated in Figure 6-2.


In cadaveric testing, overall (mean ± SD) axial and sagittal translational errors were (1.8 ± 1.4

mm) and (1.8 ± 1.5 mm), while axial and sagittal angular errors were (3.8 ± 2.9) and (3.4 ±

2.8), respectively. There were no significant differences in errors between levels, nor between

pedicle and cortical trajectory screws (Figure 6-5). The number of points registered by OTI did

not significantly correlate with any metric of absolute navigation error.

From in-vivo testing, overall (mean ± SD) axial and sagittal translational errors were (1.8 ± 1.4


3.4), respectively (Figure 6-6). In univariate analyses, there were no statistically-significant

141

differences in absolute navigation errors between cadaveric and clinical studies. MIS screws

showed increased quantitative error, relative to matched open thoracolumbar controls, in axial

translation (1.8 ± 1.4 mm vs. 1.0 ± 0.9 mm, p=0.004), axial angle (3.6 ± 2.9 vs. 2.7 ± 2.1,

p=0.032), sagittal translation (2.7 ± 2.3 mm vs. 1.0 ± 0.9 mm, p<0.001), and sagittal angle (4.6 ±

3.4 vs. 2.8 ± 2.3, p=0.006). These differences persisted when matching was performed by

pedicle diameter rather than age/gender/spinal level. However, in general linear modelling

including distance from the registered level as a covariate, there were no significant differences

in any quantitative errors between MIS and open thoracolumbar cases. All in-vivo screws were

placed 0, 1 or 2 vertebral levels from the registered level. Increasing distance between the

instrumented and registered levels correlated positively with increased axial translational error

(Pearson correlation coefficient 0.534, p = 0.007).

Table 6-1. Characteristics of cadaveric OTI registrations through mini-open exposures.

Level Successful registrations (%

of total)

Number of registered

points (mean ± SD)

T2 85.7% 355 ± 152

T6 82.1% 608 ± 288

T10 67.6% 440 ± 234

L3 58.5% 353 ± 173

Overall 71.8% 431 ± 235

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Figure 6-4. Correlation of registered points to exposure size and spinal level. Standard boxplots demonstrating

the number of surface points registered by OTI stratified by exposure size (A), and by registered level (B), in

cadaveric testing. Boxes represent the first, median and third quartiles. Whiskers represent 1.5x the interquartile

range. * represents significance at p<0.05.

Figure 6-5. Navigation application accuracy, by spinal level, in cadaveric testing. Standard boxplots

demonstrating the absolute translational (A) and angular (B) navigation errors in the axial and sagittal planes,

stratified by registered level and screw trajectory, in cadaveric testing. Boxes represent the first, median and third

quartiles. Whiskers represent 1.5x the interquartile range.

143

Figure 6-6. Navigation application accuracy, by spinal level, in clinical testing. Standard boxplot demonstrating

the absolute translational (A) and angular (B) navigation errors in the axial and sagittal planes, in clinical in-vivo MIS

testing. Boxes represent the first, median and third quartiles. Whiskers represent 1.5x the interquartile range.

6.4.3 Radiographic Navigation Accuracy

From two independent raters, an average of 94.5% of in-vivo screws were rated as acceptable on

the 2mm grade, and 100% rated acceptable by the Heary classification. Three screws were rated

as poor (Grade >2) on the 2mm classification by one or both raters. All three screws were placed

in lumbar vertebrae (L3 or L4) intentionally with a more lateral starting point, at the junction of

the transverse process and superior articular process (Figure 6-7). This is a well-documented

technique allowing docking of the awl against the transverse process/facet junction for tactile

feedback in a percutaneous procedure, to reduce the profile of the screw heads, and to avoid

damaging the superior facet capsule.(Wong et al., 2014)

There were no critical radiographic breaches, and no neurovascular or other clinical

complications from any in-vivo instrumentation.

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Figure 6-7. Representative intentional placement of a poorly-graded screw. Axial (A) and sagittal (B)

multiplanar-reformatted CT imaging demonstrating a percutaneously-inserted right L4 screw, with starting point

intentionally at the junction of the transverse process and superior articular process, and graded as ‘poor’ by the 2mm

classification.

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6.5 Discussion

The primary purported benefit of CAN for spinal procedures is improved instrumentation

accuracy and, in theory, minimization of acute and long-term complications from misplaced

screws. CAN has been shown to reduce pedicle screw breach rates from 12-40%, under freehand

or fluoroscopic guidance, to under 5% with 3D CAN.(L. P. Amiot et al., 2000; M Bydon et al.,

2014; Castro et al., 1996; E W Nottmeier, Seemer, & Young, 2009; B. J. Shin et al., 2012; Y

Wang et al., 2013) Improved instrumentation accuracy is seen across all 3D CAN techniques,

registering to pre- or intra-operative imaging, in each of the cervical, thoracic, lumbar and sacral

regions.(Barsa et al., 2016; Austin C Bourgeois et al., 2015; Hecht et al., 2010; Mason et al.,

2014; N. F. Tian et al., 2011)

Workflow disturbances continue to limit the usage of CAN among spinal surgeons, though the

technology has been adopted more readily by specialists in MIS and complex deformity surgery,

where bony landmarks may not be readily identifiable. While OTI has been validated previously

as a comparably accurate yet significantly faster technique of intra-operative navigation relative

to current CAN systems, its utility in mini-open procedures, with limited line-of-sight to exposed

anatomy, has been unproven to date.

Our group has previously quantified absolute navigation accuracy, for the first time, in current

CAN techniques registered to pre- or intra-operative imaging, as well as in OTI for open

thoracolumbar exposures.(Jakubovic et al., 2016) In our current analysis, while not reaching

statistical significance, there was a trend towards increased translational and angular errors for

MIS exposures in clinical testing relative to pre-clinical cadaveric validation. This is likely due

in part to the placement of in-vivo screws percutaneously over K-wires, but without a navigated

screwdriver, which may have led to deviation of the final screw placement from the original

navigated tract. Furthermore, in-vivo screws were placed percutaneously at up to 2 levels distant

from the registered level, potentially introducing error due to intersegmental mobility.

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Corroborating this theory, absolute translational and angular errors for in-vivo MIS exposures

were slightly greater, in both axial and sagittal planes, than those obtained using OTI for open

thoracolumbar exposures, but not significantly once distance from the registered level was

accounted for in general linear modelling. The literature on the impact of non-segmental

registration on navigation accuracy is heterogeneous, both in outcomes as well as in the metrics

used to quantify navigation accuracy.(Elias C Papadopoulos, Girardi, Sama, Sandhu, &

Cammisa; Scheufler et al., 2011b; Shimizu et al., 2014) The quantitative translational accuracy

of OTI for MIS remains within 2-3mm, comparing favorably to the accuracy of current

commercial CAN systems. Moreover, the radiographic accuracy of screws placed following

MIS-OTI registrations was 100% by the Heary classification, with no clinical complications. The

slightly greater quantitative inaccuracy in MIS vs. open OTI procedures is likely related to the

percutaneous placement of screws rather than the registration itself, as the lack of visual

anatomic feedback and the unavailability of a tracked screwdriver, with an untapped screw tract,

allow for increased error in screw placement relative to the intended navigation-guided

trajectory.

The ability of OTI to perform patient-to-image registration is contingent on the acquisition of

sufficient exposed points that can be correlated, using an iterative closest-point algorithm, to

corresponding points on a pre-operative image set. As this is most readily performed on bony

anatomy, standard midline mini-open exposures were chosen for this initial demonstration of

feasibility for MIS approaches. Cadaveric registrations were performed in the upper, middle and

lower thoracic spine as well as lumbar spine, where the bulk of current MIS procedures are

performed.(Banczerowski et al., 2015; Z. A. Smith & Fessler, 2012) Pedicle screws were

inserted at all levels. Concurrent cortical-trajectory screws were placed at L3, as cortical screws

are commonly placed in MIS midline decompression and fusion procedures to achieve greater

bony purchase with minimal muscle dissection and soft-tissue retraction.(Phan, Hogan, Maharaj,

& Mobbs, 2015; Wray et al., 2015)

We found that the number of surface points acquired and registered by the navigation system

correlated positively with the likelihood of successful registration. The first quartile of registered

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points for successful registrations and the third quartile for unsuccessful registrations converged

at approximately 325 points. The number of registered points did not, however, correlate with

any absolute navigation error. In this iteration of OTI, 325 registered points should therefore be

targeted as the minimum for successful registration through an MIS exposure, with more points

increasing the likelihood of successful registration but not final navigation accuracy.

While the number of surface points registered by OTI was correlated with the likelihood of

successful registration, the size of the exposure itself was not an independent predictor of

registration success. Therefore, while smaller exposures are considered the definition of

‘minimally-invasive’, it is the quality of the exposed anatomy rather than the size itself which

most affects the likelihood of successful registration with OTI. Regions with more geometric

variability, and hence a greater number of unique points that may be used for patient-to-image

registration, are more likely to be registered successfully even with a smaller skin opening, than

regions with geometric homogeneity. For instance, to achieve a minimum 70% likelihood of

registration success, a minimum 35x35 mm exposure was required in the lumbar spine, while

25x25 mm and 30x30 mm exposures were sufficient in the thoracic spine. This may be due in

part to geometric symmetry in the medial lumbar hemilaminae, and in part to the increased depth

of lumbar surgical cavities, resulting in increased shadowing and fewer captured points for an

optically-based acquisition system. The latter represents a known technical challenge with OTI,

one that is readily rectifiable with modified camera and projector alignments.

There are multiple limitations to our analysis. The armamentarium of MIS spinal surgeons

includes tubular ports and retractors, which were unavailable in the cadaveric study for

systematic testing of registration success. Percutaneous placement of instrumentation is

performed best with a tracked screwdriver to ensure no deviation from the navigated screw tract.

Future studies of OTI for MIS applications should include percutaneous placement of

instrumentation distant from the level of registration and reference-frame fixation, while

accounting for the additional sources of navigation error arising from non-segmental registration.

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6.6 Conclusions

Optical machine-vision is a novel navigation technique previously validated for open posterior

exposures. OTI is feasible for mini-open MIS exposures in pre-clinical and initial clinical testing,

with comparable radiographic accuracy to that achieved by OTI in open exposures. The

likelihood of successful registration is dependent on the number of points acquired and registered

by the navigation system, but not exposure size. With the exception of sagittal angular deviation,

absolute navigation accuracy is unaffected by the size of the MIS exposure, or by the number of

registered points. Future work exploring the feasibility of OTI registration through tubular

minimal-access approaches is warranted.

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Chapter 7 Optical Topographic Imaging for Spinal Intra-Operative Three-

Dimensional Navigation in the Cervical Spine

Preamble


Guha D, Jakubovic R, Alotaibi NM, Deorajh R, Gupta S, Fehlings MG, Mainprize TG, Yee A,

Yang VXD. Optical topographic imaging for spinal intra-operative three-dimensional navigation

in the cervical spine: initial pre-clinical and clinical feasibility. Manuscript in submission.

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7.1 Abstract

Computer-assisted three-dimensional navigation may guide spinal instrumentation. Optical

topographic imaging (OTI) offers comparable accuracy and significantly faster registration

workflow relative to current navigation systems. It has previously been validated in open

posterior thoracolumbar exposures. Here, we aim to validate the utility and accuracy of OTI in

the cervical spine. We conducted a prospective pre-clinical cadaveric and clinical cohort study,

on 4 human formalin-fixed cadavers, and 15 patients undergoing first-time posterior cervical

decompression and instrumented fusion, guided by intra-operative OTI navigation. In both pre-

clinical and clinical validation, standard midline open posterior cervical exposures were

performed, with segmental OTI registration. In cadaveric testing, a tracked drill guide was used

to cannulate screws tracts in the lateral mass at C1, pars at C2, lateral mass at C3-6, and pedicle

at C7. In clinical testing, translaminar screws at C2 were also analyzed in addition. Navigation

data were compared to screw positions on post-operative CT imaging, and absolute translational

and angular deviations computed. In cadaveric testing, (mean ± SD) axial and sagittal

translational errors were (1.7 ± 1.2 mm) and (2.1 ± 2.2 mm), while axial and sagittal angular

errors were (4.1 ± 3.8) and (7.0 ± 5.4), respectively. In clinical validation, (mean ± SD) axial

and sagittal translational errors were (1.9 ± 1.4 mm) and (1.3 ± 1.0 mm), while axial and sagittal

angular errors were (3.7 ± 2.6) and (3.5 ± 2.9), respectively. There were no radiographic facet,

canal or foraminal violations, nor any neurovascular complications. We conclude that optical

machine-vision is a novel navigation technique allowing efficient initial and repeat registration.

