Magnetic Resonance Imaging Detected Intraplaque Hemorrhage … · 2017-01-10 · Dr. Navneet Singh contributed to the planning and execution of the study in Chapter 2 and is listed

Magnetic Resonance Imaging Detected Intraplaque Hemorrhage in Non-Stenotic Carotid Artery Atherosclerotic

Disease of Asymptomatic Diabetic Patients

by

Tishan Maraj

A thesis submitted in conformity with the requirements for the degree of Masters in Clinical Sciences

Institute of Medical Sciences University of Toronto

© Copyright by Tishan Maraj 2016

ii

Magnetic Resonance Imaging Detected Intraplaque Hemorrhage

in Non-Stenotic Carotid Artery Atherosclerotic Disease of

Asymptomatic Diabetic Patients

Tishan Maraj

Masters in Clinical Sciences

Institute of Medical Sciences

University of Toronto

2016

Abstract

Cerebrovascular disease represents a major cause of death globally. It is directly related to

atherosclerotic development in the carotid arteries where risk is mainly assessed by measurement

of stenosis. Diabetic patients are predisposed to cerebrovascular event occurrence and

atherosclerotic development. An advanced feature of atherosclerotic disease is intraplaque

hemorrhage (IPH), which increases the risk of such events.

In this study, magnetic resonance imaging techniques were used to define an optimal method for

measuring IPH. This method was applied to a cohort of diabetic patients without carotid artery

stenosis, to find the prevalence of IPH, which was compared with carotid artery wall

measurements to determine its related effects.

This thesis provides evidence that advanced features of atherosclerosis, in the form of IPH, can be

found even when no carotid stenosis is present and may provide insight into the added risk borne

by diabetic patients.

iii

Acknowledgments

My greatest thanks and appreciation go to Dr. Alan R. Moody, my supervisor and mentor. His

knowledge, insight and ability to challenge my perspectives and decision making capabilities have

been inspirational and set me on a career path that 4 years ago I would not have thought possible.

I wish to sincerely thank Dr. David Jenkins and Dr. Adria Giacca who helped guide me through

this journey as members of my committee. Your support, feedback, suggestions and attention to

detail were instrumental in elevating the quality of my work.

To my fellow lab members, who I prefer to call my friends, the experiences that we have gone

through have forever shaped my life. I am indebted to Helen, Navneet and Mariam who gave me

the support and advice that I needed to move forward whenever I stumbled. The contributions of

Vivek, Tina, Omodele, Stephanie, Pascal, Rasha, Bowen and Marilyn were important to both my

research as well as my personal development. I also extend thanks to James who initiated my

‘circle drawing’ abilities which formed the basis of my research.

Special thanks go especially to my parents whose support and encouragement remain a

cornerstone of my life. To Jitin, Simi, Hardeep, Somant and little Vasana, I extend thanks for

always being at my side.

iv

Contributions

Dr. Tishan Maraj (author) was responsible for the preparation of this original thesis. All of the

work presented henceforth including the planning, execution, analysis and writing of the original

research was performed by the author. He is listed as Reader 1 in Chapter 2. The following

contributions to the work in this thesis are formally and inclusively acknowledged:

Dr. Alan R. Moody (Supervisor) was the primary mentor of the author. He guided the author

through this body of work and contributed to the planning, execution and analysis of the studies as

well as the thesis preparation. He is listed as Reader 2 in Chapter 2.

Dr. David Jenkins (Advisory Committee Member) guided the author through the planning of this

work and the thesis preparation.

Dr. Adria Giacca (Advisory Committee Member) guided the author through the planning of this

work and the thesis preparation.

Dr. Pascal N. Tyrrell contributed to the analysis of the studies in Chapters 2 and 3.

Dr. Navneet Singh contributed to the planning and execution of the study in Chapter 2 and is

listed as Reader 3.

Dr. Mariam Afshin assisted with the execution of the study in Chapter 2.

Laura Chiavaroli assisted with the execution of the study in Chapter 3.

Dr. Thayalasuthan Vivekanandan assisted with the execution of the studies in Chapters 2 and 3.

Dr. Helen Cheung assisted with the thesis preparation.

v

Table of Contents

Contents

Acknowledgments .......................................................................................................................... iii

Contributions .................................................................................................................................. iv

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................... x

List of Figures ................................................................................................................................. xi

List of Appendices ........................................................................................................................ xiii

List of Abbreviations .................................................................................................................... xiv

Chapter 1 Literature Review............................................................................................................ 1

Intraplaque Hemorrhage – Thesis Overview .............................................................................. 2 1

1.1 Cerebrovascular Disease...................................................................................................... 3

1.1.1 Overview ................................................................................................................. 3

1.1.2 Classification ........................................................................................................... 3

1.1.3 Ischemic Stroke ....................................................................................................... 4

1.1.4 Factors Related to Ischemic Stroke ......................................................................... 5

1.1.5 Carotid Artery Anatomy .......................................................................................... 6

1.2 Atherosclerosis .................................................................................................................. 12

1.2.1 Defining Atherosclerosis ....................................................................................... 12

1.2.2 Classification of Atherosclerotic Lesions .............................................................. 13

1.2.3 Measuring Carotid Artery Atherosclerotic Disease............................................... 15

1.2.4 Effects of Atherosclerotic Lesions ........................................................................ 18

1.3 Intraplaque Hemorrhage .................................................................................................... 20

1.3.1 Significance of IPH ............................................................................................... 20

vi

1.3.2 Pathophysiology .................................................................................................... 20

1.3.3 Consequences of IPH ............................................................................................ 21

1.3.4 Relevance of IPH Study ........................................................................................ 22

1.4 Magnetic Resonance Imaging ........................................................................................... 23

1.4.1 Overview – MRI Basics ........................................................................................ 23

1.4.2 MRI sequences ...................................................................................................... 24

1.4.3 3D-MRIPH Sequence and Detection of IPH ......................................................... 26

1.4.4 VesselMASS – Carotid MR Image Analysis Software ......................................... 28

1.5 Diabetes ............................................................................................................................. 30

1.5.1 Disease Impact ....................................................................................................... 30

1.5.2 Diabetes and Cerebrovascular Disease .................................................................. 30

1.5.3 Vascular Complications ......................................................................................... 31

1.5.4 Atherosclerosis and Diabetes................................................................................. 31

1.5.5 Diabetes and IPH ................................................................................................... 32

1.6 Hypothesis and Aims ......................................................................................................... 33

1.6.1 Hypothesis ............................................................................................................. 33

1.6.2 Aims ...................................................................................................................... 33

A Semi-Automated Method for Detecting and Quantifying Carotid Artery Vessel Wall 2

High Signal with 3-Dimensional Magnetic Resonance Imaging ............................................. 35

2.1 Introduction ....................................................................................................................... 35

2.2 Methods ............................................................................................................................. 39

2.2.1 Study Sample ......................................................................................................... 39

2.2.2 MRI Protocol ......................................................................................................... 39

2.2.3 Defining an optimal threshold ............................................................................... 41

2.2.4 Gold Standard ........................................................................................................ 42

2.2.5 Direct Area Comparison ........................................................................................ 42

vii

2.2.6 Statistical Analysis ................................................................................................ 43

2.3 Results ............................................................................................................................... 44

2.3.1 Study Characteristics ............................................................................................. 44

2.3.2 Defining an Optimal Intensity Ratio ..................................................................... 44

2.3.3 Effect of SCM Intensity Heterogeneity ................................................................. 46

2.3.4 Effect of B0 Inhomogeneity on the SCM Intensity ............................................... 46

2.3.5 Effects on High Signal Volume Quantification ..................................................... 49

2.3.6 Direct Area Comparison ........................................................................................ 49

2.4 Discussion .......................................................................................................................... 52

2.4.1 Limitations ............................................................................................................. 53

2.4.2 Future Directions ................................................................................................... 54

2.5 Conclusion ......................................................................................................................... 55

Intraplaque hemorrhage in Type 2 Diabetic Patients and its Association with Non-Stenotic 3

Carotid Artery Wall Volume .................................................................................................... 57

3.1 Introduction ....................................................................................................................... 57

3.2 Methods ............................................................................................................................. 59

3.2.1 Study Sample ......................................................................................................... 59

3.2.2 Carotid Artery IMT Measurement ......................................................................... 59

3.2.3 MRI Protocol ......................................................................................................... 60

3.2.4 Carotid Artery Image Analysis .............................................................................. 60

3.2.5 Covariates and Factors........................................................................................... 61

3.2.6 2D and 3D Wall Volume Measurement Comparison ............................................ 63

3.2.7 Statistical Analysis ................................................................................................ 63

3.3 Results ............................................................................................................................... 67

3.3.1 Patient Characteristics ........................................................................................... 67

3.3.2 Intraplaque Hemorrhage ........................................................................................ 67

viii

3.3.3 Effect of IPH on Carotid Wall Volume ................................................................. 68

3.3.4 Vessel Wall Volume and Carotid IMT .................................................................. 74

3.3.5 3D and 2D MRI Volume Comparison ................................................................... 77

3.4 Discussion .......................................................................................................................... 80

3.4.1 Limitations ............................................................................................................. 81

3.4.2 Future Directions ................................................................................................... 82

3.5 Conclusion ......................................................................................................................... 83

General Discussion ................................................................................................................... 85 4

4.1.1 Development of the Hypothesis and Related Aims ............................................... 87

4.1.2 Measurement of MRI detected IPH ....................................................................... 88

4.2 IPH Prevalence in a Diabetic Cohort ................................................................................. 90

4.3 The Association between IPH and Carotid Wall Volume ................................................. 92

4.4 Novelty of work ................................................................................................................. 96

4.4.1 Semi-automated method for identification of IPH ................................................ 96

4.4.2 Determination of the Carotid Artery Segment ...................................................... 97

4.4.3 Carotid Artery Wall Volume Associations ............................................................ 97

4.4.4 Investigation of a Unique Cohort .......................................................................... 98

4.4.5 3D and 2D MRI Sequences ................................................................................... 98

4.4.6 Carotid Artery Wall Volume and IMT .................................................................. 99

4.5 Limitations ....................................................................................................................... 100

4.5.1 Study 1 – Measurement of MRI detected IPH .................................................... 100

4.5.2 Study 2 – IPH Prevalence and Associations with VWV in a Diabetic Cohort ... 101

4.6 Conclusions ..................................................................................................................... 103

Future Directions .................................................................................................................... 106 5

5.1.1 3D MRI Sequence Use ........................................................................................ 106

5.1.2 Fully Automated Image Processing Protocol ...................................................... 106

ix

5.1.3 Follow Up of the Patient Cohort ......................................................................... 106

5.1.4 Comparison with a Non-Diabetic Population ...................................................... 107

5.1.5 Development of Carotid Artery Wall Volume Reference Value ........................ 107

5.1.6 Carotid IMT as a Screening Tool for Carotid MRI ............................................. 108

References or Bibliography ......................................................................................................... 109

Appendices .................................................................................................................................. 121

x

List of Tables

Table 1. Categories of atherosclerotic disease.............................................................................. 14

Table 2. Parameters for the 2-dimensional and 3-dimensional sequences used in this thesis. ...... 27

Table 3. Review of previous studies describing 3D MR-detection of intraplaque hemorrhage.. . 37

Table 4. Patient demographic data and carotid artery characteristics. .......................................... 45

Table 5. Sensitivity, specificity and Youden index by intensity ratio and ROI Method.. ............. 48

Table 6. Demographic data and characteristics of study population. ........................................... 69

Table 7. Risk factors and measurements associated with patient and carotid artery characteristics..