Accuracy even in the more-mobile cervical spine is comparable to current spinal neuronavigation

systems.

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7.2 Introduction

Intra-operative three-dimensional computer-assisted navigation (CAN) has become standard-of-

care in cranial neurosurgery for the localization of subsurface anatomy. CAN in spinal surgery

may guide instrumentation placement as well as bony and soft-tissue resection, however

adoption has been limited by cumbersome and lengthy registration protocols, spatial and

temporal workflow hindrances, steep learning curves and high costs. (Choo et al., 2008; Hartl et

al., 2013; Hecht et al., 2015; Rivkin & Yocom, 2014; Ryang et al., 2015; Wood & McMillen,

2014)

In current practice, the utility of CAN is most apparent in minimally-invasive and deformity-

correcting procedures in the thoracolumbar spine, where anatomic landmarks are not directly

visible or are significantly distorted.(Bandiera et al., 2013; Choo et al., 2008; Hartl et al., 2013;

Sakai et al., 2008) Navigation may play an increasing role in the cervical spine, for the

placement of minimally-invasive instrumentation in the setting of acute trauma, for fixation of

the atlantoaxial spine and craniocervical junction, and for the placement of cervical pedicle

screws which are biomechanically superior to lateral mass fixation.(Komatsubara et al., 2016;

Shimokawa & Takami, 2016a; J. D. Smith et al., 2016) However, the cervical spine is inherently

more mobile than the thoracolumbar spine, with narrower pedicles and tighter tolerances, hence

navigation inaccuracy due to intersegmental mobility is of significant concern with most current

CAN systems. Unsurprisingly, the reported radiographic accuracy of current 3D-CAN systems is

typically lower in the cervical than in the thoracolumbar spine.(Mason et al., 2014; N. F. Tian et

al., 2011)

Optical topographic imaging (OTI) is a novel technique for 3D surface anatomy acquisition,

patient-to-image registration and intra-operative navigation, developed by our research group.

OTI registers significantly faster than current CAN systems, without intra-operative radiation

exposure and with comparable accuracy in open thoracolumbar approaches (Chapter 5). This

152

technology obviates many of the limitations of current CAN techniques.(Choo et al., 2008; Hartl

et al., 2013) Here, we assess the ability of OTI to perform successful patient-to-image

registration and accurate intra-operative navigation in the mobile cervical spine, in pre-clinical

cadaveric models and in initial human clinical testing.

7.3 Methods

Reporting of all methodology is performed in accordance with the criteria for STrengthening the

Reporting of OBservational studies in Epidemiology (STROBE – www.strobe-statement.org).


Pre-clinical validation was performed in four human cadavers. All cadavers underwent pre- and

post- operative helical CT imaging at 0.5mm slice thickness. Institutional ethics board approval

was obtained (IRB# 16-0051-E).

In human in-vivo clinical validation, 15 patients without history of prior spinal surgery were

enrolled in an ongoing prospective trial of OTI navigation at Sunnybrook Health Sciences Centre

(IRB# 309-2014 and 086-2015). All patients underwent pre- and post- operative helical CT

imaging, reformatted at 0.625mm slice thickness. To demonstrate non-inferiority of OTI for

open cervical vs. thoracolumbar approaches, given standard deviations of 0.6 mm and 1.0° for

thoracolumbar translational and angular error, respectively (Chapter 5), and a non-inferiority

limit difference of 0.5 mm and 1.0°, respectively, at α = 0.05 and β = 0.20 (power = 0.80), a

minimum sample size of 18 screws is required. All sample size calculations were performed in R

(Version 3.4.1; R Project for Statistical Computing).

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7.3.2 Surgical Technique

Cadavers were positioned prone on a standard operating table, with head fixed in a Mayfield

clamp. Standard midline posterior exposures were performed from occiput to the cervicothoracic

junction, with exposure of the medial 1.5 cm of the posterior arch of C1, and the entire lateral

masses of C3-7. All cadaveric procedures were performed by a single surgeon (DG).

In human in-vivo testing, all patients underwent open posterior cervical instrumented fusion for

traumatic or degenerative pathologies. All patients were positioned prone on a Wilson frame,

with head fixed in a Mayfield clamp. Standard midline open posterior exposures were used for

all cases. In-vivo procedures were performed by a single surgeon (VXDY), with trainee

assistance.

7.3.3 Registration and Intra-Operative Navigation

In cadaveric testing, the exposed anatomy at each level was individually registered to pre-

operative CT using OTI, with the dynamic reference frame (DRF) clamped to the spinous

process of the registered level. Registration at C1 was performed segmentally, but with the DRF

clamped on C2 due to the lack of a C1 spinous process. Technical details of OTI registration are

described separately.(Jakubovic et al., 2016) Briefly, a structured-light pattern is projected onto

the exposed anatomy and recorded by stereoscopic visible-band cameras to reconstruct a 3D

surface point cloud. This is automatically aligned to pre-operative CT imaging using a

segmentation and registration algorithm in real-time.

Registration accuracy was verified manually by placing an optically-tracked awl on bony

landmarks and assessing correlation to the navigation display. Registration was deemed

154

successful if the OTI system captured sufficient anatomy for patient-to-image registration (≥100

surface points), and if manual verification by the operator demonstrated acceptable accuracy

with visual and tactile feedback.

An optically-tracked drill guide (Medtronic Sofamor Danek; Memphis, TN, USA) was used with

OTI navigation to fashion pilot holes for screw tracts in the C1 lateral mass, C2 pars, and C3-6

lateral mass (Figure 7-1). A tracked awl and gearshift probe were used to fashion tracts for C7

pedicle screws. Appropriately-sized titanium instrumentation was then placed using an untracked

screwdriver. Screw holes were not tapped.

In human in-vivo validation, segmental OTI registration, tract cannulation, and instrumentation

placement were performed similar to cadaveric testing.

Figure 7-1. Tracked cervical drill guide navigated with OTI. (A) Tracked drill guide used to cannulate all cervical

screw tracts with OTI navigation guidance. (B) OTI navigation (rectangle) integrated into surgical light head, guiding

placement of a C4 lateral mass screw.

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7.3.4 Evaluation of Navigation Accuracy

Absolute quantitative navigation accuracy was measured by comparing the final screw position,

on post-operative CT, to a screenshot of the planned screw trajectory on the navigation system

intra-operatively. Translational and angular deviations from the planned entry point and

trajectory were quantified, in the axial and sagittal planes, using multiplanar reformatting of both

pre- and post- operative CT imaging. The method of absolute navigation error quantification has

been described by our group previously in Chapters 5 and 6, and adapted for use in the cervical

spine (Figure 7-2).(Guha, Jakubovic, Gupta, Alotaibi, et al., 2017; Y. Kotani et al., 2007;

Mathew et al., 2013)

Radiographic accuracy of all in-vivo screws were graded using the 2mm classification of Neo et

al..(Neo, Sakamoto, & Fujibayashi, 2005) Screws were dichotomized as acceptable (deviation

≤2mm) or unacceptable (deviation >2mm) per convention.

All image processing and measurements were performed using an OsiriX 64-bit workstation

(version 10.9.5; PIXMEO SARL. Geneva, Switzerland).

156

Figure 7-2. Quantification of absolute navigation application accuracy. Measurement of absolute navigation

accuracy, in the axial (A+B) and sagittal (C+D) planes. Comparison is made between intra-operative navigation

screenshots of planned entry points and trajectories (A+C), to final screw placement on post-operative CT (B+D).

Reference lines (dashed) are drawn, in the axial plane in the mid-sagittal line, and in the sagittal plane along the

superior endplate. Translational error is computed as (d1-d); angular error is computed as (1-).

157


Differences in absolute navigation errors between spinal levels, and between the cervical cohort

in this study and the thoracolumbar cohort from our prior trial of OTI, were quantified with one-

way ANOVA with Tukey’s Honest-Significant-Difference test for post-hoc comparisons.

Correlation between radiographic cervical spondylosis, based on Kellgren grade (Table 7-1), and

navigation errors were assessed using general linear modelling.(Ofiram et al., 2009) Hierarchical

mixed-effects general linear modelling was employed to adjust for second-order differences

between cadavers/patients, where required based on univariate analyses. In-vivo cases were

matched 1:1 based on age and gender, to patients who had undergone open thoracolumbar

instrumentation using OTI guidance in our prior trial.(Jakubovic et al., 2016) Significance levels

for all tests were set at < 0.05.


Table 7-1. Kellgren classification of radiographic cervical spondylosis.

Grade Definition

0 No anterior osteophytes

0-25% disc space narrowing

No endplate sclerosis

No olisthesis

1 Minimal anterior osteophyte formation (<2mm)


Minimal endplate sclerosis

Olisthesis <3mm

2 Moderate anterior osteophyte formation (2-4mm)


Moderate endplate sclerosis

Olisthesis 3-5mm

3 Large anterior osteophyte formation (>4mm)


Severe endplate sclerosis

Olisthesis >5mm

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7.4 Results

For the four cadavers used in pre-clinical validation, mean age at death was 91.4 years (range 83-

96). No significant cervical deformity was evident in any cadaver, however significant

osteophyte bridging across the lateral masses was seen in two (Kellgren Grade 3). 53 screws

from 4 cadavers were included in our analysis, encompassing C1 lateral mass, C2 pars, C3-6

lateral mass and C7 pedicle screws (Table 7-2).

In-vivo clinical feasibility was assessed in 15 patients, with mean age 61.1 years (range 34-78).

74 cervical screws placed with OTI guidance were analyzed, lateral mass at C1, pars and

translaminar at C2, lateral mass at C3-6, and pedicle at C7 (Table 7-2).

Table 7-2. Number of screws in cadaveric and clinical testing, by level and Kellgren grade.

Abbreviations: LM – lateral mass; TL – translaminar

Level

C1 LM C2 pars C2 TL C3-6 LM C7 pedicle TOTAL

Cadaveric

Kellgren Grade 0 0 0 0 0 0

1 2 4 0 16 4

2 0 0 0 0 0

3 3 4 0 16 4

5 8 0 32 8 53

In-vivo

Kellgren Grade 0 0 0 0 5 0

1 0 6 0 19 5

2 0 2 2 12 4

3 0 0 0 19 0

0 8 2 55 9 74

5 16 2 87 17 127

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In pre-clinical cadaveric testing, overall (mean ± SD) axial and sagittal translational errors were

(1.7 ± 1.2 mm) and (2.1 ± 2.2 mm), while axial and sagittal angular errors were (4.1 ± 3.8) and

(7.0 ± 5.4), respectively (Figure 7-3). There were no significant differences in errors between

levels, nor any correlation between radiographic cervical degeneration, based on Kellgren

classification, and any metric of absolute navigation error.

In clinical validation, overall (mean ± SD) axial and sagittal translational errors were (1.9 ± 1.4


2.9), respectively (Figure 7-4). There were no significant differences in errors between levels,

nor correlation with radiographic cervical spondylosis. In univariate analyses, sagittal

translational and angular error were significantly greater in cadaveric than in-vivo testing

(p<0.001). There were no differences in any error metric between matched cervical cases and

open thoracolumbar controls.

7.4.2 Radiographic Navigation Accuracy

No radiographic breach was observed in any cadaveric or in-vivo C1, C2 or C7 screws (Neo

Grade 0). There were no unintentional facet violations for any lateral mass screws placed at C3-

6. There were no neurovascular or other clinical sequelae of any placed screws in clinical testing.

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Figure 7-3. Absolute navigation application accuracy in cadaveric testing. Standard boxplots demonstrating the

translational (top) and angular (bottom) absolute navigation errors, in cadaveric testing. Boxes represent the first,

median and third quantiles. Whiskers represent 1.5x the interquartile range.

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Figure 7-4. Absolute navigation application accuracy in clinical testing. Standard boxplots demonstrating the

translational (A) and angular (B) absolute navigation errors, in clinical in-vivo testing. Boxes represent the first,

median and third quantiles. Whiskers represent 1.5x the interquartile range.

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7.5 Discussion

We demonstrate here that application accuracy in the cervical spine of optical topographic

imaging, a novel technique for image-to-patient registration and intra-operative navigation, is

comparable to that in open thoracolumbar procedures and to currently-available navigation

devices.(Jakubovic et al., 2016) The workflow advantages associated with OTI in the

thoracolumbar spine are maintained with comparable accuracy in the cervical spine.

The most frequently cited benefit of CAN for spinal procedures is improved instrumentation

accuracy, and minimization of associated acute and long-term complications from misplaced

hardware. CAN has been shown to reduce pedicle screw breach rates from 12-40%, under

freehand or fluoroscopic guidance, to under 10% with 3D CAN.(L. P. Amiot et al., 2000; M

Bydon et al., 2014; Castro et al., 1996; Eric W Nottmeier et al., 2009; B. J. Shin et al., 2012; Y

Wang et al., 2013) Improved instrumentation accuracy is seen across all 3D CAN techniques,

registering to pre- or intra-operative imaging.(Mason et al., 2014) In one of the most recent meta-

analyses on CAN-guided instrumentation accuracy, 3D-CAN was shown to result in pedicle

screw accuracy of 96.7% in the lumbosacral spine, 93.2% in the thoracic spine, and 90.3% in the

cervical spine, with the finding of reduced CAN accuracy in the cervical spine echoed in an

earlier review.(Mason et al., 2014; N. F. Tian et al., 2011)

The reduced accuracy of cervical CAN in the literature is likely due in part to the focus of

previous publications on navigated subaxial pedicle screws specifically, rather than the more

common lateral mass and C2 pars/translaminar implants. Radiographic accuracy has also been

evaluated purely on ordinal classifications based on some variant of 2mm gradients, which is not

necessarily reflective of quantitative application accuracy.(Guha, Jakubovic, Gupta, Alotaibi, et

al., 2017; Neo, Sakamoto, & Fujibayashi, 2005) In addition, navigation for cervical procedures is

often performed with non-segmental registration. That is, the dynamic reference frame (DRF) is

affixed to either a head clamp or to the cervicothoracic junction, distant from the level being

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instrumented to avoid the DRF obstructing the surgeon’s hands as may often occur with C1/2

instrumentation, where CAN guidance is most useful. As intersegmental mobility is typically

greater in the cervical spine, this practice may lead to increased navigation error.(Tauchi et al.,

2013) In our analysis, we demonstrate equivalent navigation accuracy with OTI in our cervical

cohort and our prior open thoracolumbar cases. This is likely due largely to our practice of

registering each instrumented level segmentally, a technique facilitated by the rapid registration

workflow of OTI, which thereby eliminates error from intersegmental mobility.

Sagittal translational and angular errors in our clinical cohort were significantly lower than those

observed in pre-clinical cadaveric testing. This is likely due in part to the significantly older age

of the cadaveric specimens, with commensurately greater degenerative cervical spondylosis. Half

of the cervical screws in cadaveric testing were placed in severely degenerated spines (Kellgren

Grade 3), whereas only 19 of 74 screws (25.6%) in clinical testing were placed in Kellgren

Grade 3 spines. Facet arthrosis in the more severely degenerated cadaveric cervical spines,

resulting in poorer vertebral segmentation with machine vision, is a likely contributor to the

increased sagittal-plane errors observed in cadaveric testing, however without ultimate

radiographic misplacement. In the setting of severe facet arthrosis, therefore, clinicians should be

cognizant to carefully verify navigation accuracy manually based on correspondence to known

anatomic landmarks.

There are multiple limitations to our analysis. 87 of the 127 screws analyzed in combined

cadaveric and clinical testing were lateral mass implants at C3-6. While it is well-documented

that freehand placement of C3-6 lateral mass instrumentation is safe, obviating the need for

navigation, these screws were analyzed here to quantitatively assess OTI application

accuracy.(H.-S. H.-S. Kim et al., 2014) Future studies of OTI may include larger prospective

cohorts of C1/2 instrumentation.

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7.6 Conclusions

Optical topographic imaging is a novel navigation technique previously validated for open

posterior thoracolumbar exposures. We show here that OTI is feasible and comparably accurate

in open posterior cervical approaches. Accuracy is not dependent on the instrumented spinal

level. Careful manual verification of navigation accuracy should be performed particularly with

severe facet arthrosis, to minimize the likelihood of navigation error.

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Chapter 8 Error Propagation in Spinal Intra-Operative Three-Dimensional

Navigation from Non-Segmental Registration

Preamble


Guha D, Jakubovic R, Gupta S, Fehlings MG, Mainprize TG, Yee A, Yang VXD. Intra-operative

error propagation in three-dimensional spinal navigation from non-segmental registration: a

prospective cadaveric and clinical study. Manuscript in submission.

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8.1 Abstract

Spinal instrumentation may be guided by intra-operative computer-assisted navigation (CAN).

Current systems rely on a dynamic reference frame (DRF) for image-to-patient registration and

tool tracking. Working distant to a DRF may generate inaccuracy from intrinsic limitations of

optical tool tracking, and from intersegmental mobility during surgical manipulation and patient

respiration. Our aim in this study was to quantitate and identify predictors of absolute navigation

error as a function of distance from the registered vertebral level, and from intersegmental

mobility due to surgical manipulation and patient respiration. We conducted a prospective pre-

clinical and clinical cohort study involving 4 human formalin-fixed cadavers, and 10 patients

undergoing first-time posterior cervical/thoracic/lumbar instrumented fusion ± decompression,

guided by intra-operative three-dimensional CAN. Navigation error from working distant to the

level to which the DRF is affixed, and from surgical manipulation, was quantified in four human

cadavers. The 3D position of a tracked tool tip at 0-5 levels from the DRF, and during the

targeting of a pedicle screw tract, was captured in real-time by an optical navigation system.

Respiration-induced vertebral motion was quantified from 10 clinical cases of open posterior

instrumented fusion. The 3D position of a custom spinous-process clamp was tracked over 12

respiratory cycles using an optical navigation system. Data on patient and ventilator parameters

were collected. Absolute quantitative translational navigation error was computed in 3D, and in

each of the component medio-lateral (ML), antero-posterior (AP), and cranio-caudal (CC) axes.

We observed an increase in mean quantitative 3D navigation error of ≥ 2 mm from baseline at ≥2

levels distant from the DRF in the cervical and lumbar spine, due predominantly to increased

error in the AP axis. (Mean ± SD) displacement due to surgical manipulation was 1.6 ± 1.1 mm

in 3D across all levels, ≥ 2 mm in 17.4%, 19.2% and 38.5% of levels in the cervical, thoracic and

lumbar spine, respectively. (Mean ± SD) absolute respiration-induced 3D motion was 2.0 ± 1.3

mm, greatest in the lower thoracic spine (p<0.001). TV and PEEP correlated positively with

increased vertebral displacement. We concluded therefore that vertebral motion is unaccounted

for during image-guided surgery when performed at levels distant from the DRF. Navigating

instrumentation within 2 levels of the DRF is likely to minimize the risk of navigation error.

While respiration- and manipulation-induced vertebral motion is typically small, there is

significant variability in magnitude, particularly with spinal region and ventilator parameters.

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8.2 Introduction

Intra-operative three-dimensional computer-assisted navigation (CAN) in spinal procedures may

guide instrumentation placement as well as bony and soft-tissue resection. Contemporary

navigation systems register patient anatomy to an imaging dataset, allowing real-time instrument

tracking and/or robotic guidance in the virtualized environment. Whether the imaging data is

acquired pre-operatively, as CT or MRI, or intra-operatively, as 2D/3D-fluoroscopy or CT,

current CAN systems rely on a dynamic reference frame (DRF) for maintaining the image-to-

patient registration and tool tracking (Chapter 2). The accuracy of spinal CAN systems has been

studied extensively, and varies by registration and imaging technique as well as spinal

region.(Du et al., 2017; Laudato, Pierzchala, & Schizas, 2017; Mason et al., 2014; N. F. Tian et

al., 2011) Concern over registration accuracy is one of several reasons for the relative lack of

widespread adoption of CAN amongst spinal surgeons.(Choo et al., 2008; Hartl et al., 2013)

Displacement of vertebral levels distant to the DRF may generate navigation inaccuracy from

intersegmental mobility, which is seen to varying extents across the cervical, thoracic and lumbar

spines.(Tauchi et al., 2013) While intersegmental motion due to patient positioning, for instance

between supine pre-operative CT imaging and intra-operative prone positioning, is accounted for

by CAN systems registering to intra-operative imaging, there are multiple sources of intra-

operative post-imaging intersegmental motion. These include patient respiration-induced

vertebral motion, as well as displacement from surgeon manipulation during the placement of

instrumentation.(N. Glossop & Hu, 1997; Liu et al., 2015) In long-segment deformity corrections

and minimally-invasive lumbosacral procedures, or in some cases of posterior cervical

instrumentation, with DRF fixation to the pelvis or Mayfield clamp, respectively, navigation

inaccuracy due to intersegmental mobility can become particularly pronounced. However, the

current literature on the extent and significance of navigation inaccuracy due to intersegmental

mobility is conflicted.(N. Glossop & Hu, 1997; Liu et al., 2015; E C Papadopoulos, Girardi,

Sama, Sandhu, & Cammisa Jr., 2005; Scheufler et al., 2011a, 2011b; Takahashi, Hirabayashi,

Hashidate, Ogihara, & Kato, 2010; Uehara et al., 2017)

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Here, we perform a prospective cadaveric and in-vivo human clinical study to quantify intra-

operative vertebral motion from patient respiration and surgical manipulation, using continuous

tracking enabled by a novel in-house navigation technology based on optical topographic

imaging (OTI), described in greater detail in Chapter 5.

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8.3 Methods


Pre-clinical testing was performed in four formalin-fixed human cadavers. All cadavers

underwent pre- and post-operative helical CT imaging at 0.5mm slice thickness, for registration

using an OTI navigation system. Institutional ethics board approval was obtained (REB# 16-

0051-E).

In-vivo testing was performed in 10 clinical cases of open posterior instrumented fusion, for

degenerative, traumatic or neoplastic etiologies. All patients had no history of prior spinal

surgery at the operated levels. All patients underwent pre-operative helical CT imaging,

reformatted at 0.625mm slice thickness, for registration using an OTI navigation system as part

of an ongoing trial of OTI at Sunnybrook Health Sciences Centre (REB# 309-2014 and 086-

2015).

8.3.2 Quantification of Navigation Error from Proximity to DRF

Navigation error due to working at a level distant from that to which the DRF is affixed, was

assessed in four human cadavers. Cadavers were placed prone on a standard operating table, and

a standard midline posterior exposure performed from C1 to S1. Bone screws were implanted

into the superolateral edges of the laminae at each level as internal fiducials, to approximate the

entry point of typical pedicle screws. The DRF was clamped at various levels in the

cervical/thoracic/lumbar spine, and OTI navigation registered. The tip of a tracked awl was then

placed into the head of the bone screws at 0-5 levels away from that to which the DRF was

affixed. The three-dimensional location of the tool tip as seen by the OTI navigation system was

recorded at each point, and compared using image-processing software to the actual position of

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the center of the bone screw head on post-operative CT imaging. All image processing and

measurements were performed using a 64-bit OsiriX workstation (version 10.9.5; PIXMEO

SARL. Geneva, Switzerland).

8.3.3 Quantification of Navigation Error from Surgical Manipulation

Using the same midline exposures and laminar fiducials in four human cadavers, the tip of a

tracked awl (thoracolumbar) or tracked drill guide (cervical) was placed into the heads of the

bone screws at each level, and pressure exerted with the appropriate force and trajectory to

simulate the creation of pedicle screw tracts. The 3D position of the tracked tool tip prior to and

following the exertion of force was recorded by the OTI navigation system.

8.3.4 Quantification of Navigation Error from Respiration-Induced Motion

Respiration-induced vertebral motion was quantified in-vivo in 10 human patients. Patients were

positioned prone on a Wilson frame, with Mayfield head clamp for cervical fusions. Following

standard midline open posterior exposure, OTI image-to-patient registration was performed. A

custom spinous-process clamp with passive-reflective infrared (IR) tracking spheres, fabricated

in-house, was clamped to the level adjacent to the level to which the DRF was affixed, to 2-5

levels distant from the DRF, or to a stationary anatomic target supported by pelvic bolsters or a

Mayfield head clamp on the operating table (Figure 8-1). The 3D position of the spinous-process

clamp was tracked at 20 Hz over ~12 respiratory cycles. Tracked levels were categorized into

cervical, upper thoracic (T1-T6), lower thoracic (T7-T12), and lumbar. Data were collected on

multiple parameters that may influence respiration-induced motion, including patient age,

gender, body-mass index (BMI), spinal level, respiratory rate (RR), heart rate (HR), mean

arterial pressure (MAP), tidal volume (TV), positive end-expiratory pressure (PEEP) and

ventilator mode.

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Motion during each respiratory cycle was quantified as the ‘peak-to-peak’ displacement (i.e.

from end-expiration to end-inspiration) in each of the antero-posterior, cranio-caudal, and medio-

lateral axes (Figure.8-2)

Figure 8-1. Respiratory motion tracking with a custom spinous process clamp. In-vivo surgical field with DRF

for in-house OTI navigation system (arrowhead), and custom spinous-process infrared tracking clamp to quantify

vertebral motion (arrow).