....................................................................................................................................................... 71

Table 8. Associations between risk factors and IPH.. .................................................................. 72

Table 9. Associations between risk factors and the outcomes of: Model 1 - vessel wall volume

(VWV), Model 2 - mean-maximum carotid intima media thickness (mm-CIMT) ....................... 75

Table 10. Multivariable linear regression model for prediction of VWV from mm-CIMT, gender,

age and BMI. ................................................................................................................................. 77

xi

List of Figures

Figure 1. Diagram of the thoracic aorta and its main branches. ...................................................... 8

Figure 2. 3-dimensional angiographic reconstruction of the right and left carotid arteries. ........... 9

Figure 3. Cross sectional structure of the arterial wall showing its layers. .................................. 11

Figure 4. Defined stages of an atherosclerotic plaque. .................................................................. 19

Figure 5. 3-dimensional MRI-detected intraplaque hemorrhage................................................... 29

Figure 6. Repeated 3D-T1w GRE reformatted axial image.. ........................................................ 38

Figure 7. 3D-T1w GRE semi-automated image analysis protocol. ............................................... 40

Figure 8. ROC curves for intensity ratio determination. .............................................................. 47

Figure 9. Box and whisker plots. .................................................................................................. 50

Figure 10. Intraclass Correlation Coefficient (ICC) variation between manual and semi-

automated measurements across intensity ratios for 5 carotid vessels.. ........................................ 51

Figure 11. Summary of image processing protocol. ..................................................................... 62

Figure 12. Detection of IPH in consecutive axial images. ........................................................... 65

Figure 13. Axial images with detected IPH from 3 different carotid arteries in the absence of

stenosis. ......................................................................................................................................... 66

Figure 14. Distribution of IPH by carotid artery side and location. ............................................. 70

Figure 15. Error bars comparing mean carotid wall volumes and mean carotid IMTs.. .............. 73

Figure 16. Scatterplot of mean-maximum carotid IMT versus vessel wall volume..................... 76

xii

Figure 17. Mean difference plot showing 3D to 2D comparisons of the measured carotid wall

volume.. ......................................................................................................................................... 78

Figure 18. Location of the carotid artery bifurcation with 2D MRI acquisition.. ........................ 79

xiii

List of Appendices

Appendix 1. Image quality assessment scale.............................................................................. 121

Appendix 2. Intraclass correlation coefficients for 2D-3D volume comparisons. ..................... 121

xiv

List of Abbreviations

2D Two Dimensional

3D Three Dimensional

AHA American Heart Association

AUC Area Under the Curve

BMI Body Mass Index

CT Computed Tomography

CUS Carotid artery Ultrasound

CNS Central Nervous System

CV Coefficient of Variation

CVA Cerebrovascular Accident

CE Contrast Enhanced

CCA Common Carotid Artery

ECA External Carotid Artery

FA Flip Angle

GEE Generalized Estimating Equation

GRE Gradient Recalled Echo

HDL High Density Lipoprotein

ICA Internal Carotid Artery

IMT Intima Media Thickness

ICC Intraclass Correlation Coefficient

IPH Intraplaque Hemorrhage

LDL Low Density Lipoprotein

mm-CIMT Mean-Maximum Carotid Intima Media Thickness

MRI Magnetic Resonance Imaging

MRIPH Magnetic Resonance Imaging for Intraplaque Hemorrhage

NASCET North American Symptomatic Carotid Endarterectomy Trial

RBC Red Blood Cell

REB Research Ethics Board

RF Radiofrequency

RIND Reversible Ischemic Neurologic Deficit

xv

ROC Receiver Operating Characteristic

ROI Region Of Interest

ROS Reactive Oxygen Species

SCM Sternocleidomastoid Muscle

SD Standard Deviation

TIA Transient Ischemic Attack

TR Repetition Time

TE Echo Time

TOF Time Of Flight

T1W T1 Weighted

T2W T2 Weighted

US Ultrasound

VA Vertebral Artery

VWV Vessel Wall Volume

YI Youden Index

1

Chapter 1

Literature Review

2

Intraplaque Hemorrhage – Thesis Overview 1

The role of carotid artery vessel wall intraplaque hemorrhage (IPH) in cerebral ischemic events

was suggested as early as the 1930s, where it was thought to play a major role in arterial occlusion

and subsequent patient mortality1–3

. At the time, atheroma formation and its contribution to

vascular disease and end organ damage was being realized. The link between plaque hemorrhage

and atherosclerotic progression was being investigated through examination of coronary artery

histopathology specimens. In these coronary arteries, hemorrhages within the vessel walls were

noted near the origins of arterial branches, causing compression of nearby smaller vessels. As a

result, IPH was suggested to be an important factor of atherosclerotic disease.

Greater evidence was provided by Lusby et al in 1982, with their prospective study of carotid

endarterectomy specimens4. Their patient population included those with previous

cerebrovascular events, as well as an asymptomatic group. It was theorized that IPH changed the

natural progression of atherosclerotic development, leading to event occurrence. They noted an

increased frequency of IPH incidence in the carotid plaque specimens within the symptomatic

group compared with the asymptomatic group, as well as with plaques contralateral to events in

the brain. They found a significant correlation between onset of symptoms related to

cerebrovascular disease and the presence of hemorrhage within the removed plaque. These studies

provided evidence of the importance of IPH as a predictor of cerebrovascular events.

Interest in IPH increased around the turn of the 21st century with advances in techniques for in

vivo visualization of the carotid atherosclerotic plaque, namely magnetic resonance imaging

(MRI)5–7

. High resolution MRI was able to characterize the atherosclerotic plaque, delineating

IPH and other plaque components. Scientists once again began exploring the path trodden by

Lusby, looking at the relationship between IPH, plaque progression and cerebrovascular events.

Increasing evidence exists demonstrating the correlation between IPH and cerebrovascular events,

but translation into clinical practice has yet to be realized. Little is known about the early

formation of IPH and its effect in patients with minimal or no carotid artery stenosis– an area of

study which might help to increase the understanding of IPH as a biomarker and assist with its

clinical adoption.

3

1.1 Cerebrovascular Disease

1.1.1 Overview

Cerebrovascular accidents (CVAs) or strokes account for a major cause of mortality worldwide

and the 4th

major cause in Canada 8. Of all Canadian deaths, 6% are due to stroke. This

corresponds to an estimated 50 000 strokes each year, or 1 every 10 minutes. The economic

implications are significant, costing Canada $3.6 billion each year in medical services and

diminished productivity9.

The term ‘stroke’ is considered to originate from Hippocrates, who described it as “apoplexy”,

stemming from the Greek word “apoplexia”, suggesting the person had been ‘struck down’10

. The

definition of a stroke is not consistent in clinical practice or research, even though it has such a

global impact. Its description and scope has changed over time, with more recent definitions being

based on advances in the understanding of pathophysiology, improvements in available

neuroimaging techniques as well as its usefulness in guiding patient treatment.

1.1.2 Classification

Stroke is the most debilitating manifestation of cerebrovascular disease and is attributed to an

acute focal injury of the central nervous system (CNS) from a vascular cause 11

. These injuries or

CVAs are considered events, and are broadly classified as ischemic, resulting from a thrombo-

embolic episode, or hemorrhagic, where bleeding occurs. A stroke encompasses a broad range of

injuries causing infarction of the CNS. CNS infarction itself is defined as brain, spinal cord or

retinal cell death attributable to ischemia based on:

1. Evidence of such infarction on pathological, imaging or objectively otherwise in a defined

vascular distribution, or

2. Clinical evidence of such infarction based on symptoms persisting for at least 24 hours, or

until death, with other etiologies excluded.

Older classifications were based on the resulting focal or general cerebral dysfunction that

resulted, as well as the temporal relationship with symptoms. These were divided into transient

ischemic attacks (TIAs) where symptoms resolved within 24 hours, reversible ischemic

neurological deficits (RINDs) where symptoms resolved within 24 hours to 7 days and finally,

4

strokes. More recent definitions, however, incorporate the pathophysiological understanding of

the event, by specifically attributing classes based on the tissue damage involved. Current

categorization of stroke include infarction related to ischemic stroke, silent CNS infarction,

intracerebral hemorrhage, silent cerebral hemorrhage, subarachnoid hemorrhage, central venous

thrombosis and causes not otherwise specified. Each of these divisions is also defined

individually.

Considered a reversible event, TIAs are brief episodes of neurological dysfunction that result

from focal brain, spinal cord or retinal ischemia without acute infarction12

. They were classically

defined by the time frame of 24 hours, but it was found that 30-50% of events that fell within this

classification actually demonstrated infarction on diffusion-weighted MRI. The 24 hour cutoff is

still applicable, but is considered secondary to the presence of infarction when appropriate

neuroimaging is available, due to the variability in ischemic time needed. With the presence of

infarction, the event would be considered a stroke. As a result, the term RIND was rendered

obsolete and its use has been generally discontinued since the 1970s. The advent of thrombolytic

therapy and similar treatments at early stages added to the rationale for redefining strokes and

TIAs, as management became directed at the cause of ischemia and not just based on whether

infarction had occurred.

Symptoms that constitute an event are quite variable in occurrence and severity with some of the

more frequent ones including visual disturbances, dysphasia, dysarthria, motor and sensory

changes. As TIAs are reversible events, symptom assessment and diagnosis becomes very

important for early management and evaluating the risk of future events. After a TIA, 10-15% of

patients have a stroke within 3 months, with half of these occurring within 48 hours.

1.1.3 Ischemic Stroke

Ischemic strokes are due to obstruction of an artery supplying blood to the brain, primarily

through thromboembolic disease. It is defined as “an episode of neurological dysfunction caused

by focal, cerebral, spinal or retinal infarction”11

. Hemorrhagic strokes meanwhile, refer to

bleeding within the brain from arterial rupture, commonly aneurysms. In both types, there is an

interruption of the normal arterial supply to the brain tissue resulting in neuronal death.