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Figure 8-2. Vertebral respiratory motion tracking. Representative tracking of the 3D displacement of a cervical

vertebra over ~14 respiratory cycles, spanning 72 seconds. ‘Peak-to-peak’ displacement is computed as the change

in displacement from end-expiration to end-inspiration within one respiratory cycle, indicated by the red arrow.

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8.3.5 Statistical Analyses

Differences in absolute navigation errors between spinal levels were quantified with one-way

ANOVA, with Tukey’s Honest-Significant-Difference test for post-hoc comparisons.

Differences in error dispersion were computed using Levene’s test of homogeneity of variances.

Predictors of increased vertebral motion from distance from the DRF, surgical manipulation, or

respiratory motion were assessed using multiple linear regression models. Variables were entered

simultaneously into a full model, with nonlinearity checked using 3 cubic splines. Models were

assessed for collinearity as well as quality of fit. Hierarchical mixed-effects general linear

modelling was employed to adjust for second-order differences between cadavers/patients, where

required based on univariate analyses.


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8.4 Results

In cadaveric testing, 132 laminar fiducials were implanted from C2-S1, 46 cervical, 47 thoracic,

and 39 lumbar. 583 respiratory cycles were tracked in 10 patients in-vivo, 136 in the cervical

spine, 167 upper thoracic, 74 lower thoracic, and 206 lumbar.

8.4.1 Navigation Error from Proximity to DRF

(Mean ± SD) quantitative navigation error at the level of the DRF was 1.1 ± 0.3 mm in the

antero-posterior (AP) axis, 1.3 ± 0.2 mm in the medio-lateral (ML) axis, and 1.6 ± 0.4 mm in the

cranio-caudal (CC) axis, for an overall 3D error of 2.7 ± 0.4 mm, representing the baseline

navigation error.

An increase in mean quantitative navigation error of ≥ 2 mm was seen in 3D in the cervical and

lumbar spine at ≥ 2 levels distant from the DRF, driven largely by an equivalent increase in error

in the AP axis at ≥ 2 levels distant from the DRF, and in the ML axis at ≥ 3 levels distant (Figure

8-3). An increase in error of ≥ 4 mm was seen in 3D at 5 levels from the DRF, driven by an

equivalent increase in AP error at ≥ 4 levels from the DRF. No significant increases in error in

the CC axis were seen up to 5 levels from the DRF (Figure 8-3).

The variability in navigation error increased significantly in the AP axis at ≥ 2 levels from the

DRF (SD 0.41 vs. 0.27 mm, p = 0.026), and in the ML axis at ≥ 1 level from the DRF (SD 0.53

vs. 0.18 mm, p < 0.001), by Levene’s test of homogeneity of variances. No significant increases

in error dispersion in the CC axis were observed.

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8.4.2 Navigation Error from Surgical Manipulation

(Mean ± SD) displacement due to surgical manipulation was 1.6 ± 1.1 mm in 3D across all

levels, non-significantly greater in the lumbar spine (1.8 ± 1.5 mm) than in the thoracic (1.5 ± 1.0

mm) and cervical (1.3 ± 0.8 mm) spine. Displacement in the ML axis was significantly greater in

the thoracic spine relative to the cervical spine (1.0 ± 0.9 mm vs. 0.4 ± 0.3 mm; p<0.001), and in

the CC axis in the lumbar spine relative to both the cervical and thoracic spine (1.4 ± 1.1 mm vs.

0.9 ± 0.8 mm and 0.8 ± 0.7 mm, respectively; p<0.001).

Deviation of ≥ 2 mm was observed in 3D in 17.4% of cervical levels, 19.2% of thoracic levels,

and 38.5% of lumbar levels. 3D displacement of ≥ 3 mm was recorded in 6.5% of cervical levels,

10.7% of thoracic levels, and 10.3% of lumbar levels (Figure 8-4).

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Figure 8-3. Translational navigation error from distance to DRF. Standard boxplots demonstrating the increase

in translational error from baseline, as a function of the number of levels distant from the DRF, in 3D (A) and each of

the medio-lateral (B), antero-posterior (C) and cranio-caudal (D) axes. Boxes represent the first, median and third

quartiles. Whiskers represent 1.5x the interquartile range. * represents significant difference from baseline error, at

p<0.05.

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Figure 8-4. Translational navigation error with surgical manipulation. Histograms demonstrating the percentage

of screw tracts with ≥ 2 mm, ≥ 3 mm and ≥ 4 mm deviation with surgeon manipulation, in 3D and in each of the

medio-lateral (ML), antero-posterior (AP) and cranio-caudal (CC) axes. Manipulation-induced displacement is shown

in each of the cervical (A), thoracic (B) and lumbar (C) spines.

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8.4.3 Navigation Error from Respiration-Induced Motion

Respiration-induced motion was quantified as absolute motion, or motion relative to a DRF

clamped 1-5 levels adjacent. In general linear modelling, accounting for spinal level, there were

no differences in relative vertebral motion in any axis between the DRF at any of 1-5 levels

distant, hence these are pooled for subsequent analysis as ‘relative motion’.

(Mean ± SD) absolute 3D vertebral motion across all levels was 2.0 ± 1.3 mm, significantly

greater in the lower thoracic spine than in the cervical, upper thoracic or lumbar spine (4.3 ± 0.4

mm vs. 2.6 ± 1.2 mm, 1.7 ± 0.5 mm, and 1.1 ± 0.4 mm, respectively; p<0.001). Absolute motion

was greatest in the AP axis vs. the ML and CC axes (2.4 ± 1.8 mm vs. 0.8 ± 0.6 mm and 0.9 ±

0.6 mm, respectively; p<0.001)(Figure 8-5). Absolute 3D motion was greater than 2 mm in

32.9% of cases and greater than 4 mm in 11.6%, significantly more frequently in the lower

thoracic spine (p<0.001)(Figure 8-6). (Mean ± SD) relative respiration-induced 3D displacement

across all levels was 0.5 ± 0.1 mm, significantly greater in the lumbar spine and in the AP and

CC axes (Figure 8-5).

In general linear modelling, TV (r = 0.263, p=0.032), PEEP (r = 0.756, p<0.001) and MAP (r =

0.150, p<0.001) were positively correlated with absolute 3D and AP respiration-induced motion

(r – Pearson correlation coefficient). Age, gender, and BMI did not significantly correlate with

any respiration-induced displacement.

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Figure 8-5. Respiration-induced vertebral motion. Standard boxplots demonstrating absolute (top) and relative

(bottom) respiration-induced vertebral motion, in 3D and in each of the ML, AP and CC axes, stratified by spinal

region. Boxes represent the first, median and third quartiles. Whiskers represent 1.5x the interquartile range. *

represents significant difference, at p<0.05.

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Figure 8-6. Respiratory cycles with clinically significant vertebral motion. Histograms demonstrating the

percentage of respiratory cycles with ≥ 2 mm and ≥ 4 mm of displacement, in 3D and in each of the ML, AP and CC

axes, in the cervical (A), upper thoracic (B), lower thoracic (C), and lumbar (D) spine.

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8.5 Discussion

While modern 3D-CAN has demonstrably increased instrumentation accuracy across all spinal

levels, widespread enthusiasm for the technology has been tempered by high costs as well as

workflow disruption.(Barsa et al., 2016; Austin C Bourgeois et al., 2015; Hartl et al., 2013;

Hecht et al., 2010; Mason et al., 2014; W. Tian et al., 2013; Wagner et al., 2017) Initial 3D CAN

systems employing registration of patient anatomy to pre-operative imaging, using either point-

or surface-matching techniques, were highly cumbersome to register. Advances in intra-

operative imaging, either 3D fluoroscopy or cone- or fan-beam CT, have allowed for faster

automatic patient registration to intra-operatively-acquired imaging.(F Costa et al., 2014) Intra-

operative imaging devices have concurrently eliminated one source of navigation error due to

intersegmental mobility, from positional changes from a supine pre-operative scan to a prone

operating position. However, all contemporary 3D-CAN systems, whether registering to pre- or

intra-operative imaging, retain dependence on a DRF to maintain patient-to-image registration

and allow tool tracking. Navigation errors may therefore arise by virtue of distance from the

DRF, a limitation largely of the infra-red optical tracking technology used by most current CAN

devices, as well as due to intersegmental mobility, from surgical manipulation and patient

respiration-induced motion at levels distant to the DRF. Using an OTI navigation system

developed in-house, allowing granular control and root-level system access, we quantify and

identify predictors of these errors for the first time in the literature.

The impact of working distance from a DRF on navigation error has been studied

heterogeneously in the limited literature to date. In the cervical spine, using point-matching

registration to pre-operative CT, Tauchi et al. demonstrated a 17% increase in cervical pedicle

screw perforation rate when working one level distant from the DRF, with greater distance

correlated with larger error.(Tauchi et al., 2013) The literature in the thoracolumbar spine is

more controversial. Using point-matching registration to pre-operative CT in the setting of

adolescent scoliosis, Uehara et al. demonstrated significantly increased pedicle screw perforation

rates at 3 or more levels distant to the DRF, while Takahashi et al. showed pedicle violation rates

of 1.5% at up to 3 levels distant from the DRF, though without comparison to segmental

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registration.(Takahashi et al., 2010; Uehara et al., 2017) Papadopoulos et al. demonstrated no

significant increases in computer-reported registration error, known to correlate poorly with true

application error, at up to 4 levels from the DRF.(Guha, Jakubovic, Gupta, Alotaibi, et al., 2017;

E C Papadopoulos et al., 2005) Scheufler et al. claimed safe instrumentation up to 12 levels from

the DRF with registration to an intra-operative fan-beam CT, based on radiographic pedicle

screw grading, though requiring 3 intra-operative CT spins and with an additional 2.5% rate of

K-wire revision.(Scheufler et al., 2011b) Here, we show quantitatively that an increase in

navigation error of ≥ 2mm, corresponding to the difference between an ‘acceptable’ and an

‘unacceptable’ pedicle screw in common radiographic grading classifications, occurs at 2 or

more levels distant from the DRF in the medio-lateral and antero-posterior axes.(Gertzbein &

Robbins, 1990; Neo, Sakamoto, & Fujibayashi, 2005) While antero-posterior accuracy, i.e. screw

depth, may be less important due to typically greater tolerances, inaccuracy in the medio-lateral

axis may lead to neural or vascular injury. Our study, performed in a cadaveric setting, also

eliminates respiration-related motion as a confounder for error due to distance from the DRF.

Intersegmental mobility may also lead to error during surgical manipulation at levels distant

from the DRF. This has been studied only once in the literature to our knowledge, with Glossop

et al. demonstrating up to 12mm of 3D movement in the lumbar spine with paraspinal muscle

dissection and pedicle targeting in an in-vivo setting.(N. Glossop & Hu, 1997) We show absolute

3D movement of only 1.55mm across all levels, due in part to the rigid formalin-fixed cadaveric

setting, as well as because only pedicle targeting was assessed, whereas typically more force may

be applied for paraspinal tissue dissection. However, the key point is that while absolute

manipulation-related motion is typically small in our study, variability in motion is high

particularly in the lumbar spine, and may be expected to be even greater in an in-vivo setting

with more pliable tissues, as well as a range of degenerative pathologies with greater

intersegmental mobility.

Vertebral motion from patient respiration may result in navigation inaccuracy distant to a DRF,

due to intersegmental mobility. In the limited literature to date, respiratory motion has been

reported up to 1.6mm in the lumbar spine, with no direct quantification of vertebral motion at

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other levels.(N. Glossop & Hu, 1997; Liu et al., 2015) In our study, we demonstrate via direct

measurement that while respiration-induced vertebral motion averages only 0.48 mm at 1-5

levels from a DRF, absolute motion with respect to a DRF affixed to a remote fixed anatomical

site may be as large as 10 mm in the AP axis and 6 mm in the ML axis. The attenuation of

respiration-induced error with an adjacent DRF, even though we demonstrate increased error in

optical tracking at 2 or more levels distant from the DRF, is likely due to temporal averaging of

errors which does not occur at a single time point. Caution should therefore be exercised when

performing navigated procedures with a DRF affixed to the pelvis or Mayfield head clamp, as is

commonly done in long-segment lumbosacral or cervical procedures, respectively. Furthermore,

greater respiration-induced displacement is correlated with larger TV and PEEP; apnea or

modification of ventilator parameters may therefore be warranted at critical stages of a navigated

procedure where accuracy is paramount.

There are multiple limitations to our analysis. Pre-clinical testing was conducted in formalin-

fixed cadavers, with far more rigid tissues and therefore underestimated vertebral motion than

might be expected in clinical application. Respiration-induced motion was assessed up to 5 levels

distant to a DRF, due to the limited exposure in most procedures in this series. Further work is

therefore required to assess the impact of working at greater distances from a DRF on mitigation

of respiration-induced vertebral motion.