5

The ischemic type is predominant in Canada accounting for 85% of all strokes9. Further subtyping

of ischemic stroke can be done where they are categorized by cause – the main ones being related

to large artery atherosclerosis, cardio-embolism, small vessel occlusion, stroke of other

determined etiology and stroke of undetermined etiology13

. Much emphasis is placed on large

artery atherosclerosis, mainly the carotid arteries, which supply the majority of the cerebral

circulation.

1.1.4 Factors Related to Ischemic Stroke

Carotid artery atherosclerotic disease and the resulting stenosis caused by plaque buildup, forms

the basis for the surgical management of carotid disease in stroke prevention. The North

American Symptomatic Carotid Endarterectomy Trial (NASCET) concluded that there was a

large benefit of endarterectomy for symptomatic patients with severe stenosis (>70%), while there

was modest benefit in high-moderate stenosis (50-69%)14

. The NASCET measure of stenosis

compares the narrowest diameter caused by a lesion within the carotid artery against the diameter

of a normal segment of the internal carotid artery (ICA) and reports the result as a percentage. The

result of this study has since influenced clinical practice, shaping treatment guidelines worldwide.

As such, the main indications for surgical versus medical management are determined primarily

by the degree of stenosis and symptomatology of each individual patient.

More recently, greater emphasis has been placed on the constituents of atherosclerotic plaque that

are responsible for stenosis. Current thinking has leaned toward the relationship of specific plaque

constituents with plaque vulnerability and their propensity for causing a cerebrovascular event15

.

Improvements in imaging techniques and their abilities to locate, classify and measure

atherosclerotic plaque in vivo have led to increasing focus on the study of each individual

component to determine its potential for predicting a future event16–19

.

6

1.1.5 Carotid Artery Anatomy

1.1.5.1 Gross Anatomy

The ascending aorta emerges from the left ventricle of the heart and contains oxygenated blood

necessary for supplying the tissues of the body. At the upper convexity of the arch of the aorta,

three great vessels emerge – namely the brachiocephalic trunk, the left common carotid artery and

the left subclavian artery (Figure 1). The brachiocephalic trunk, which arises to the left of the

midline, crosses to the right where it divides into the right common carotid artery and the right

subclavian artery20

.

The common carotid arteries, both right and left, pass up the neck alongside the trachea and

oesophagus and protected laterally by the sternocleidomastoid muscle. No branches emerge from

the common carotid artery proximal to its bifurcation. The carotid artery bifurcation occurs at the

upper border of the thyroid cartilage, or at the level of the C4 spinal vertebra. The right and left

common carotid arteries commonly divide at different levels, which can vary from person to

person, giving off the internal carotid and external carotid arteries (Figure 2).

At the bifurcation, the common carotid leading to the internal carotid artery is slightly dilated and

referred to as the carotid sinus or bulb. Baroreceptors, or cells that detect and regulate changes in

blood pressure, are housed here and respond to arterial wall stretching.

The external carotid artery is somewhat medial to the internal carotid and ascends anterior to it. It

supplies the head and neck outside the skull, including the facial muscles. Several branches

emerge from the external carotid artery before it terminates within the parotid gland, emerging as

the superficial temporal and maxillary arteries. The six branches that arise from the external

carotid prior to entering the parotid include the superior thyroid, lingual, facial, ascending

pharyngeal, occipital and posterior auricular artery. The posterior auricular, occipital and

superficial temporal arteries are part of the dense blood supply to the scalp. The maxillary and

facial arteries supply the deep muscles and superficial aspects of the face respectively.

The internal carotid arteries arise at the bifurcation and move superiorly from the carotid sinus. It

begins lateral to the external carotid, then moves posteriorly to a more medial and deeper level. It

does not give off any branches and ascends through the carotid canal, a foramen at the base of the

skull. It gives off the ophthalmic artery before dividing into the anterior and middle cerebral

7

arteries. These branches are responsible for most of the blood supply to the brain, including the

sensorimotor cortex and internal capsule.

The posterior cerebral arteries supply the parts of the temporal and much of the occipital lobes of

the brain and originate from the basilar artery. Moving backwards, the basilar artery is formed

from the right and left vertebral arteries, which enter the skull through the foramen magnum. The

vertebral arteries, in turn originate from the right and left subclavian arteries.

8

Figure 1. Diagram of the thoracic aorta and its main branches. (Image adopted from Henry Gray (1918)

Anatomy of the Human Body, Bartleby.com: Gray's Anatomy, Plate 506)

https://en.wikipedia.org/wiki/Henry_Gray

https://en.wikipedia.org/wiki/Bartleby.com

http://www.bartleby.com/107/

http://www.bartleby.com/107/illus506.html

9

Figure 2. 3-dimensional angiographic reconstruction of the right and left carotid arteries. (CCA – common

carotid artery, ECA – external carotid artery, ICA – internal carotid artery, VA – vertebral artery)

10

1.1.5.2 Microstructure

The carotid artery, like all arteries, is comprised of 3 main layers. These include the inner layer,

tunica intima, the middle layer, tunica media, and the outer layer, tunica adventitia. The intimal

layer is thin, comprised of endothelial cells, little connective tissue and an internal elastic lamina

(Figure 3). The medial layer is muscular and quite elastic, allowing it to expand with the systolic

phase of the heart. It is comprised mainly of smooth muscle and elastic connective tissue, and

makes up the largest cross sectional area of an artery. The outermost or adventitial layer is also

thin and is comprised of connective tissue and an external elastic lamina. The larger arteries also

contain the vasa vasorum, or a specialized network of blood vessels that supply the medial and

adventitial layers. As they may be relatively thick vessels, due to their muscularity, diffusion of

oxygen and other necessary metabolites from the lumen can be insufficient20

.

11

Figure 3. Cross sectional structure of the arterial wall showing its layers. (Image adopted from

Blausen.com staff. "Blausen gallery 2014". Wikiversity Journal of Medicine.

DOI:10.15347/wjm/2014.010. ISSN 20018762).

12

1.2 Atherosclerosis

1.2.1 Defining Atherosclerosis

In a breakdown of the word – “athero” refers to a gruel-like, soft pasty material, while “sclerosis”

refers to hardening of a tissue. Atherosclerosis is synonymous with arterial vascular disease where

there is hardening of the vessel due to the deposition and build-up of plaque. Atherosclerotic

plaque can contain debris from cellular breakdown, inflammatory cells, cholesterol, calcium,

fibrin and fibrous tissue depending on the stage of the lesion21

.

Atherosclerosis typically occurs at the intima-media surface of arteries22

. The vascular

endothelium, which comprises the intima, is considered a dynamic interface and consequentially

responds to a host of local and systemic stimuli. There are regions of intima, which are thickened,

but non-diseased, due to physiological adaptations to mechanical stress from flow or wall tension

changes. These areas of adaptive intimal thickening are believed to be most prone to initiation of

atherosclerotic lesions. A specific subset, termed eccentric intimal thickening, has its distribution

which coincides with the overall distribution of atherosclerotic prone areas. This type of

thickening is characteristically associated with the arterial branches and orifices and involves the

carotid, coronary, cerebral and renal arteries.

Initiation of atherosclerotic lesions can be explained by a variety of processes that contribute to

endothelial dysfunction. Such atherogenic processes include contributions from pro-inflammatory

cytokines, bacterial products and viruses, advanced glycation end products generated in diabetes,

aging and hypercholesterolemia23

. This initial insult then leads to the subsequent development of

atherosclerosis as suggested by the “response to injury” model. After injury, there is expression of

chemotactic and growth factors that lead to intimal migration of leukocytes followed by

proliferation of smooth muscle cells. Eventually, there is progression to incorporate lipid and

other products of cellular breakdown, such as cholesterol, which form the basis of atherosclerotic

plaques.

13

1.2.2 Classification of Atherosclerotic Lesions

The American Heart Association (AHA) classifies atherosclerotic lesions into 8 subtypes (Table

1). Types I and II are termed ‘initial lesions’24

. Type I lesions consist of microscopic and

chemically detectable lipid deposits that are usually not visible to the naked eye. The histological

changes are minimal with isolated groups of macrophage ‘foam cells’. Meanwhile type II lesions

are easily visible as yellow streaks, patches or spots on the arterial intimal surface. These fatty

streaks are microscopically different from type I lesions with stratified layers of macrophage foam

cells. Additionally, the intimal smooth muscle cells also contain lipid droplets. Type III, or the

“transition” lesion, can progress to an advanced form in subsequent stages, where it takes on the

description of an atheroma. It is therefore termed the preatheroma and features extracellular lipid

droplets and particles among the intimal smooth muscle layers. A well-defined collection of

extracellular lipid, or lipid core, has not yet developed at this stage, however.

True atheroma is defined from type IV onward and has an increasing correlation with clinical

events25

. Type IV lesions contain a collection of extracellular lipid which occupies a well-defined

region of the intima. There is no increase in fibrous tissue and no surface defects or thrombosis.

Type V lesions possess a fibromuscular cap, which is thought to form by replacement of tissue

that was disrupted by lipid and hematoma accumulation or thrombosis. Following this, there is an

inability of the vessel to expand outward, which leads to encroachment on the lumen with

subsequent narrowing. Luminal stenosis is a prominent feature of type V lesions.

The type V lesion subtypes underwent re-classification by the AHA in 2000. In the study by

Stary26

, types Vb and Vc became their own entities, becoming types VII and VIII. This was due to

the increased understanding of their formation whereby processes other than lipid regression

could lead to their morphology. In type VII lesions, calcification is a dominant feature while in

type VIII, fibrous tissue predominates.

Type VI lesions are considered complicated lesions and are most commonly associated with

clinical manifestations and fatal outcomes. These lesions comprise one or more of a surface

defect, plaque hemorrhage or surface thrombosis (Figure 4).

14

Category AHA Description Modified Description for MRI

I Isolated macrophage foam cells. Near-normal wall thickness. No

calcification. II Multiple layers of foam cells.

III Isolated extracellular lipid pools

present

Diffuse intimal thickening or small

eccentric plaque with no calcification

IV Extracellular lipid core Visible lipid or necrotic core surrounded

by fibrous tissue. Calcification may be

present. V Fibromuscular tissue layers produced

VI Surface defect, hematoma and/or

thrombosis present

Complex plaque with surface defect,

hemorrhage and/or thrombus.

VII Predominant calcification Calcified plaque.

VIII Fibrous tissue changes predominant Fibrotic plaque without lipid core and

possible small calcifications.

Table 1. Categories of atherosclerotic disease. Atherosclerotic stages as defined by the American Heart

Association and the respective description as seen by MRI.

15

1.2.3 Measuring Carotid Artery Atherosclerotic Disease

Techniques for measurement of atherosclerosis are critical in the development of new treatments,

or monitoring of therapeutic effects over time. Subclinical atherosclerosis detection, or

identification of high-risk, atherosclerotic biomarkers before symptom occurrence, may provide

new opportunities for initiation of early treatments and possible reduction in cerebrovascular

morbidity and mortality. Such measurements and monitoring can be achieved through several

modalities, using non-invasive and invasive methods.