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8.6 Conclusions

Vertebral motion is unaccounted for during image-guided surgery when performed at levels

distant from the DRF. Navigating instrumentation within 2 levels of the DRF is likely to

minimize the risk of navigation error. While respiration- and manipulation-induced vertebral

motion is typically small, there may be significant variability in magnitude, particularly with

spinal region and ventilator parameters. Surgeons may mitigate these errors intra-operatively by

placing the DRF adjacent to the registered level, rather than on a Mayfield head clamp or pelvis,

to minimize respiration-induced error. If performing work distant to a DRF, temporary apnea or

adjustment of ventilator parameters may be warranted at critical stages of the procedure, to

minimize respiration-induced error. Surgeons should also take care to cease manipulation of

bony elements when actively using navigation guidance. Future generations of image-guidance

systems should compensate for these errors in real-time to minimize navigation inaccuracy.

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Chapter 9 Geometric Congruence in Surface Registration for Spinal Intra-

Operative Three-Dimensional Navigation

Preamble


Guha D, Jakubovic R, Leung MK, Ginsberg HJ, Fehlings MG, Mainprize TG, Yee A, Yang

VXD. Quantification of computational geometric congruence in surface-based registration for

spinal intra-operative three-dimensional navigation. Manuscript in submission.

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9.1 Abstract

Computer-assisted navigation (CAN) may guide spinal instrumentation, and requires alignment

of patient anatomy to imaging. Iterative closest-point (ICP) algorithms register anatomical and

imaging surface datasets, which may fail in the presence of significant geometric symmetry

(congruence), leading to failed registration or inaccurate navigation. In this study, we therefore

strove to computationally quantify geometric congruence in posterior spinal exposures, and

identify predictors of potential navigation inaccuracy. Midline posterior exposures were

performed from C1-S1 in four human cadavers. CAN based on optical topographic imaging

(OTI) generated surface maps of the posterior elements at each level. Maps were reconstructed to

include bilateral hemilamina, or unilateral hemilamina with/without the base of the spinous

process. Maps were fitted to symmetrical geometries (cylindrical/spherical/planar) using

computational modelling, and the degree of model fit quantified based on the ratio of model

inliers to total points. Geometric congruence in a clinical setting was assessed similarly, in 11

patients undergoing midline exposures in the cervicsal/thoracic/lumbar spine for posterior

instrumented fusion. In cadaveric testing, increased cylindrical/spherical/planar symmetry was

seen in the subaxial cervical spine relative to the high-cervical and thoracolumbar spine

(p<0.001). Extension of unilateral exposures to include the ipsilateral base of the spinous

process, or to a central bilateral exposure, decreased symmetry independent of spinal level

(p<0.001). In clinical testing, increased cylindrical/spherical/planar symmetry was again seen in

the subaxial cervical spine relative to the thoracolumbar spine (p<0.001), and in the thoracic

spine relative to the lumbar spine (p<0.001). Symmetry in all geometric configurations in

unilateral exposures was decreased by 20% with inclusion of the ipsilateral base of the spinous

process. We concluded that geometric congruence is most evident at C1 and the subaxial cervical

spine, warranting greater vigilance in navigation accuracy verification. At all levels, inclusion of

the base of the spinous process in unilateral registration decreases the likelihood of geometric

symmetry and navigation error. This work is important to allow the extension of line-of-sight

based registration techniques, including OTI, to minimally-invasive unilateral approaches.

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9.2 Introduction

Spinal instrumentation traditionally has been placed freehand based on anatomic landmarks,

which may be highly variable, or with fluoroscopic guidance resulting in significant radiation

exposure to operating room personnel.(Nelson et al., 2014; Robertson, Novotny, Grobler, &

Agbai, 1998; Villard et al., 2014) Computer-assisted navigation (CAN) may guide spinal

instrumentation placement, significantly improving accuracy and minimizing acute and long-

term malposition related complications.(Fichtner et al., 2017; Luther, Iorgulescu, Geannette,

Gebhard, Saleh, Tsiouris, & Härtl, 2015; Xiao et al., 2017) Image guidance in CAN may be

based on pre-operative imaging, typically CT, or intra-operatively-acquired 3D fluoroscopy or

CT; in all cases, navigation requires registration of the image and patient spaces. Our laboratory

has developed a novel technique for image-to-patient registration, based on optical topographic

imaging (OTI), which rapidly acquires a surface map of exposed spinal anatomy under direct

vision and automatically registers to pre-operative CT in real-time, minimizing workflow

disturbance (Chapter 5). Registration of three-dimensional point sets in contemporary surface-

based navigation techniques, including OTI, is typically performed using variants of the iterative

closest-point (ICP) algorithm, in which two meshes are aligned using an initial rigid-body pose

estimation, followed by iterative refinement of the translational and rotational transformations to

minimize a distance error metric between the two meshes.(Besi & Mckay, 1992; Y. Chen &

Medioni, 1991) ICP algorithms may be prone to instability when too many point pairs arise from

unconstrained symmetrical, or congruent, geometries, including cylinders, spheres, and

planes.(Armesto et al., 2010; Gelfand et al., 2003; Pottmann & Hofer, 2003) While multiple

variations of ICP have attempted to address the stability of the final alignment between meshes,

non-convergence from geometric congruence remains a potential source of registration error in

image-guided surgery, leading to failed registration or, worse, successful registration with

inaccurate navigation. ICP convergence is particularly critical in surface-based registration

techniques, such as OTI, especially when applied to minimally-invasive (MIS) exposures with

fewer available points to increase the variance in input geometry and specify the initial alignment

pose. Here, we therefore quantify geometric congruence, or symmetry, in posterior spinal

exposures using computational modelling, and identify predictors of potential navigation

inaccuracy from this error mechanism. This understanding is essential to allow the safe and

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efficient translation of any surface-based navigation technique requiring line-of-sight to exposed

anatomy, to minimally-invasive spinal exposures.

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9.3 Methods


Surface geometry of posterior spinal exposures was assessed initially in four human cadavers, as

part of pre-clinical validation of our OTI navigation system. All cadavers underwent pre-

operative helical CT imaging at 0.5mm slice thickness. Institutional ethics board approval was

obtained (REB# 16-0051-E).

Surface geometry of posterior spinal anatomy was subsequently assessed in-vivo in 11 patients,

undergoing midline exposures in the cervical/thoracic/lumbar spine for OTI-guided posterior

instrumented fusion as part of an ongoing trial of OTI navigation at Sunnybrook Health Sciences

Centre (REB# 309-2014 and 086-2015). All patients underwent pre-operative helical CT

imaging, reformatted at 0.625mm slice thickness.

9.3.2 OTI Registration

Cadavers were positioned prone on a standard operating table. Midline posterior exposures were

performed bilaterally from C1-S1 in four human cadavers, extending to the lateral edge of the

lateral masses in the cervical spine, and to the transverse processes in the thoracolumbar spine.

3D surface maps of the posterior elements were generated using OTI at each level (Figure 9-1).

Technical details of OTI registration are described separately, in Chapter 5.(Jakubovic et al.,

2016) Briefly, structured light is projected onto the exposed anatomy and its deformation

recorded by stereoscopic cameras to generate a 3D surface point cloud, followed by automatic

registration via an iterative closest-point (ICP) algorithm to the pre-operative CT.

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In clinical testing, all patients were positioned prone on a Wilson frame. Patients undergoing

cervical instrumentation were also placed in a Mayfield head clamp. Standard midline posterior

exposures sufficient for open instrumentation placement were performed, with OTI surface

acquisition and registration similar to cadaveric testing.

Figure 9-1. Cadaveric midline exposures for OTI. (A) Representative standard midline posterior spinal exposure in

cadaveric testing. (B) OTI 3D surface map of a midline posterior cervical spine exposure.

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9.3.3 Computational Modelling of Geometric Congruence

3D surface maps generated from each vertebral level were thresholded to isolate the vertebra.

The point clouds comprising each surface map were subsequently reconstructed to capture the

bilateral hemilaminae including spinous process (Group A), each unilateral hemilamina

including the ipsilateral base of the spinous process (Group B), and each unilateral hemilamina

excluding the spinous process (Group C)(Figure 9-2). All thresholding and reconstruction was

performed in an open-source data visualization package (ParaView 5.2.0. Kitware, Inc.; Clifton

Park, NY, USA).

Reconstructed point clouds from each group at each registered level were subsequently fitted to

symmetrical geometries (cylindrical, spherical, planar) in a computing package (MATLAB

R2016b. The MathWorks, Inc.; Natick, MA, USA) using a random sample consensus

(RANSAC) algorithm, iterated 100 times (Figure 9-3).

The degree of fit of each point cloud to a geometrically-symmetric shape was quantified using

two metrics, the mean-adjusted coefficient of variation in the root-mean-square error (CoV-

RMSE) of the point cloud to the fitted shape, as well as the proportion of total points fitted to the

symmetric shape (inliers-to-points ratio, ITPR). Increased fit to a symmetric geometry, and

therefore increased likelihood of navigation error, is denoted by decreased CoV-RMSE and

increased ITPR. As the CoV-RMSE is highly dependent on the user-specified maximum inlier

error for the RANSAC algorithm, sensitivity analyses were performed for each geometry with

the maximum inlier error set to 0.1mm, 0.5mm, 1.0mm and 2.0mm, and the maximum inlier

error resulting in the lowest CoV-RMSE chosen for subsequent ITPR analyses; a maximum

inlier error of 0.5mm was selected for all analyses in this study.

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Figure 9-2. Reconstruction of OTI surface map point clouds. Reconstructed surface map point clouds, capturing

the bilateral hemilaminae including spinous process (Group A) viewed from above (top) and axially (bottom),

unilateral hemilamina including base of the spinous process (Group B), and unilateral hemilamina excluding the

spinous process (Group C).

Figure 9-3. Fitting of symmetrical geometries to OTI point clouds. (A) Point cloud of an L2 unilateral hemilamina

including base of spinous process (Group B). Fitting of a cylinder (B), sphere (C), and plane (D) to the L2 point cloud.

Red dots represent points included in the fitted model (inliers); green dots represent points excluded from the fitted

model (outliers).

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Predictors of increased fit to symmetric geometries were explored using univariate and multiple

linear regression modelling. For univariate analyses, RMSE and ITPR were compared between

spinal levels and between point cloud reconstruction groups using one-way analysis of variance

(ANOVA), with Tukey’s Honest-Significant-Difference test for post-hoc comparisons.

Differences in CoV-RMSE were compared using Levene’s test of homogeneity of variances.

Hierarchical mixed-effects general linear modelling was employed for multivariate analyses to

adjust for second-order differences between cadavers/patients. Significance levels for all tests

were set at α < 0.05.


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9.4 Results

For the four cadavers used in pre-clinical testing, mean age at death was 91.4 years (range 83-

96). In-vivo clinical testing was performed in eleven patients, with mean age 58.3 years (range

42-71).

9.4.1 Geometric Congruence by Spinal Region

In cadaveric testing, for unilateral registrations (Group C), C1 was found to have greater

cylindrical and planar symmetry than C2, the subaxial cervical spine, as well as the thoracic,

lumbar and sacral spines, based on ITPR (p<0.001)(Figure 9-4). C2 demonstrated increased

symmetry in all configurations relative to the thoracolumbar and sacral spines, while the subaxial

cervical spine showed greater planar symmetry than the thoracolumbar and sacral spines

(p<0.001). ITPR stratified by individual spinal levels are shown in Table 9-1.

In unilateral in-vivo registrations (Group C), the subaxial cervical spine again demonstrated

greater symmetry, in all configurations, relative to the thoracic and lumbar spine

(p<0.001)(Figure 9-4). Uniquely, the posterior elements of thoracic vertebrae also showed

greater symmetry in all configurations relative to the lumbar spine (p<0.001).

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Figure 9-4. Geometric congruence by spine region. (Top) Standard boxplot of the ITPR stratified by spine region

(C1, C2, subaxial cervical, T - thoracic, L – lumbar, S – sacrum), for each of cylindrical, spherical and planar

geometries, in cadaveric testing. (Bottom) Standard boxplot of the ITPR stratified by spine region, in clinical testing.

Error bars represent 1.5xIQR. * denotes significance at p<0.05.

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Table 9-1. Geometric congruence for unilateral registrations by spinal level. Inliers-to-points ratio (ITPR) for

unilateral registrations (Group C) for each symmetrical configuration, stratified by spinal level, in cadaveric testing. All

values reported as (mean±SD).