1.2.3.1 Ultrasound

Ultrasound (US) uses the properties of reflected sound waves as detected by a transducer, to

generate images of internal body structures. Several techniques incorporating ultrasound methods

are used in measurement of carotid artery atherosclerosis and plaque components. It is a popular

choice of imaging technique because of its widespread availability, non-invasive technique and

lower costs.

Doppler US can be used for disease screening, as it measures carotid stenosis and may be able to

evaluate the macroscopic appearance of plaque. Sound waves reflect the movement of blood,

causing changes in the echo produced. Results are audible, with changes in flow resulting in

changes in frequency and pitch of sound waves, as well as visible, through colour changes applied

to flow patterns. Flow velocity increases with increasing stenosis but decreases at near occlusion.

There is potential for error with the Doppler US technique depending on the angle of insonation,

near-occlusion of the artery, the influence of collateral circulation and the technical aspect of the

spectrum analysis of echo frequencies27

. The reported accuracy for predicting carotid artery

stenosis varies from 80-97%, but there is a greater challenge with detecting occlusion28

.

B-mode ultrasound is a 2-dimensional technique that is recommended for measurement of

carotid intima-media thickness (IMT), a known predictor of cardiovascular and cerebrovascular

risk. Reflected sound waves produce a 2D image, where the luminal-intimal and the medial-

adventitial boundaries are seen, allowing for measurement of the intima-media thickness. Mean

IMT measurements are systematically larger than histology29

but have a coefficient of variation

(CV) of 9-13% for repeat IMT measurements30,31

and correlate well (r=0.89) with in vivo MRI

16

measurements31

. Conventional IMT measurement includes the mean thickness over a 1cm region

of the carotid artery at 3 locations – the common carotid, bulb and internal carotid – both

anteriorly and posteriorly. Recommendations by the Mannheim consensus32

suggest reporting the

mean thickness obtained at the common carotid region. An absolute progression of common

carotid artery IMT by 0.1mm suggests an increased risk of stroke by 13 to 18% 33

.

3-dimensional ultrasound techniques use a volumetric transducer for capturing images, which

can then be analyzed to yield plaque and artery volume measurements. This technique has good

reproducibility, but its ability to delineate individual plaque components has not been well

defined. Measurement of total plaque volume has a high CV around 47% but less variability for

VWV measurements with a CV of 14%30

.

Contrast-enhanced ultrasonography uses compressible gas bubbles, or targeted microbubbles,

for visualizing cell surface structures as well as microvessels. These bubbles produce acoustic

energy by resonating or releasing free gas when sound waves are applied, enabling imaging of the

vasa vasorum as well as neo-vessel development in atherosclerosis. However, bubbles have a

short half-life in vivo, are limited to the intravascular space and have a low contrast to noise

ratio34,35

. Neovascularization detection has a correlation of 0.68 compared with histopathology

specimens36

.

Intravascular ultrasound (IVUS) uses a special miniaturized ultrasound probe within a catheter.

It has limited resolution, in the range of 200 microns, and has the ability to identify plaque

components by using radiofrequency signals. IVUS correlates well with MRI measurement of

vessel wall cross-sectional area measurement with r=0.7937

. IVUS can identify calcium, fibrous

tissue and the necrotic core, but further study is needed for its ability to identify IPH. Detection of

iron deposits represents a possible marker for hemorrhage detection with IVUS. As with invasive

techniques, the risks associated are greater than non-invasive techniques, including bleeding and

thrombo-embolic events35,38

.

1.2.3.2 Computed Tomography

Computed tomography (CT) angiography is a non-invasive imaging modality with high

temporal and spatial resolution, which permits delineation of large and medium-sized vessels. It

17

combines multiple x-ray projections and requires iodinated contrast infusion to form detailed

cross sectional images of the vessel. It can delineate stenosis, calcification and fibrous tissue,

allowing for qualitative and some quantitative categorization of atherosclerotic plaques. However,

there is poor delineation of other plaque components for overall characterization of the plaque.

There is good correlation of CT and histology measurements for calcified (R2=0.74) and fibrous

tissue (R2=0.76) areas, but poor correlation with for lipid areas (R

2=0.24)

39. Disadvantages of CT

include the use of radiation, the invasive injection of intravenous contrast injection, as well as the

potential allergic side effects of the contrast agent itself34,35

.

1.2.3.3 Magnetic Resonance Imaging

High-resolution magnetic resonance imaging (MRI) techniques have been preferentially used

more recently for measuring carotid artery atherosclerosis and its individual components40

. MRI is

a non-invasive and non-ionizing modality. Its images can display atherosclerotic components,

which exhibit strong agreement with histology. Plaque component identification of calcifications

(r=0.74), lipid(r=0.75), hemorrhage (r=0.66), and loose matrix (r=0.55)18

are possible with 2D

MRI, with strong correlation with hemorrhage (=0.75) using 3D MRI41

. Imaging has been

undertaken using various scanner magnetic strengths, including 1.5 and 3.0 Tesla, as well as with

2-dimensional and 3-dimensional sequences. One of the greatest strengths of MRI is the ability to

use angiographic sequences for bright-blood images, as well as create black-blood sequences

where the signal from flowing blood is eliminated42

. The combination of these multi-contrast

protocols can characterize the lumen-vessel wall interface, which is extremely important in the

assessment of vessel dimensions and plaque morphology. MRI vessel wall thickness

measurements also correlate well with carotid IMT measurements31,43

. The main disadvantages of

MRI include its relatively high associated cost, longer scan time, restriction on individuals with

particular metallic implants, as well as the confined space within the scanner potentially resulting

in claustrophobia44

.

18

1.2.4 Effects of Atherosclerotic Lesions

Lesions from stage IV onward have the potential to cause arterial stenosis and occlusion25

. The

resulting degree of stenosis is currently the major guiding factor in the treatment of patients.

However, intraplaque hemorrhage, seen in complicated (type VI) atherosclerotic lesions, has been

linked with cerebrovascular event occurrence even in the absence of carotid artery stenosis. It

therefore represents an important and clinically relevant biomarker with the potential for

identifying patients who may be at risk of future cerebrovascular events.

19

Figure 4. Defined stages of an atherosclerotic plaque. (Image adopted from

https://commons.wikimedia.org/wiki/File%3AEndo_dysfunction_Athero.PNG)

20

1.3 Intraplaque Hemorrhage

1.3.1 Significance of IPH

Intraplaque hemorrhage has been the focus of more intensive studies with the relatively recent

ability to identify its presence in vivo using MRI5. IPH has been positively associated with risk

factors of increasing age, male sex, smoking, lower diastolic blood pressures and increased pulse

pressures45–47

. Several studies have since investigated the association of carotid artery IPH with

cerebrovascular events. A recent meta-analysis concluded that for patients with >30% carotid

artery stenosis, the risk of having a future cerebrovascular event was 5.6 times greater than the

general population48

. The 30% cut-off point was used due to the lack of data for patients below

this degree of stenosis suggesting that further study of IPH in patients with less than 30% stenosis

would be useful.

1.3.2 Pathophysiology

Intraplaque haemorrhage is categorized as a feature of type VI atherosclerotic lesions, which are

known to be associated with increased clinical outcomes and mortality26

. There is some debate,

however, over the origin of IPH. The 2 most acknowledged theories for IPH formation are

explained by plaque fissuring, and by neo-angiogenesis49–51

.

i. Plaque fissuring occurs at the arterial lumen-intima interface, when there is an initial

insult to the intima and formation of a non-occlusive luminal thrombus which is then incorporated

into the plaque. There are repeated occurrences of this process leading to development of

intraplaque hemorrhages. This course is thought to be similar to incorporation of luminal thrombi

within the aorta.

ii. The neo-angiogenesis theory is based on the formation of capillaries into hypoxic areas

of the plaque from the vasa vasorum in the adventitial layer. It is believed that an increased vessel

wall thickness of only 200 microns is necessary for initiation of neo-angiogenesis. These new

vessels lack supporting cells increasing their fragility and making them prone to rupture. The

extravasation of blood is thought to contribute to plaque progression with expansion of the

necrotic core and development of further neo-vessels.

21

IPH stimulates plaque progression through a number of mechanisms52,53

. Erythrocyte

extravasation is a significant factor. Cholesterol makes up 40% of the weight of red blood cells,

which is more than any other cell type. Bleeding delivers cells into the vessel wall where they are

degraded, and with the inability to metabolize cholesterol internally, cholesterol becomes

available and contributes to the growth of the necrotic core. Other contributory factors include the

effect of heme, which intensifies intraplaque inflammation. 49,54,55

.

Haptoglobin phenotype has also been deemed an important factor in IPH formation56

. The

homozygous 1-haptoglobin phenotype has been found to reduce risk in diabetic patients from

cardiovascular events. The risk reduction is thought to be due to the superior ability of 1-1

homozygous haptoglobin to clear free hemoglobin within the plaque, compared with the other

type 2 phenotypes, the heterozygous 2-1 and homozygous 2-2. This effect was shown to be

limited to diabetic patients57

.

1.3.3 Consequences of IPH

The onset of hemorrhage within the atherosclerotic plaque leads to deposition of red blood cells

(RBCs). Here, RBCs are quickly degraded and release free hemoglobin, which can either be

removed after binding with haptoglobin before phagocytosis, or undergo proteolysis. If the latter

process occurs, the globin protein core is broken down and the iron containing heme component is

left free in the plaque before binding to hemopexin and removed by phagocytosis49

. Within the

hemoglobin molecule, heme is oxidized from Fe2+

to the more reactive Fe3+

form, or

methemoglobin58

. Fe3+

dissociates readily from the globin component and mediates oxidative

modification of lipids, contributing to development of the necrotic core.

Increasing the size of the necrotic core is thought to be a critical feature related to plaque rupture.

The initial inflammatory response evoked by IPH causes migration of leukocytes and activation of

the clotting cascade. Macrophage death contributes free cholesterol to the core upon degradation,

in addition to the contribution by RBC membranes. The aggregation of platelets and prothrombin

activation that occur with the clotting process induce fibrin polymerization and entrapment of

leukocytes, which leads to thrombus formation. Fibrin formation then leads to secondary

activation of the fibrinolytic system, which, together with the release of other proteases, may be

22

related to fibrous cap rupture on the luminal side of the plaque59

. Rupture of the fibrous cap is

thought to be a significant mechanism of IPH-related atherothrombotic event occurrences60

.

The contribution of IPH, therefore, seems not to be restricted to the volume of blood deposited

within the atherosclerotic plaque, as it leads to the release and activation of several factors, which

contribute to the lipid-rich necrotic core and buildup of the plaque. This plaque growth would be

expected to cause an increase in the size of the carotid artery wall, which could have possible

implications as a diagnostic measure.

1.3.4 Relevance of IPH Study

The discussions surrounding IPH with respect to its formation and initial effects within the

atherosclerotic plaque are still inconclusive. There exists a knowledge gap related to IPH

initiation and direct consequences in early atherosclerotic disease. The relationship between IPH

and cerebrovascular event occurrence is well known from previous studies, but it has not yet been

adopted as a risk factor for treatment within the clinical guidelines. Clinical trials are yet to be

developed that attempt to elucidate the benefits of treating patients with IPH positive plaques.

Although carotid artery IPH presence is associated with cerebrovascular events, the amount that

must be present to convey this risk is also unknown. Little is known of the clinical relevance of

minute volumes of IPH or its associated volumetric changes. Imaging standards for detection and

measurement of IPH become more important as performing this task using histology is not

feasible. IPH should be studied using a well-defined method, with a high degree of accuracy and

reproducibility for detection and measurement. Once this is established, we may be able to detect

small volumes of IPH in early disease, directly observing its effects and gaining the necessary

knowledge to help bridge the gap that currently exists.

23

1.4 Magnetic Resonance Imaging

1.4.1 Overview – MRI Basics

Magnetic Resonance Imaging (MRI) is an important modality in the measurement of

atherosclerotic plaque as a non-invasive technique with the ability to delineate plaque

constituents. It has shown good correlation with histological samples and is a popular method for

plaque characterization61

. It can also identify IPH using a variety of sequences, which also

correlate strongly with histology.

MRI utilises the nuclear magnetic resonance properties of certain atoms within the body,

combined with radiofrequency (RF) waves, releasing energy that is detected and transformed into

images. Clinical MR machines use powerful magnets, which can be as much as 60 000 times

stronger than the Earth’s gravitational field62

. This is utilised in 3.0 Tesla machines, which was

the magnetic strength used for imaging in this project.

The property of “spin” exhibited by subatomic particles due to their magnetic momentum makes

MRI possible but not all nuclei have this feature. Hydrogen nuclei, represented by a single proton,

exhibit spin and this principle forms the basis for most clinical MRI. Hydrogen atoms align with

the magnetic field of the machine, or B0 field, resulting in a net magnetization vector in what is

termed the z-plane. The rate of spin of these protons, or precession, occurs at the Larmor or

precessional frequency calculated by the equation;

= x B0

Where = the precessional or Larmor frequency (megahertz), = the gyro-magnetic ratio

(megahertz per tesla) and B0 = the external magnetic field strength (Teslas). The Larmor

frequency is integral to calculating the operating frequency of the MRI system42

.

A RF pulse, perpendicular to the z-plane for example, is applied and protons spinning at that

frequency are “excited”. Magnetization is tipped by a certain angle, 90o in this case, which is

termed the flip angle (FA). This moves the net magnetization perpendicular to the z-plane and

into the x-y planes. From this point, relaxation of the protons occurs and can be divided into 2

independent processes: T1 and T2 relaxation63

.

24

T1 relaxation, or spin-lattice relaxation, refers to the recovery of the longitudinal magnetization

where protons revert to their alignment with the z-axis. However, the variable presence of protons

in different tissues leads to variations in this relaxation rate. Tight bonds, as in fat, release energy

quickly, while looser bonds, as with water, result in slower relaxation. T1 relaxation is dependent

on time and is defined as the time for longitudinal magnetization to reach 63% of the original

excited state63

.

T2 relaxation, or spin-spin relaxation, occurs as the excited protons that are spinning in-phase, or

with the same magnetization vector, begin to de-phase back to equilibrium in the transverse plane.

This is also dependent on the tissue as fat de-phases faster than water. T2 relaxation is also time

dependent and is defined as the time it takes for spins to de-phase to 37% of its excited state63

.

During T1 and T2 relaxation, the released energy induces a current that is detected by receiver

coils and the data undergoes a mathematical transformation, called the Fourier transform, to

generate the resulting MR-image. Knowledge of these relaxation times and the properties of the

specific tissues to be imaged guides the setting of radiofrequency pulses, angles and timing of

sequences, which leads to different image weightings42

.

The repetition time (TR) refers to the time between successive RF pulses and the echo time (TE)

refers to the time between application of RF pulse and the peak of echo detection. These

parameters play key roles in MR image contrast because of the variation in recovery and

relaxation times between tissues. These therefore affect the T1 and T2 weighting of images which

each have their advantages. Increasing the T1 weighting of an image shows fat and blood

products as bright intensities. With an increased T2 weighting, free water in tissues appear bright,

while fat is of intermediate intensity. These distinctions are important and vary based on the

pathological process being investigated.

1.4.2 MRI sequences

MR imaging of the carotid artery predominantly focuses on the segment immediately proximal

and distal to the bifurcation. It has been shown that there is increased potential for atherosclerotic

development around the bifurcation where eccentric intimal thickening occurs22

. Additionally, a

meta-analysis showed that type VI lesions favoured this 32mm segment around the bifurcation, in

25

the range of stenosis from 1-99%64

. Some 2D imaging protocols use this guideline for carotid

artery imaging, but 3D imaging allows for greater coverage and potential for greater areas of

analysis.

Current MRI techniques of the carotid artery use multiple 2D sequences for vessel wall

measurement, as well as plaque component identification and measurement. Several combinations

of sequences can be used, with the most commonly used sequences being the black-blood T1 and

T2 weighted (T1w, T2w) sequences, as well as the bright-blood time of flight (TOF), and contrast

enhanced T1 weighted (CE-T1w) sequence16,18,65

. The T1w sequence is most important for vessel

wall measurement, but all sequences are necessary for plaque component delineation. Each

sequence and slice thickness is individually acquired, resulting in increased scan times for a short,

specific segment of carotid artery, and has the potential for misalignment of the sequences.

The 3D-MRIPH is a T1w gradient recalled echo (GRE) sequence (see section 1.4.3), which

allows for imaging of the entire length of carotid artery, 19

. It is centered at the carotid artery

bifurcation of interest, but allows for capture of both carotid arteries. Combined with the 3D-TOF,

a bright blood sequence, the vessel wall can be easily delineated and measured, while also

detecting and measuring IPH. The bright blood appearance is achieved by using multiple RF

pulses to saturate the spins in stationary tissues of the imaged section. The signal from inflowing

blood, which is not affected by these pulses, appears hyper-intense, or bright, compared to the

surrounding stationary tissues42

thereby easily distinguishing the boundary of the lumen with the

vessel wall.

A major advantage of the 3D-MRIPH sequence is that only a single sequence is needed for IPH

detection and measurement66,67

. As a result, IPH detection can be done semi-automatically, where

a threshold is set and areas of intensity above the set threshold are measured and quantified. One

of the disadvantages of the 3D-MRIPH is its current inability to effectively identify plaque

components aside from IPH, although this has not been explored or reported on thus far.

Differences between 2D and 3D sequences are reflected by the variations in scanning parameters,

as shown in Table 2, but may also include differences in overall scan time, ability to delineate

plaque components and the carotid artery segment captured.

26

1.4.3 3D-MRIPH Sequence and Detection of IPH

The 3D-MRIPH sequence is termed for its ability to detect IPH based on histological validation41

.

It is a 3-dimensional (3D) T1 weighted (T1w) gradient recalled echo (GRE) sequence. Fat signal

is suppressed and an inversion time is used to null the signal derived from flowing blood, leaving

the bright intensities of blood products easily defined6. Images are acquired in the coronal plane

with a relatively short scan time and capture the carotid artery from origin at the arch of the aorta,

to the Circle of Willis. As a 3D sequence, it can be reformatted in different planes, making it

versatile. This is particularly useful in combination with the 3D-TOF sequence, which is acquired

axially.

The 3D-MRIPH sequence exploits the T-1 shortening effects of methemoglobin generated from

extravasated hemoglobin within the vessel wall. As red blood cells enter the extravascular space,

there is cellular lysis and release of hemoglobin. In the low oxygen environment, oxidative

denaturation occurs, ferrous iron becomes the ferric form (Fe2+ Fe3+), and methemoglobin is

formed. The paramagnetic property of methemoglobin confers a contrast-like effect, shortening

T1 and resulting in a high intensity signal on T1-weighted imaging66

.

Areas of high intensity within the carotid artery wall are strongly correlated with IPH. While a

single intensity value for IPH detection would be ideal, it is not feasible due to the variability that

exists in overall image intensities between patients and machine types. However, using the

intensity of a specific tissue as a comparator has overcome this problem. The sternocleidomastoid

muscle (SCM) is adjacent to the carotid artery and has a similar intensity to the muscular wall of

the artery itself. These features make the SCM a useful reference tissue for IPH detection, which

is defined as a ratio relative to the SCM intensity. Though several studies have used varying

intensity ratios of IPH:SCM, with 1.5 being the most common, there is no confirmatory evidence

as to the ideal ratio of the SCM intensity to vessel wall intensity as applies to IPH detection.

27

2 Dimensional 3 Dimensional

T1W T2W PDW CE-T1W T1W TOF

Slice number / n 16 16 16 16 100 160

Slice thickness / mm 2.00 2.00 2.00 2.00 0.50 1.40

Acquisition plane Axial Axial Axial Axial Coronal Axial

TE / ms 8.55 50.00 17.89 8.55 4.11 3.45

TR / ms 1034.48 1846.15 2068.97 923.08 11.22 26.00

TI / ms - - - - 560.00 -

Flip Angle / deg 90 90 90 90 15 18

NEX 1.00 1.00 1.00 1.00 2.00 1.00

FOV / mm 130.00 130.00 130.00 130.00 270.00 190.00

Matrix 256 x 220 256 x 209 256 x 209 256 x 220 272 x 224 360 x 232

Resolution 256 x 256 256 x 256 256 x 256 256 x 256 560 x 560 640 x 640

Table 2. Parameters for the 2-dimensional and 3-dimensional sequences used in this thesis.

28

1.4.4 VesselMASS – Carotid MR Image Analysis Software

VesselMASS (Medis, Netherlands) is a type of image processing software, using vascular MRI

sequences that allows for vessel wall measurement and identification of atherosclerotic plaque

components. It has been validated for measurement of the vessel wall, as well as for plaque

component quantification68

. Additionally, it possesses the ability to generate vessel wall contours

using edge detection algorithms.

The software is able to replace a number of steps performed and reduce the length of time

necessary with manual measurements. Alignment of carotid artery images can be user-dependent

and is time-consuming, so reducing this to an automated method provides benefits in the

reduction of time and expertise needed for image processing. Its ability to automatically register

2D and 3D images compared well with expert, manual registration techniques and can account for

patient movement in all 3 planes69

.

VesselMASS has been used in previous studies for measurement of vessel wall volumes and

thicknesses, including the mean and maximum thickness70,71

. It has also been used in these studies

to identify and quantify individual plaque components, as well as accurately generate vessel wall

contours. These properties have made VesselMASS a useful tool for measuring various aspects of

the carotid artery wall and atherosclerotic plaque, as well as identification and quantification of its

components (Figure 5).