Level Cylinder Sphere Plane

C1 0.541 ± 0.092 0.555 ± 0.091 0.529 ± 0.072

C2 0.499 ± 0.081 0.541 ± 0.066 0.510 ± 0.064

C3 0.414 ± 0.100 0.494 ± 0.125 0.433 ± 0.100

C4 0.527 ± 0.125 0.645 ± 0.165 0.553 ± 0.150

C5 0.618 ± 0.150 0.727 ± 0.155 0.669 ± 0.186

C6 0.513 ± 0.088 0.591 ± 0.067 0.535 ± 0.060

C7 0.507 ± 0.108 0.560 ± 0.076 0.522 ± 0.091

T1 0.491 ± 0.089 0.582 ± 0.079 0.530 ± 0.066

T2 0.524 ± 0.104 0.603 ± 0.122 0.561 ± 0.123

T3 0.468 ± 0.088 0.514 ± 0.090 0.468 ± 0.108

T4 0.474 ± 0.110 0.529 ± 0.107 0.495 ± 0.116

T5 0.472 ± 0.096 0.546 ± 0.093 0.521 ± 0.084

T6 0.448 ± 0.088 0.521 ± 0.090 0.503 ± 0.099

T7 0.442 ± 0.101 0.507 ± 0.102 0.486 ± 0.095

T8 0.438 ± 0.091 0.513 ± 0.111 0.478 ± 0.106

T9 0.409 ± 0.100 0.473 ± 0.140 0.444 ± 0.132

T10 0.432 ± 0.083 0.454 ± 0.105 0.429 ± 0.102

T11 0.472 ± 0.097 0.539 ± 0.097 0.485 ± 0.089

T12 0.458 ± 0.086 0.517 ± 0.094 0.479 ± 0.109

L1 0.434 ± 0.093 0.510 ± 0.097 0.462 ± 0.106

L2 0.417 ± 0.091 0.483 ± 0.084 0.455 ± 0.084

L3 0.452 ± 0.073 0.486 ± 0.035 0.466 ± 0.042

L4 0.492 ± 0.099 0.518 ± 0.073 0.479 ± 0.052

L5 0.510 ± 0.098 0.599 ± 0.130 0.521 ± 0.077

S1 0.449 ± 0.091 0.537 ± 0.144 0.503 ± 0.095

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9.4.2 Geometric Congruence by Laterality

In cadaveric testing, extension of the registered anatomy from a unilateral exposure (Groups

B+C) to a bilateral acquisition (Group A) resulted in significant reduction in symmetry in all

geometric configurations (Figures 9-5, 9-6). ITPR for cylindrical configurations, i.e. cylindrical

symmetry, was decreased by 47.9% (0.436 ± 0.107 vs. 0.227 ± 0.055, p<0.001)(mean ± SD),

spherical symmetry by 42.0% (0.493 ± 0.121 vs. 0.286 ± 0.082, p<0.001), and planar symmetry

by 48.6% (0.438 ± 0.119 vs. 0.225 ± 0.063, p<0.001), for unilateral vs. bilateral registrations

(Groups B+C vs. Group A).

In clinical testing, absolute ITPRs were decreased for all geometric configurations relative to

cadaveric data (p<0.001), but with similar reductions in symmetry by extending unilateral

acquisitions (Groups B+C) to a bilateral registration (Group A)(Figure 9-5). Cylindrical

symmetry was reduced by 50.0% (0.366 ± 0.111 vs. 0.183 ± 0.037, p<0.001), spherical

symmetry by 47.7% (0.451 ± 0.136 vs. 0.236 ± 0.069, p<0.001), and planar symmetry by 50.8%

(0.390 ± 0.144 vs. 0.192 ± 0.048, p<0.001), in Group A vs. Groups B+C.

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Figure 9-5. Geometric congruence by registration laterality. (Top) Standard boxplot of the ITPR stratified by

unilateral vs. bilateral registrations, for each of cylindrical, spherical and planar geometries, in cadaveric testing.

(Bottom) Standard boxplot of the ITPR stratified by unilateral vs. bilateral registrations, in clinical testing. Error bars

represent 1.5xIQR. * denotes significance at p<0.05.

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Figure 9-6. Reduction in geometric congruence with bilateral registration. (Top row) Fitting of a cylinder (A),

sphere (B), and plane (C) to a unilateral L2 registration (D). Inliers are denoted by red dots, outliers by green dots.

(Bottom row) Significant reduction in symmetry, i.e. ratio of inliers (red dots) to outliers (green dots), by extension to a

bilateral L2 registration (H), in each of cylindrical (E), spherical (F), and planar (G) geometries.

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9.4.3 Geometric Congruence by Inclusion of the Spinous Process

In cadaveric testing, extension of the registered anatomy from a unilateral exposure (Group C) to

include the ipsilateral base of the spinous process (Group B) reduced symmetry significantly in

all configurations (Figures 9-7, 9-8). Cylindrical symmetry was reduced by 16.5% (0.472 ±

0.107 vs. 0.394 ± 0.089, p<0.001)(mean ± SD), spherical symmetry by 18.4% (0.539 ± 0.119 vs.

0.440 ± 0.099, p<0.001), and planar symmetry by 26.1% (0.498 ± 0.111 vs. 0.368 ± 0.087,

p<0.001), for unilateral registrations including the ipsilateral spinous process base vs. without

(Group B vs. Group C).

For in-vivo registrations, absolute ITPRs were decreased for all configurations relative to

cadaveric testing (p<0.001). Inclusion of the ipsilateral spinous process base in the registration

reduced cylindrical symmetry by 24.6% (0.418 ± 0.116 vs. 0.315 ± 0.078, p<0.001), spherical

symmetry by 28.8% (0.527 ± 0.136 vs. 0.375 ± 0.082, p<0.001), and planar symmetry by 40.2%

(0.488 ± 0.138 vs. 0.292 ± 0.054, p<0.001), relative to registrations excluding the spinous

process (Group B vs. Group C).

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Figure 9-7. Geometric congruence by spinous process inclusion. (A) Standard boxplot of the ITPR stratified by

inclusion of the spinous process (SP) base, for unilateral registrations, in cadaveric testing. (B) Standard boxplot of

the ITPR stratified by inclusion of the spinous process base, in clinical testing. Error bars represent 1.5xIQR. *

denotes significance at p<0.05.

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Figure 9-8. Reduction in geometric congruence with inclusion of ipsilateral spinous process base. (Top row)

Fitting of a cylinder (A), sphere (B), and plane (C) to a unilateral L2 registration excluding the base of the ipsilateral

spinous process (D). Inliers are denoted by red dots, outliers by green dots. (Bottom row) Significant reduction in

symmetry, i.e. ratio of inliers (red dots) to outliers (green dots), by extension of the unilateral registration to include

the base of the ipsilateral spinous process (H), in each of cylindrical (E), spherical (F), and planar (G) geometries.

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9.5 Discussion

While CAN has been shown to improve instrumentation accuracy across all spinal levels,

widespread adoption has been limited by high capital costs and workflow disruptions.(Barsa et

al., 2016; Austin C Bourgeois et al., 2015; Hartl et al., 2013; Hecht et al., 2010; Mason et al.,

2014; N. F. Tian et al., 2011; Wagner et al., 2017) OTI for spinal navigation significantly

streamlines registration workflow by employing rapid optical 3D scanning to generate a high-

density surface point cloud.(Jakubovic et al., 2016) However, OTI requires direct vision of bony

anatomy for registration, limiting its applicability to some current paradigms of minimally-

invasive approaches. Extension of OTI, and other efficient surface-based registration techniques,

to minimally-invasive approaches requires an understanding of mechanisms of registration

failure. OTI, along with every current navigation technique applying surface-based registration

to pre-operative imaging, employs an ICP algorithm to register point sets. Some pitfalls of ICP

algorithms are known, including failed registration due to poor initial pose estimation from large

rigid-body fiducial localization errors or soft tissue deformation, susceptibility to mismatched

outliers, and inability to account for differences in scale between point sets, resulting in hundreds

of variants of the original ICP algorithm published in the past 20 years.(Clements et al., 2008;

Maurer et al., 1996; Pomerleau et al., 2013; Xin & Pu, 2010; Ying et al., 2009) A lack of

convergence, i.e. failed or inaccurate registration, of ICP algorithms in the presence of geometric

congruence has been demonstrated in the context of 3D scanned shapes, with multiple variants

attempting to minimize the associated rotational error, albeit with a target translational error of

<25mm as a definition of ‘successful registration’, far too large for surgical navigation.(Armesto

et al., 2010) Other variants have attempted to use a similar RANSAC-algorithm based approach

as used in our study, to detect geometric symmetry in the point sets to be aligned, however

requiring a computational time of 10 minutes, again unacceptable for real-time surgical

navigation.(Berner et al., 2008) To date, geometric homogeneity has not been demonstrated in

the context of surgical navigation; in spinal surgery, geometric congruence is likely to arise in

unilateral or minimal-exposure registrations. While CAN techniques employing intra-operative

3D imaging do eliminate this potential error, it comes with significant capital expense, operating

time, and workflow hindrance. To allow the workflow improvements of surface-scanning

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techniques such as OTI to be fully realized, with their significantly greater point density, it is

paramount to characterize their potential limitations and failure mechanisms.

Here, we show first that geometric instability, or congruence, is greatest at C1 and in the subaxial

cervical spine, in both cadaveric and in-vivo settings. This is certainly intuitive given the

relatively smooth and symmetric nature of the C1 posterior arch, disrupted minimally by the

posterior tubercle. In the subaxial cervical spine, facets are relatively flat and smooth relative to

those in the thoracolumbar spine, again resulting in significant geometric congruence that may

lead to potential navigation error when registered through minimal exposures. In the literature on

navigated pedicle screw placement, breach rates are consistently greater in the cervical spine

than in the thoracolumbar spine, although the relatively smaller diameter of cervical pedicles

may certainly contribute to this.(Mason et al., 2014) In our own clinical validation of OTI

navigation, quantitative navigation accuracy has been comparable in the cervical vs.

thoracolumbar spine, albeit with statistically insignificantly-greater translational and angular

errors in the cervical spine.(Guha, Jakubovic, Gupta, Fehlings, et al., 2017; Jakubovic et al.,

2016)

For unilateral registrations using surface-based navigation techniques, geometric instability can

be improved significantly by extending the registration to a bilateral exposure, intuitive as three-

dimensionally-unique geometry in the form of the spinous process and contralateral hemilamina

is now included in the surface dataset to be registered by ICP. More practically, however, we

show that geometric instability may also be improved in a unilateral registration by simply

including the adjacent base of the ipsilateral spinous process in the registered anatomy. While the

absolute values of ITPR were greater in our cadaveric vs. clinical testing, due likely to larger and

more rounded osteophytes in the significantly older cadaveric population, improvement in

symmetry by including the ipsilateral spinous process base was seen in both cadaveric and

clinical settings, in fact more so in the in-vivo population. Clinically, this has relevance in

performing for instance minimally-invasive TLIFs with surface-based navigation guidance,

whereby a unilateral exposure is required for the interbody work, and slight medialization of the

exposure to include the ipsilateral spinous process base can significantly improve the likelihood

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of successful navigation registration and implant accuracy. It is important to note, however, that

geometric symmetry is minimized but not eliminated with these maneuvers, hence manual

verification of navigation accuracy by the surgeon remains paramount to the safe and efficient

performance of navigated spinal procedures. We propose a systematic technique of manual

registration verification to account for all dimensions in which geometric congruence may lead

to navigation error (Figure 9-9).

Our findings may be extended to surface imaging and navigation for cranial and non-

neurosurgical procedures. In cranial navigation, while initial surface-based registrations prior to

patient draping are likely to be accurate due to the unique geometry offered by variant facial

features, intra-operative updating of registrations on the external skull requires caution

particularly over the convexity, where significant cylindrical and spherical symmetry may be

expected.

While our study was conducted using an OTI navigation system, our findings may be

generalized to those of any surface-based navigation techniques. Our study is limited by its

simulation of unilateral exposures, as our surface scanning was performed on fully-open bilateral

exposures, and reconstructed to simulate unilateral registration post-hoc. Future studies of in-

vivo MIS surgery using tubular retractors are warranted, to assess registration quality in true

unilateral exposures.

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Figure 9-9. Protocol for manual registration verification. (A) Representative point cloud of an open bilateral

posterior lumbar exposure. Manual accuracy verification should be performed with a tracked sharp-tip tool statically at

the superior and inferior facet joints (*), and dynamically by sliding axially (1) and sagittally (2) along the hemilaminae

as well as along the spinous process tip (3). Verification steps shown on orthogonal sagittal (B) and axial (C) CT

reconstructions as seen on typical navigation displays, with static verification points (*) and dynamic sliding

maneuvers (1, 2, 3).