29

Figure 5. 3-dimensional MRI-detected intraplaque hemorrhage. Image A shows the 3D-MRIPH sequence

as acquired in the coronal plane. The yellow line indicates the level at which the reformatted axial plane,

B, was obtained. C shows the section of the right carotid artery with a region of high signal consistent with

intraplaque hemorrhage (IPH). Contours are drawn for the outer wall (green) and lumen (red) with the area

of IPH in this segment shaded blue in D.

30

1.5 Diabetes

1.5.1 Disease Impact

The International Diabetes Federation estimated that 415 million people in the world were living

with diabetes mellitus in 201572

. The complications of diabetes affect all age groups and can be

severely incapacitating. The global increase in cases is of major concern with numbers expected

to reach 642 million in 2040, predominantly affecting developing countries72

. However,

developed countries are also expected to see similar increases, with Canada experiencing a 230%

increase from 1998 to 2009 where 2.4 million cases were recorded73

. This is expected to further

increase to 3.7 million Canadians in 2019.

Diabetes mellitus can be classified into 4 major groups. These are type 1, type 2, gestational

diabetes and those that fall into the ‘other specific types’ category. Type 1 diabetes refers to those

who develop the disease mainly as a result of pancreatic beta cell destruction and possess

increased susceptibility to ketoacidosis. Type 2 diabetes includes those in the range between

predominantly insulin resistant with a relative insulin deficiency to those with a defect in insulin

secretion with insulin resistance. Gestational diabetes occurs during pregnancy and refers to

associated glucose intolerance. Finally, there is a range of uncommon conditions consisting

mainly of genetically defined forms, as well as drug or disease related development of diabetes

that makes up diabetes group termed as ‘other specific types’.

1.5.2 Diabetes and Cerebrovascular Disease

Diabetes is considered to be a modifiable risk factor for ischemic stroke. It increases the risk of

the first stroke in males by 2-3 times and in females, by 2-5 times74

. There is also a higher risk of

recurrence and greater overall morbidity and mortality, contributing to the economic burden of the

disease. For those who survive an event, it is estimated that 50% will have a long term

disability75

.

The risk of ischemic stroke specifically increases in diabetic patients compared with the risk of a

hemorrhagic stroke. This risk is thought to be related to the impact of the characteristic metabolic

and hemodynamic changes involved in diabetes with factors such as insulin resistance and central

31

obesity thought to contribute. Specific guidelines have been developed and are constantly

reviewed for prevention, monitoring and management of cerebrovascular events in diabetic

patients for these reasons. These guidelines are evidence based and point to the need for

aggressive and early intervention for acute stroke occurrence in patients with diabetes.

1.5.3 Vascular Complications

Vascular dysfunction is synonymous with diabetes76

. The related complications can be

categorized as micro-vascular and macro-vascular. Microvascular complications include

retinopathy, neuropathy and nephropathy while the macrovascular complications stem from

atherosclerosis development and include cardiovascular events from myocardial infarctions to

strokes77

.

From the perspective of glycemic homeostatic control, there are 3 main factors associated with

development of vascular disease. These are the chronically elevated glucose levels, its increased

variability and the occurrence of hypoglycemic episodes. The Diabetes Control and

Complications Trial (DCCT) concluded that intensive control of blood glucose to near normal

reduced the incidence of microvascular complications in type 1 diabetes78

. The United Kingdom

Prospective Diabetic Study explored these effects in a type 2 diabetic population and found that

intensive control improved microvascular and macrovascular complications, but not mortality.79

.

1.5.4 Atherosclerosis and Diabetes

Early atherosclerosis development in diabetes is considered to be a product of molecular

mechanisms, which induce endothelial damage through overproduction of superoxide by the

mitochondrial electron transport chain. This results in formation of reactive oxidative species

(ROS), leading to endothelial damage, depletion of nitric oxide and prostacyclin, increased

production of prostanoids and endothelin, and atherosclerotic plaque formation76,80,81

.

Endothelial dysfunction, which precedes atherosclerotic lesion development, is present from very

early in diabetes. The atherosclerotic lesions themselves are not discernible from those that are

related to other disease processes. Additionally, patients with type 2 diabetes possess a

32

characteristic dyslipidemic profile that, while not elevated, contributes to plaque development.

This comprises a high triglycerides, increased small, dense LDLs and low HDL levels82

. High

blood pressure in diabetes also contributes to development of macrovascular complications where

control improves clinical outcomes.

1.5.5 Diabetes and IPH

Diabetes leads to increased development of atherosclerotic disease, progression and end organ

effects. This is well known as evidenced by the increased risk of cardiovascular and

cerebrovascular events. The poorer outcomes and increased risk of events suggest that

identification of an early biomarker for improving risk stratification would be valuable in the

prevention and treatment of diabetic patients.

The risk of having IPH as a diabetic patient is unknown and is not an established risk factor, but

the accelerated development of atherosclerosis, coupled with the increased risk of ischemic

events, make this population an ideal group for investigating early atherosclerotic development

non-invasively, using MRI techniques. The presence of IPH within the diabetic cohort may

explain this increased susceptibility to end organ atherosclerotic events.

Little is known of the incidence of IPH in individuals with <30% carotid artery stenosis.

Therefore, imaging this cohort of patients who are also susceptible to early atherosclerotic

development and increased risk of events may provide some insight into early IPH detection, IPH

progression and its effect on atherosclerotic plaque, as well as its possible association with

subsequent clinical events.

33

1.6 Hypothesis and Aims

1.6.1 Hypothesis

MR-detected intraplaque hemorrhage is present in non-stenotic carotid artery

atherosclerotic disease of asymptomatic diabetic patients and is associated with an increased

volume of the vessel wall.

1.6.2 Aims

1. To define a semi-automated image processing protocol for intraplaque hemorrhage

quantification with the accuracy of an expert reader using 3-D MRI.

2. To identify the prevalence of intraplaque hemorrhage in an asymptomatic diabetic population

without carotid artery stenosis.

3. To observe the association between intraplaque hemorrhage and carotid artery wall volume.

34

Chapter 2

A Semi-Automated Method for Detecting and Quantifying

Carotid Artery Vessel Wall High Signal with 3-

Dimensional Magnetic Resonance Imaging

35

A Semi-Automated Method for Detecting and 2

Quantifying Carotid Artery Vessel Wall High Signal with 3-Dimensional Magnetic Resonance Imaging

2.1 Introduction

Carotid artery intraplaque hemorrhage (IPH) is a known predictor of cerebrovascular

thromboembolic events and a potent factor in atherosclerotic plaque progression. In vivo

identification of IPH has become possible with the advent of high resolution magnetic resonance

imaging (MRI)19,52,53,83,84

. Methods for detecting IPH have been validated using various two-

dimensional (2D) and three-dimensional (3D) sequence acquisition parameters (Table 3)6,19,41,85–

93.

Stages of IPH can be recognized based on specific combinations of hyper- and hypo-intense

appearing areas in the carotid wall across multiple 2D sequences16,18

. However, a single 3D T1-

weighted (T1w) black blood gradient recalled echo (GRE) MRI sequence can detect a single stage

of carotid artery IPH shown to be related to clinical events19,87

. This sequence exploits the

paramagnetic properties of methemoglobin within the carotid artery wall, resulting in areas of

hemorrhage displaying high signal41

. High signal is defined as intensities above a threshold set by

comparison with normal vessel wall. This intensity is difficult to measure due to the small region

of interest arising from a normal thin vessel wall; the substitution of an iso-intense tissue,

commonly adjacent skeletal muscle such as the sternocleidomastoid (SCM), makes the threshold

measurements easier to acquire.

Clinically, intensities that fall above a defined threshold set from an intensity ratio within the

carotid artery wall are considered to be positive for IPH. Experts frequently cite this intensity ratio

as 1.541,67,87,91,94

but there is little evidence in the literature to support the use of this specific ratio.

This ratio originally used to help with detection when expert readers were unsure of the presence

or absence of IPH. Review of previous work (Table 3) has not described the use of an automated

or semi-automated method for IPH detection. Reading by expert readers is subject to human error

particularly when areas are small, as is the ad hoc application of a signal intensity ratio.

Incremental changes in this ratio leads to differences in the detection and measurement of high

signal areas (Figure 6) and SCM intensity quantification is affected by its heterogeneous nature as

36

well as by magnetic field variations seen in GRE sequences, termed B0 inhomogeneity42

. In

addition, the availability of expert readers for analysis of large case volumes may not always be

readily available. Development of a semi-automated method for carotid artery image analysis that

accounts for these principles would allow for consistent detection and measurement of IPH across

sites and analysts, decreasing the user input steps and potentially reducing measurement

variations.

We hypothesized that a semi-automated quantitative method of IPH detection and quantification,

comparable with assessment by expert readers, could be developed. We aimed to create a semi-

automated image processing protocol for identification and quantification of small areas of IPH

on 3D MRI with the accuracy of an expert reader.

37

Publication

Author Year

Sequence(s)

Used

Validated

with

Histology

IPH Result Intensity

Reference

IPH intensity

identified

Moody et al. 1999 3D No Present/Absent Adjacent

muscle

Visual assessment.

Ratio not specified.

Moody et al. 2003 3D Yes Present/Absent Adjacent

muscle

> SCM intensity.

Ratio not specified.

Cappendijk et al. 2004 2D/3D Yes Present/Absent Adjacent

muscle

Not specified

Yamada et al. 2007 3D No Quantified SCM 2.0 x SCM

Altaf et al. 2007 3D No Present/Absent SCM 1.5 x SCM

Bitar et al. 2008 3D Yes Present/Absent SCM 1.5 x SCM

*Yoshida et al. 2008 3D Yes *Matched SCM 1.57 + 0.25 x SCM

U-King-Im et al. 2010 3D No Present/Absent SCM 2.0 x SCM

Qiao et al. 2011 3D Yes Present/Absent SCM Not specified

Singh et al. 2013 3D No Present/Absent SCM 1.5 x SCM

Kurosaki et al. 2015 3D No Present/Absent SCM 1.4 x SCM

McLaughlin et al. 2015 3D No Present/Absent SCM 2.0 x SCM

Table 3. Review of previous studies describing 3D MR-detection of intraplaque hemorrhage.

Review of previous studies describing 3D MR-detection of intraplaque hemorrhage. Publications are

categorized in chronological order by author and year. Other columns reflect IPH identification based on

corresponding sequence type(s) used, whether histological comparison was performed, how IPH was

reported, the tissue used for the reference intensity and how IPH intensities were identified respectively.

2D – two-dimensional, 3D – three-dimensional, SCM – sternocleidomastoid.

*This study directly matched histological IPH with its corresponding area on a 3D-T1 weighted black

blood gradient echo sequence to derive the relative signal intensity (rSI).

38

Figure 6. Repeated 3D-T1w GRE reformatted axial image. Common carotid artery segment depicted

surrounded by a vessel contour (blue circle) with an area of high signal detected as the blue highlighted

region.

Image A shows the vessel contoured before detection. Subsequent images show the diminishing area

measured as intraplaque hemorrhage at the corresponding intensity ratio displayed in the top left corner.

39

2.2 Methods

This retrospective study was a part of two ongoing research ethics board approved prospective

clinical trials - the Canadian Atherosclerosis Imaging Network and the Low Glycemic Index Diet

for Type 2 Diabetics (clinical trial numbers NCT01440296 & NCT01063374;

https://clinicaltrials.gov). The studies are being conducted in compliance with the Personal Health

Information Protection Act. All patients gave informed consent and study procedures were

reviewed by the respective research ethics boards.

2.2.1 Study Sample

Patients with IPH positive carotid arteries only were included. IPH was previously identified by

expert readers and this formed the sole criterion for study inclusion. Fifteen carotid artery MRI

studies (n=15) performed between January 2011 and November 2013 at a single site were

selected. These came from 14 subjects (range 60 to 100 years, mean 73.9+/-10.7 years)

comprising 12 males (range 60 to100 years, mean 74.7+/-11.4 years) and 2 females (range 67 to

71 years, mean 69.0+/-2.8 years) , with bilateral carotid arteries used from one subject. Ten

carotid artery studies were used for defining an optimal intensity ratio for IPH detection. The

remaining five studies were used to validate this result by directly comparing manual and

automatic IPH measurements.

2.2.2 MRI Protocol

Studies for both trials were performed using a 3-Tesla MR scanner (Philips Achieva) with a 16-

channel neurovascular coil (16-NV-SENSE). The MRI sequence used was a 3 dimensional T1-

weighted fat-suppressed fast field echo sequence, obtained in the coronal plane. Imaging

parameters were as follows: TR 11ms; TE 4ms; TI 800ms; flip angle 150; 0.5mm thickness; FOV

270 x 190mm2; matrix size 512 x 256; scan time 8 minutes 54 seconds. High order shimming was

prescribed for a 10cm region of the neck to improve B0-field homogeneity around the carotid

arteries. Imaging was centered at the level of the carotid artery bifurcation and covered from the

top of the arch of the aorta to the Circle of Willis.

40

Figure 7. 3D-T1w GRE semi-automated image analysis protocol. Image A shows the coronal view with a

high signal area in the left carotid artery. Centering was done on this region, Image B, to produce the 16

slice x 2mm thickness axial reformat, Image C. A hemorrhage contour was created around the outer vessel

wall from the common carotid to the internal carotid artery. Iso-intensity was determined on the adjacent

sternocleidomastoid muscle. ROI Method 1 used the mean intensity within a circular region of interest at

the bifurcation (Image D). ROI Method 2 used the mean intensity of the entire SCM at the bifurcation

(Image E). ROI Method 3 also used the mean intensity within a circular region of interest, but at every

axial level (Image F). The SCM intensity was used to calculate the threshold at the ratios from 1.2 to 2.2

for each method. The area of intensity above the set threshold (1.5 in this example) was highlighted per

axial slice and measured (Image G).

41

2.2.3 Defining an optimal threshold

2.2.3.1 Image Processing

A single reader, Reader 1 (> 2 years experience), analyzed 10 carotid arteries using vascular

image analysis software(van Klooster et al. 2013), VesselMASS (Medis, Netherlands) with a

semi-automated method (Figure 7). A hyper-intense region was identified on the 3D-T1w GRE

sequence in the coronal plane and used to center the reformatting parameters. A 16-slice, 2mm

slice thickness multi-planar reformat (MPR) was created in the axial plane for each carotid artery,

spanning a 32mm segment.

The vessel wall was manually outlined on each of the 16 slices from the common carotid to the

internal carotid artery. The SCM intensity was obtained at the level of the carotid artery

bifurcation defined as the mean intensity within a 20mm2 circular region of interest (ROI). This

ROI was placed within the muscle as close as possible to the bifurcation for the technique termed

ROI Method 1 (Figure 7D). VesselMASS automatically calculated the mean intensity within this

contour, which was used as the SCM intensity for that carotid artery.

Eleven (11) thresholds were calculated using intensity ratios from 1.2 to 2.2 times the SCM

intensity, in 0.1 unit increments. Carotid artery IPH was defined per axial slice as:

i.) Positive – when an area of intensity above the set threshold was measured as >0.1mm2,

which was the minimum area detectable by the software, or

ii.) Negative - when all intensities within the vessel wall fell below the set threshold.

The area of IPH was recorded for every axial slice (n=160) and designated IPH positive or

negative at each of the 11 intensity ratios. The volume of IPH was also generated for each carotid

artery (n=10).

2.2.3.2 Sternocleidomastoid Intensity Measurement Comparison

The axial reformats of the 10 carotid arteries were used. Two additional methods of measuring the

SCM intensity were performed. This was done to observe the effects that SCM intensity

42

heterogeneity and B0 inhomogeneity exerted on the obtained SCM intensity. IPH was again

reported as a binary result for each axial slice at the 11 intensity ratios.

ROI Method 1 – Described above.

ROI Method 2 – The entire area of the SCM was contoured on the axial slice containing the

carotid artery bifurcation (Figure 7E). The mean intensity obtained within the contour was used as

the SCM intensity for that carotid artery.

ROI Method 3 – A unique SCM intensity was obtained at every generated axial level for the 10

carotid arteries (n=160). A 20mm2 circular ROI was placed on the SCM (Figure 7F) as close as

possible to the CCA, bulb or ICA, depending on the level. The mean intensity within the contour

was used as the SCM intensity for that particular axial slice.

2.2.4 Gold Standard

Axial images (n=160) were randomized. Images were randomized to eliminate the influence of

adjacent axial slice results on subsequent assessments. Each slice was independently rated as

positive or negative for IPH by 2 experienced readers, Reader 2 (>20 years experience) and

Reader 3 (>4 years experience). Both experts performed an initial independent read using a

qualitative intensity comparison with the SCM as performed in clinical practice. A consensus

decision between the experts was used for conflicting slices and represented the ground truth for

the presence/absence of IPH against which the automated method was compared.

2.2.5 Direct Area Comparison

Five carotid arteries were used to compare manually measured areas of IPH with those derived

from the semi-automated method at each intensity ratio. A 16 slice, 2mm thickness MPR was

created for each carotid artery, generating a total of 80 axial slices. Reader 1 manually contoured

each slice generating the area of IPH per image, when present.

43

The semi-automated method was then used to quantify IPH, using ROI Method 1, for the eleven

intensity ratios from 1.2 to 2.2 in 0.1 unit increments. There was a 2 week gap between the

manual and semi-automated evaluations.

2.2.6 Statistical Analysis

Patient data was expressed as means with standard deviations for continuous variables and

percentages for discrete variables. Cohen’s kappa statistics were used to compare the initial

agreement between expert readers for the gold standard. The eleven intensity ratios produced

corresponding 2x2 tables, which were used to calculate the sensitivities and specificities for IPH

detection. Receiver operating characteristic (ROC) curves were then plotted for each of the ROI

Methods 1, 2 and 3. Area under the curve (AUC) was calculated for each method to obtain the

overall accuracy of the SCM intensity comparison. The Youden Index determined the best cutoff

point for each curve corresponding with optimal threshold for IPH detection95

. For an AUC above

0.80 (=0.05, =0.20), a minimum of 13 IPH positive and 13 negative images were needed96

.

Intra-class correlation coefficient (ICC) statistics97

compared the measured areas of IPH between

the manual and semi-automated methods. Statistics were performed using IBM SPSS Statistics

(Version 22.0. Armonk, NY) and statistical significance was defined by a p-value below 0.05.

44

2.3 Results

2.3.1 Study Characteristics

All carotid artery 3D-T1w GRE sequences were used and none of the generated axial slices

(n=240) were excluded. Table 4 summarizes the demographic data and carotid artery

characteristics. Ten carotid arteries were left-sided and five were from the right. Carotid artery

stenosis ranged from 0 to 70% with 6 carotid arteries falling within the category of mild stenosis

(0-30%) and 9 in the moderate stenosis (31-69%) category. The area used for measuring the SCM

intensity was largest with ROI Method 2 with the greatest variability at 260 + 66mm2 compared

with ROI Methods 1 and 3, which used a constant area at 20+0mm2.

2.3.2 Defining an Optimal Intensity Ratio

Ten carotid arteries, each with 16 reformatted axial slices (n=160) were evaluated by 2 expert

readers. Initial agreement was assessed (ĸ = 0.90, p<0.01) and a consensus decision used for

conflicting slices. This gave a total of 81 IPH positive slices (50.6%) and 79 that were negative.

These results were compared with the binary outcomes for each slice at the eleven intensity ratios,

producing 11 corresponding sensitivities and specificities (Table 5). This was also performed for

ROI Methods 2 and 3.

A receiver operating characteristic (ROC) curve (Figure 8A) was plotted for ROI Method 1

derived from the sensitivity and specificity at each threshold. The highest sensitivity-specificity

combination, defined by the Youden index, was 0.80 at the ratio of 1.5 times the SCM intensity.

The AUC was 0.94, which indicated excellent accuracy as a diagnostic test. At the intensity ratio

of 1.5, the sensitivity was 80% with perfect specificity at 100%. This produced a positive

predictive value of 100% with a negative predictive value of 83%.

45

Characteristics Value

Patients, n 14

Age, years (mean + sd) 73.9 + 10.7

Male Gender, % 85.7 (n=12)

BMI, kg/m2 (mean + sd) 26.2 + 2.3

Hypertension, % 71.4 (n=71.4)

Hyperlipidemia, % 78.6 (n=11)

Anti-platelet medication, % 71.4 (n=10)

Smoking history, % 42.9 (n=6)

Carotid arteries, n 15

Left Carotid, % 66.7 (n=10)

Carotid stenosis, n 0-30% 31-69% >70%

6 9 0

*Axial images reported, n 160

*IPH Positive, % 81 (50.6)

*IPH Negative, % 79 (49.4)

*ROI Area, mm2 (mean + sd) Method 1 Method 2 Method 3

20 + 0 260 + 66 20 + 0

Table 4. Patient demographic data and carotid artery characteristics.

sd – standard deviation, BMI- body mass index, IPH – intraplaque hemorrhage, SCM –

sternocleidomastoid muscle.

*Refers to the 10 carotid arteries used for defining the optimal intensity ratio.

46

2.3.3 Effect of SCM Intensity Heterogeneity

ROI Method 2 aimed to account for the influence of intensity heterogeneity within the SCM, by

measuring the mean intensity within its entire available cross-sectional area at the level of the

carotid artery bifurcation. Compared with ROI Method 1, the area of SCM used for obtaining the

mean intensity was significantly different (p<0.01).