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9.6 Conclusions

Geometric congruence may lead to failed or inaccurate registration with surface-based surgical

navigation techniques. Congruence is most evident at C1 and the subaxial cervical spine,

warranting greater vigilance in navigation accuracy verification in these regions. At all spinal

levels, medial extension of a unilateral exposure to include the base of the ipsilateral spinous

process, or to a central bilateral exposure, decreases the likelihood of geometric symmetry and

therefore improves the likelihood of successful and accurate navigation in minimally-invasive

approaches.

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Chapter 10 Concluding Summary, General Discussion, and Future Directions

Preamble

Chapter 10.3 (Future Directions) is modified from the following:

Guha D, Yang VXD. Perspective review on applications of optics in spinal surgery. Journal of

Biomedical Optics 2018, in press.

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10.1 Concluding Summary

Neurosurgery as a surgical discipline has, by necessity, long been at the forefront of image-

guided technologies. Computer-assisted intra-operative navigation (CAN) represented a

significant leap forward in spatial planning, and has become standard of care in cranial

neurosurgery. Applications of CAN have been transitioned from cranial neurosurgery to spinal

approaches since the mid-1990s. While techniques used for tracking instruments in a surgical

field have evolved significantly, from early acoustic devices to subsequent electromagnetic and

now passive/active optoelectronic tracking, registration techniques have evolved less

substantially. Nonetheless, the accuracy of spinal CAN techniques has collectively improved to

the point that a substantial body of literature exists supporting improved radiographic outcomes

relative to pre-CAN methodologies. However, adoption of spinal CAN remains limited, with

well-defined barriers: questionable accuracy and clinical benefit, significant workflow

disruption, steep learning curves, and high capital costs. In this thesis our objectives were to

identify settings and populations in which spinal CAN is most usefully applied, and examine

how a novel registration technique, optical topographic imaging (OTI), could obviate many of

the concerns plaguing current CAN techniques. To this end, we characterized practice patterns in

spinal CAN usage in a representative Canadian cohort, and additionally investigated the utility of

CAN intra-operatively as a training adjunct. This set the stage for subsequent work assessing the

feasibility of OTI for spinal image guidance in a number of common-use clinical settings.

Finally, as an exercise in translating not just OTI but other navigation techniques to clinical

fruition, we first explored and quantified error modalities afflicting all current navigation

techniques, and subsequently those pertinent to surface mapping techniques alone, and outlined

steps to be taken both by surgeons intra-operatively, and engineers in the

development/refinement phases, to identify and mitigate these errors.

While barriers to the adoption of CAN have been explored in a small number of surveys of

selected spinal surgeons, real-world practice patterns in spinal CAN usage have not been

investigated thoroughly. The gains in radiographic accuracy with CAN techniques are not often

sufficient to overcome poor workflow and high capital cost in many settings, however there may

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be specific applications and demographics for which the yields of CAN guidance may be

maximized. In a retrospective review of a prospectively-maintained database of patients

undergoing spinal instrumentation or percutaneous vertebroplasty/kyphoplasty, we found that

spinal CAN guidance was applied for less than one fifth of instrumented spinal fusions,

significantly more frequently by neurosurgeons than orthopedic spinal surgeons, and more often

in academic than community institutions. The use of CAN was associated with a reduction in the

need for revision surgery, though this conclusion is limited by the constraints of reviewing an

administrative database. Assessing the impact of spinal CAN on trainee education may provide

an additional application to justify the work needed to overcome typical barriers to adoption. In a

nationwide survey of spinal trainees, we found that almost two-thirds of orthopedic surgical and

neurosurgical trainees are not fully comfortable with the setup and use of spinal CAN, however

self-reported proficiency in instrumentation improves when CAN is applied, more so for

orthopedic surgical trainees. From a public health and resource allocation perspective, therefore,

these conclusions suggest that maximal clinical benefit of spinal CAN for patients may be

achieved if barriers to adoption are overcome among the primary practitioners performing the

bulk of instrumentation, orthopedic surgeons in the community. This requires significant

education at the trainee level, with additional emphasis on CAN operation for orthopedic and

neurosurgical spine fellows perhaps a worthwhile investment to improve adoption and ultimately

clinical outcomes.

Once we identified that, at least in the context of a single-payer health care system such as in

Canada, opportunities exist to maximize the utility of spinal CAN if specific barriers can be

overcome, we turned our attention to the translation of OTI for spinal CAN. As one of the

primary barriers to adoption is the unclear radiographic and clinical benefit relative to expert

freehand placement, we first explored how current CAN techniques are evaluated in the

literature, to allow optimal comparison with a novel technique, OTI, as well as guide future

studies of subsequent new technologies. We found that while most studies of spinal CAN assess

instrumentation accuracy as their primary outcome, less than half report radiographic accuracy,

with heterogeneous classification schemes used by those that do. Somewhat surprisingly, we

found that radiographic accuracy does not correlate with absolute quantitative engineering

accuracy, a more objective measure that can allow direct comparison between CAN techniques.

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This finding emphasized that CAN is an adjunct tool but is not a replacement for knowledge of

surgical anatomy, as we demonstrated quantitatively that surgeons compensate for navigation

error based on this knowledge. These conclusions also led us to propose that reproducible

literature comparisons among CAN techniques must therefore be made by reporting of both

engineering accuracy, as a measure of relative technical merit, as well as radiographic accuracy

coupled with clinical sequlae, as a measure of application accuracy.

To then explore OTI as a feasible alternate technique for spinal neuronavigation, we assessed the

accuracy and workflow of OTI-CAN in multiple common-use clinical contexts. In open posterior

thoracolumbar approaches, the most commonly applied portal for spinal instrumentation, we

quantified engineering accuracies of less than 2 mm translationally, and 2.5° angularly, in each

of the axial and sagittal planes, in keeping with reported values for current commercial CAN

techniques. Total time from registration to navigation averaged less than one minute,

significantly faster than existing techniques. We subsequently extended our analysis to

minimally-invasive thoracolumbar instrumentation, and found comparable accuracy in both

cadaveric and clinical studies with similar workflow benefits. Finally, we validated the accuracy

of OTI in the more mobile cervical spine, and found comparable translational and angular

accuracy to our thoracolumbar cohort, again with similar workflow.

Finally, we set about characterizing error modalities that arise first with all spinal navigation

techniques, and subsequently those unique to range scanning methodologies such as OTI. Errors

from non-segmental registrsation arise in all techniques dependent on a dynamic reference frame

(DRF), including OTI. We categorized non-segmental registration errors as those arising by

virtue of distance from the DRF alone, from surgical manipulation of elements distant to the

DRF, and from patient respiration-induced motion. We found that the largest of these errors

arose from distance to the DRF, with an increase in mean three-dimensional translational error of

more than 2 mm at 2 or more levels distant from the DRF in the cervical and lumbar spine. Error

from surgical manipulation and respiration-induced motion was less than 2 mm on average,

however with a not-insignificant proportion greater than 2 mm, the threshold commonly cited for

clinical significance. Errors unique to high-density surface scanning techniques, of which OTI is

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essentially the only example among current CAN techniques, relate to geometric congruence,

and were quantified for the first time as part of this thesis as being greater in the atlantoaxial and

subaxial cervical spine. Intra-operative maneuvers to avoid this pitfall, including bilateral

registration or extension of a unilateral exposure to include the base of the ipsilateral spinous

process, were identified.

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10.2 Unifying Discussion

The ‘multiple paper’ format of this thesis has allowed for the pertinent discussion around each

set of experimental work to be included within the corresponding data chapter (see Sections 3.5,

4.5, 5.5, 6.5, 7.5, 8.5, 9.5). In order to minimize redundancy, these final unifying thoughts are

directed towards the overarching theme and goal of this body of work: the knowledge translation

of a novel surgical technique, and paradigm for the interaction of operators with image guidance

systems.

This journey began with the realization that, in general terms, for spinal pathologies which

constitute a significant and growing health care burden, a technology exists, in the form of

computer-assisted navigation, which has largely been shown to improve the accuracy of a given

set of surgical procedures, however with a highly heterogeneous body of evidence that renders

the results questionable and difficult to interpret. This technology is also associated with other

drawbacks, particularly in workflow and efficiency, and requires a steep capital and mental cost

in the form of a significant learning curve, all of which beg the question of whether the capital

and intellectual investment are worthwhile for nebulous benefit. The aim of this thesis was

therefore to first clarify, for spinal pathologies, where the concept of computer-assisted

navigation is best applied. We know intuitively and from limited surveys that minimally-invasive

and deformity cases are likely the best surgical procedures to take advantage of CAN.(Choo et

al., 2008; Hartl et al., 2013) However, from a broader perspective, the question of which

populations are most likely to benefit from and maximize the advantages of CAN, must be

posed. This is the primary focus of the experimental work in Chapter 3. We identified for the

first time in the literature that, at least in a Canadian cohort, most spinal instrumentations are

performed in a community setting by orthopedic surgeons, thereby pinpointing a key

demographic which may be able to maximize the benefits of CAN for patients. Barriers to spinal

CAN adoption must be overcome particularly in this population; while multiple surveys have

identified barriers in sample groups biased towards orthopedic surgeons, they are largely

reflective of academic practitioners, and therefore do not necessarily reflect barriers experienced

by community orthopedic surgeons in Canada.(Hartl et al., 2013) Nonetheless, what these prior

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studies have suggested is that the largest impediments to adoption include a lack of proven and

readily-comparable accuracy, high capital cost, daunting training requirements, and intra-

operative radiation exposure. From a cost perspective, the utility of OTI for spinal image

guidance is not yet clear, as our work involved predominantly a research prototype system.

However, as dedicated intra-operative imaging devices, which constitute at least half of the cost

of a typical navigation setup, are avoided with this system, it is likely that overall capital

expenditure will be decreased. This remains to be demonstrated in future endeavours, however,

once commercialization of OTI platforms begins in earnest. We do know that OTI represents a

radiation-free technology intra-operatively, with therefore no exposure to both OR personnel and

intra-operatively to patients. However, one must be mindful that the total radiation cost to the

patient includes not only intra-operative exposure, but also any associated pre-operative imaging

for planning, and post-procedure imaging for instrumentation checks and follow up evaluation.

The experimental work in this thesis has not assessed or definitely shown lower overall radiation

cost to the patient relative to current CAN techniques. In our research paradigms assessing OTI

feasibility, a post-operative CT scan was required for accuracy quantification with high

precision. In routine clinical practice, however, a plain intra-operative XR following

instrumentation placement might often suffice for placement verification without quantification

of millimetric accuracy. Future studies of OTI in a more routine clinical workflow are therefore

required to assess whether this technique reduces the overall burden of radiation to the patient

during their entire clinical course. With regards to onerous training requirements as a barrier to

adoption of current CAN techniques, several studies have demonstrated significant learning

curves for navigation systems, with improvement in efficiency and accuracy demonstrated after

35-50 cases though with no robust assessment of whether improvement might have occurred at a

lower case volume.(Ryang et al., 2015; Wood & McMillen, 2014) In our initial analysis of OTI

feasibility for open posterior thoracolumbar exposures (Chapter 5), no significant operator

learning curve was demonstrated with regards to accuracy or efficiency. However, our studies

were performed by a single surgeon and trainee with involvement in the development of the

technology, hence it is not unfathomable that these trialists had greater upfront expertise than

might otherwise be expected from a truly novice user. The other possible perspective, of course,

is that the workflow improvements in the registration process significantly flattern the learning

curve of OTI relative to other CAN techniques. This remains to be addressed experimentally in

future multicenter comparative studies of commercialized OTI systems.

216

The primary rationale for CAN systems remains, however, improved accuracy through virtual

visualization of subsurface structures. The decision by a clinician to adopt any novel technique,

be it a diagnostic tool or therapeutic modality, is made by careful comparison of relative merits

and drawbacks. Meaningful comparisons can be made only on a level playing field, that is, with

homogeneity in classification and reporting mechanisms for the parameters relevant to

benchmarking. To date, uniformity in classification systems have not existed in the context of

spinal CAN systems. For the first time in the literature, we devised and demonstrated

quantitatively the rationale for a mechanism of reporting navigation accuracy based on both

engineering and clinicoradiographic parameters which, if applied readily in future investigations,

will allow practitioners to easily compare between novel navigation techniques. One immediate

potential application of this proposed reporting scheme is to compare the accuracy of robotically-

actuated navigation with freehand-actuated navigation. As discussed in Chapter 2, robotic

systems are on the immediate horizon of current CAN paradigms, with demonstrated

improvements in accuracy relative to traditional freehand techniques but minimal evidence to

demonstrate any additional utility over the current standard of freehand image guidance.(Roser et

al., 2013) However, uniform reporting remains a significant barrier, with new subjective

classification schemes being proposed even as of this writing.(Rajasekaran, Bhushan, Aiyer,

Kanna, & Shetty, 2018) Nonetheless, using our proposed reporting mechanism we demonstrated

for the first time in the literature that OTI is a faster yet comparably accurate and therefore

feasible CAN technique in relation to the current complement of technologies.

Another barrier to the routine usage of CAN has been its role in trainee education. While

multiple studies exist integrating CAN into phantom/cadaveric/virtual-reality simulators, with

evidence supporting subjective acceptance by trainees as an educational tool, if not significant

objective improvement in assessment metrics, there have been no studies to date assessing the

intra-operative utility of CAN for training. Anecdotally, CAN is often derided particularly by

non-adopters, as being a ‘crutch’ for inexperienced surgeons, hindering the acquisition of tactile

and visual landmarks for safe freehand instrumentation as is often the capability of expert senior

surgeons. However, an alternative perspective is that CAN, when applied correctly with

217

pedagogical intent as a verification tool rather than first-line guidance, may be a useful training

adjunct.(Manbachi et al., 2014) We demonstrate this for the first time in a nationwide survey of

surgical trainees, however with only self-reported metrics rather than true objective assessments.

Part of the process of training new CAN users and flattening their learning curve involves

identifying pitfalls of navigation systems, and knowing how to avoid them or otherwise correct

for them pre- and intra-operatively. To this end, common errors associated with all CAN

techniques including OTI (Chapter 8), as well as to OTI uniquely (Chapter 9), were identified.

More importantly, predictors of increased error from these modalities were characterized, to

allow surgeons to identify them and subsequently engage in mechanisms to address them. While

some of these error modalities may be considered common sense on a trial-and-error basis for

some surgeons, characterization by absolute quantification is often necessary to appreciate their

magnitude and mechanisms for avoidance, and we have done so for the first time here.

It is my hope that, collectively, the body of work presented in this thesis demonstrates the

feasibility of OTI as a safe and efficient technique for spinal CAN to allow increased adoption

amongst key players, and ultimately improved outcomes for their patients. Moreover, I hope to

have demonstrated that barriers to the knowledge translation of any novel medical technologies

can be overcome with a similar set of analyses, beginning with characterization of existing usage

patterns to identify key demographics for adoption, along with exploration and quantification of

specific shortcomings, and standardization of reporting schema where significant heterogeneity

exists.

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10.3 Future Directions

Several avenues of largely epidemiological experimental work have presented themselves based

on limitations of our existing analyses. We explored usage patterns of spinal CAN among

Ontario surgeons, identifying specific demographics for which barriers to adoption must be

overcome by OTI and other future technologies. While we demonstrate that OTI-CAN represents

a significant improvement in workflow efficiency and therefore likely cost-effectiveness relative

to current systems, particularly given the lack of a requirement for intra-operative imaging

devices, this will need to be investigated definitively in a larger prospective cohort, accounting

for complications and reoperation costs following OTI-guided procedures. Similarly, while OTI

has no explicit radiation cost intra-operatively to patient or surgeon, the overall radiation

exposure to the patient including pre- and post-surgical imaging has not yet been evaluated, and

must be done with prospective observational dosimetric cohort studies. Comparison of the

learning curve between OTI and other CAN techniques also requires prospective multi-center,

multi-surgeon comparative cohorts.

The ability of structured light imaging to rapidly generate three-dimensional topographic maps of

a scanned surface lends itself well to numerous other avenues for potential future exploration. In

the current paradigm of open spinal surgery, possibilities include the application of OTI for real-

time continuous anatomical and instrument tracking, for machine learning-based adaptation to

level localization, and for the scanning of non-osseous surfaces to eliminate the need for intra-

operative imaging. Longer term possibilities include the natural extension of OTI-based

techniques to other non-spinal surgical applications.

In all current paradigms of frameless stereotactic navigation, a dynamic reference frame (DRF) is

required for relative instrument tracking and for maintenance of the initial registration. If the

pose of exposed surgical anatomy could be tracked in real-time, however, this would permit

updating of the initial registration simultaneously. Similar principles have already been applied

219

in the use of structured light illumination to update cranial registrations to compensate for intra-

operative brain shift, albeit at non-contiguous intervals (Chapter 2).(DeLorenzo et al., 2010; Paul

et al., 2009; Sun et al., 2005) With increases in the refresh rates of the visible-band cameras

responsible for capturing the deformed structured light illumination, and with appropriate

computing power, an exposed surface could be tracked at a frequency on the order of 10-30 Hz.

More intriguingly, surgical tools within the detection range of stereocameras could also be

tracked at the same frequency using the same visible band cameras, using object identification

algorithms to isolate and track a given surgical instrument. Modern infra-red (IR) optical

instrument tracking systems update at a frequency of 20-60 Hz,(NDI, 2018b) hence direct

visible-band instrument tracking at a comparable frequency would not result in any appreciable

lag to the operator. If computing power is insufficient to update registrations at this frequency, a

hybrid system of IR tool tracking within the visible-band camera volume may also be

considered, requiring an additional calibration to mesh the coordinate spaces of the IR and

visible cameras. Either technique would eliminate the need for a DRF and thereby obviate one of

the major pain points identified in current navigation workflows, as well as eliminate a

significant source of instrument-tracking error (Chapter 8).(Choo et al., 2008; Hartl et al., 2013)

The ability of OTI to segment and track vertebral levels individually may also be applied to the

real-time tracking of alignment intra-operatively. Particularly in long-segment deformity

correction procedures, a common-use scenario for spinal CAN, the final desired alignment is

decided a priori based on pre-operative imaging parameters and the necessary operative

maneuvers planned accordingly, i.e. what type of osteotomies may be required and at which

levels to achieve the desired correction. Intra-operative confirmation of achievement of the

desired alignment, however, remains a mainstay of traditional fluoroscopy, with repeated

imaging required to iteratively confirm the required correction at each operated level in order to

attain the desired global alignment. Real-time optical tracking of each segmental vertebral level

allows independent registration to the corresponding imaging dataset; changes in intervertebral

alignment may therefore be computed by repeat OTI registration, obviating the need for ongoing

fluoroscopic imaging. Changes in global alignment may then be computed by summating the

segmental changes in intervertebral alignment, to provide a radiation-free snapshot of global

alignment. Particularly in the context of adolescent idiopathic scoliosis, extension of OTI to real-

220

time segmental alignment tracking has the potential to significantly reduce radiation burden as

well as time cost, major topics of ongoing investigation in the adolescent spine field.(Presciutti,

Karukanda, & Lee, 2014; Ughwanogho, Patel, Baldwin, Sampson, & Flynn, 2012)

Another feasible application of vertebral imaging using OTI involves the localization of levels

for surgical site identification. The target level to be operated on is typically identified using

some combination of intra-operative localization, using fluoroscopy or mobile XR, and pre-

operative placement of radio-opaque markers which are then captured on pre-operative

imaging.(Hsiang, 2011; Hsu et al., 2008) Localization remains most challenging in the mid-

thoracic region or with poor visibility on XR imaging, as levels must be counted from the

occipito-cervical junction (‘top-down’) or lumbosacral junction (‘bottom-up’). The rate of

wrong-level surgery in the United States is approximately 0.03% which, while small, remains

unacceptably high for what should be a ‘never’ incidence.(Mody et al., 2008) With advances in

machine learning algorithms for object recognition,(Nasrabadi, 2007) as seen most evidently in

the consumer electronics arena with applications such as Google Lens, an opportunity exists for

a potential application of OTI in target level identification. A library of structured light-based

surface maps of various spinal levels may be constructed, with computation of relevant

differentiating parameters such as laminar and spinous process dimensions, and incident angles

of the lamina relative to cranial and caudal levels as well as to the spinous process; free

parameters would also be available, to allow application of a standard neural network

framework. Deep learning algorithms may then be applied to identify a set of parameters reliably

and uniquely identifying spinal levels, with testing and validation performed on a distinct set of

scanned vertebrae. OTI imaging might then be applied to an open posterior exposure to identify

the level without additional fluoroscopic imaging, or as a confirmatory measure if fluoroscopic

identification is equivocal or difficult. This is likely most appropriate only in larger open

exposures; in most minimally-invasive approaches, the skin incision must be tailored to the

target of interest, hence XR target localization prior to exposure remains a requirement. Certainly

this approach may be feasible, however, with precedent set by a similar algorithm employing a

2D-3D registration of intra-operative lateral XR to pre-operative CT for automatic level

confirmation, once levels had been reliably identified on the CT initially as the ‘gold

standard’.(Lo et al., 2015)

221

Continuing along the established path of spinal surgery, OTI may also be applied to image other

instruments in the operative field. A major limitation of OTI-CAN in its current form is the lack

of integrated verification of instrumentation placement accuracy; automatically-registered CAN

techniques require an intra-operative imaging device, which may be used post-instrumentation to

assess the accuracy of placed hardware, and revise intra-operatively if necessary. A scenario can

easily be envisioned however, with minimal software modification, whereby structured light

illumination may be applied to scan a pedicle screw as it is partially inserted into a target

cannulated tract. If the known screw diameter and length are specified, a virtual projection of the

imaged screw may then be placed onto the concomitantly-registered spine imaging, and

automatically advanced into final position in the imaging space to provide a real-time ‘virtual’

check on implant placement accuracy. The trajectory of pedicle screws is unlikely to change

significantly after the screw has been partially threaded into the cannulated tract, particularly if

the tract has been tapped previously,(Erkan et al., 2010) hence errors in the virtual projection can

reasonably be assumed to be minimal. Instrumentation verification may therefore be performed

in a radiation-free fashion, obviating the need for a bulky and costly intra-operative imaging

device or post-operative CT, with its associated radiation burden.

Adaption of OTI for the scanning of soft-tissues in the context of spinal surgery is also within the

realm of short-term possibility. Registration in its current prototype format is performed via

scanning of rigid osseous anatomy which, while readily accessible for open (Chapter 5) and even

mini-open MIS (Chapter 6) approaches, precludes fully percutaneous approaches to the spine.

Structured light-based 3D scanning of the back has already been described in the context of non-

invasive methods for evaluating patients with idiopathic scoliosis,(Mínguez et al., 2007) using

surface maps of the dorsal skin to compute metrics of axial deformity and global symmetry as

surrogates for traditional radiographic alignment parameters. One can imagine performing a

similar maneuver on a patient positioned prone on an operating table, or even coupling an OTI

scanner to a motorized rotatory frame similar to those in isocentric fluoroscopy or cone-beam CT

devices, to generate a partially circumferential surface map of the dorsal skin anatomy. Applying

a correction function to adjust for skin shift from supine pre-operative imaging to prone

222

operative positioning, registration may then theoretically be achieved using the skin surfaces

alone, allowing image-guided fully percutaneous procedures. With these advancements, there

would remain no limitations to OTI for spinal applications relative to current CAN techniques.

It is without question that OTI can readily be translated to other non-spinal surgical applications

as well. Our group is in the process of developing and validating OTI for cranial

neuronavigation; further developments will integrate real-time brain-shift compensation

algorithms on a much more efficient scale than those described previously in the literature

(Chapter 2). Applications in oromaxillofacial reconstruction have been described,(Kau,

Richmond, Incrapera, English, & Xia, 2007) as well as in facial morphology assessment for

infants with cleft palates.(Li et al., 2013) Our group is fortunate to have access to one of the

largest pediatric neurosurgical and plastic surgical units in Canada. One can imagine the practical

applications of OTI in, for instance, real-time updating of cephalometric parameters in patients

undergoing reconstruction for craniosynostoses, obviating the need for costly patient-specific

templates and moulds to guide intra-operative repair. 3D scanning in these patients has already

been described for post-operative routine follow up; it is but a small leap to extend this

application intra-operatively for real-time updates.(Tenhagen et al., 2016) Miniaturized

structured light-based 3D range scanning has also been described for laparoscopic applications to

intra-abdominal organ mapping, of significant utility in guiding liver resections.(Maurice,

Albitar, Doignon, & de Mathelin, 2012)

Looking to the more distant future, robotic actuation of instruments traditionally guided freehand

has begun to take hold particularly in spinal surgery (see Section 2.2.3). While no clear benefit

over freehand navigation has yet been demonstrated, one prevalent perspective is that robotic

actuation flattens the learning curve and maximizes the efficiency of image guidance for non-

CAN users, with perhaps negligible benefit for operators already proficient in freehand

navigation. With increasing advancements in OTI and other machine vision technologies

however, automated robotic instrument actuation using machine vision for real-time bony and

soft-tissue tracking and registration may represent a possible view of the future operating room.

223

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