The ROC curve plotted using the sensitivity and specificity results of ROI Method 2 produced an

AUC of 0.93 across the intensity ratios. This suggested that it was also an excellent diagnostic

test. With this technique, the highest accuracy was found at the intensity ratio of 1.7 where the

Youden index was 0.74, with a sensitivity of 74% and a perfect specificity at 100%. This was

slightly less accurate than results obtained using ROI Method 1 at the 1.5 threshold.

2.3.4 Effect of B0 Inhomogeneity on the SCM Intensity

ROI Method 3 aimed to account for the impact that B0 inhomogeneity, caused by magnetic field

differences, had on IPH detection. Changes in the overall image intensity moving through the

longitudinal axis would be offset by using an individual SCM intensity per axial slice for

threshold determination. This gave 160 unique SCM intensity values to derive individual

thresholds for each of the 11 intensity ratios.

The resulting ROC curve was plotted using the resulting sensitivities and specificities at each

intensity ratio. The subsequent AUC was 0.95, again suggesting an excellent diagnostic test. The

maximum value of the Youden index obtained was 0.79, with a corresponding sensitivity of 84%

and specificity of 95%. The most accurate intensity ratio was 1.5, but demonstrated a lower

Youden Index than the same cutoff in ROI Method 1.

47

Figure 8. ROC curves for intensity ratio determination. ROI Method 1 (A), ROI Method 2 (B) and ROI

Method 3 (C) shown with interval intensity ratios labelled for each technique at 1.2, 1.5 and 2.2.

Corresponding area under the curve (AUC) is displayed for each. ROC curves for all methods are shown

together in D.

48

Intensity Ratio

ROI METHOD 1 ROI METHOD 2 ROI METHOD 3

Sensitivity Specificity Youden Index



1.2 0.963 0.494 0.457 1.000 0.397 0.397 0.975 0.494 0.469

1.3 0.926 0.709 0.635 0.951 0.494 0.444 0.938 0.696 0.634

1.4 0.852 0.911 0.763 0.877 0.658 0.535 0.889 0.899 0.788

1.5 0.802 1.000 0.802 0.840 0.861 0.700 0.840 0.949 0.789

1.6 0.728 1.000 0.728 0.778 0.962 0.740 0.765 1.000 0.765

1.7 0.704 1.000 0.704 0.741 1.000 0.741 0.741 1.000 0.741

1.8 0.654 1.000 0.654 0.704 1.000 0.704 0.704 1.000 0.704

1.9 0.605 1.000 0.605 0.667 1.000 0.667 0.642 1.000 0.642

2.0 0.580 1.000 0.580 0.630 1.000 0.630 0.568 1.000 0.568

2.1 0.543 1.000 0.543 0.568 1.000 0.568 0.543 1.000 0.543

2.2 0.531 1.000 0.531 0.568 1.000 0.568 0.531 1.000 0.531

Table 5. Sensitivity, specificity and Youden index by intensity ratio and ROI Method. The highest Youden

index for each is shown in bold with its corresponding sensitivity and specificity.

49

2.3.5 Effects on High Signal Volume Quantification

Volume of detected high signal was generated for each of the 10 carotid artery studies using the

semi-automated method with ROI Method 1 at the 11 intensity ratios. Box and whisker plots

show the spread of volumes across the 10 carotid arteries at each threshold and across the 11

thresholds for each carotid artery (Figure 9).

An inverse relationship between threshold and range of volumes was seen, where an increase in

the intensity ratio led to a decrease in the spread of high signal volumes for all carotid arteries.

There was a tendency to a positively skewed distribution. When plotted on a per case basis, high

signal volumes with median values below 0.1ml demonstrated an increased number of significant

outliers (> 1.5 times the inter-quartile range) at the lower thresholds. This emphasized the need

for a standardized optimal intensity ratio as there is increased variation at lower disease volumes.

2.3.6 Direct Area Comparison

Five carotid arteries were used for validation of the intensity ratio results by directly comparing

the areas contoured manually with those detected with the semi-automated method. Intraclass

correlation coefficients were used for this comparison and plotted for each intensity ratio with

their respective 95% confidence intervals (Figure 10). Results showed ICCs of >0.8 for all

intensity ratios, or near perfect correlation. The graph trended to maximal correlation at the

thresholds of 1.4, 1.5 and 1.6 with associated narrow confidence intervals. Corresponding ICC

values were 0.989 at 1.4, 0.993 at 1.5 and 0.985 at 1.6.

50

Figure 9. Box and whisker plots. Volumes of measured high signal shown across the 10 carotid arteries

per threshold (A) and across the 11 thresholds per carotid artery/case (B). The gray bar denotes the 1st to

3rd quartiles, with the median value shown as the line within, and whiskers denote minimum and

maximum values of data within 1.5 times the inter-quartile range. In A, the volume of high signal wall

intensity demonstrates an inverse relationship with increasing intensity ratio. A decrease in range of

volumes and increased tendency to a positively skewed distribution are also seen as intensity ratio

increases. In B, the quartiles demonstrate the distribution of the volume measurements across the intensity

ratios for each case. Below smaller volumes of high signal seen by median values of 0.2ml, there are

greater implications noted as the lower intensity ratios produce significant outliers for volume

measurement.

51

Figure 10. Intraclass Correlation Coefficient (ICC) variation between manual and semi-automated

measurements across intensity ratios for 5 carotid vessels. The peak of the curve corresponds with the high

degree of correlation between manual hemorrhage area measurements and semi-automated hemorrhage

detection at the 1.4 to 1.6 intensity ratios.

52

2.4 Discussion

This study showed that areas of high signal intensity within the carotid artery wall consistent with

IPH, were most accurately detected using a vessel wall intensity above 1.5 times the intensity of

the SCM (at areas >0.1mm2) when compared with expert clinical readers, using a semi-automated

image processing protocol for a single 3D –T1w GRE sequence. The SCM intensity was sampled

within a 20mm2 ROI at the level of the carotid artery bifurcation. Quantitative comparison of high

signal area measurements with this method against expert measurements confirmed accuracy of

the 1.5 threshold level and SCM measurement technique.

Important aspects of this study include the development and validation of a semi-automated image

processing method for detection of small areas of high carotid artery wall signal. The semi-

automated method was developed to overcome the shortcomings of expert readers, who are

subject to human error. By comparing a range of intensity ratios against expert readers, the signal

intensity ratio that detected high signal with similar accuracy to the expert readers was identified.

The increased automation and objective intensity comparisons outlined in this method therefore

reduced the human-related error, improving detection and quantification of small IPH areas

(>1mm2) that fulfilled the defined vessel wall:SCM ratio of 1.5. IPH detection at this level has not

been previously described and its clinical implications are unknown.

The accuracy of the semi-automated technique was tested using individual slice IPH area

measurements derived from the semi-automated method were compared with an expert reader and

this confirmed the high accuracy of the 1.5 intensity ratio with an ICC of 0.99 for the area

measurements. These results confirmed the semi-automated method could achieve an accuracy of

plaque area/volume measurement approaching that of an expert reader.

We explored the effect of SCM intensity differences, from tissue heterogeneity and magnetic field

variations, on detection of high signal disease. This has not been addressed in previous studies,

although using an ROI to measure the SCM intensity, which gave the most accurate result in this

study, has been described. Detection of vessel wall high signal regions requires consistent

measurement of the SCM intensity as a comparator. The role of the shim volume at the level of

the carotid bifurcation is to adjust the B0 magnetic field and optimize homogeneity. As each

excitation pulse and the blood nulling inversion pulse both have a frequency selective response,

the resulting signal will vary outside the shimmed volume. The choice of an ROI on the SCM

53

closest to the carotid bifurcation ensures the most homogeneous magnetic field, as this is the

centering point for the shim volume. A constant ROI ensures consistent SCM intensity

measurements at different time points and between patients. The three ROI methods used aimed

to investigate the effects that these factors, namely SCM intensity heterogeneity and B0

inhomogeneity, had on the semi-automated high signal detection process. We found that there was

little overall difference, as reflected by the excellent AUCs achieved, suggesting that the SCM

was a suitable intensity comparator. ROI Method 1 distinguished itself, however, by producing

the most accurate threshold, while requiring the least number of user related steps.

High signal areas on T1w MRI sequences correlate well with hemorrhage histologically, as they

result from the paramagnetic properties of ferrous iron in degraded heme. Defining high signal

areas against the SCM intensity on a 3D T1w GRE sequence is a well-known method used to

detect the presence of IPH clinically98

. The results of this method, performed by the expert

readers, represented the gold standard in this study because of previously described problems

noted with carotid artery MRI – histology comparisons16,18,65,99

. Specimen shrinkage is a well-

known feature of sample processing, which poses a substantial challenge when matching

measured areas and volumes. The image slice thickness on MRI differs from the histological slice

thickness, requiring multiple specimen slices to be equated to a single image location. Also, there

is variation in the 3D spatial orientation between the removed specimens and their alignment with

in vivo images, requiring adaptation of matching techniques for an effective comparison. These

challenges will likely result in increasing difficulty matching smaller areas of interest between

histology and MRI. In order to avoid these problems, and to reproduce the clinical reporting of

carotid artery IPH, we therefore chose the consensus decision of expert readers as the gold

standard for comparison.

2.4.1 Limitations

Comparisons were made with expert readers who define IPH as high signal above the threshold of

1.5 times the intensity of the SCM and not with histology. This comparison was chosen as none of

the included patients were candidates for carotid endarterectomy as well as the shortcomings with

histological sample processing.

54

Even though our results favoured the 1.5 intensity ratio using a particular ROI method, there was

little difference among the highest sensitivity-specificity combinations for each ROI method. It

should also be noted that this study used a single 3D-T1w GRE sequence and these results are not

necessarily applicable to other sequence acquisitions aiming to measure high signal areas within

the carotid artery such as 2D spin echo acquisitions.

2.4.2 Future Directions

Semi-automated methods have been developed for carotid artery image assessment, as

demonstrated by previous studies for 2D sequence analysis 68,100,101

, but have neither been

employed in large clinical studies nor applied to 3D MRI acquisitions. A dedicated semi-

automated protocol for IPH measurement could lead to faster image processing with greater

accuracy and reproducibility across institutions and over time. These results therefore may

encourage the use of a semi-automated technique for 3D MRI sequence analysis in subsequent

studies, improving IPH identification and quantification between sites and studies. This is relevant

for multicentre and longitudinal clinical trials aiming to measure IPH quantitatively or

qualitatively for trial inclusion, or as an outcome looking at progression or regression.

As IPH detection becomes incorporated into routine clinical practice there will be a need to

rapidly measure this clinically useful imaging biomarker. Quantification of small volumes of

hemorrhage may allow earlier identification of IPH positive patients, which could have

implications for early disease management. This could also provide greater insight into the

pathobiology of plaque hemorrhage formation, progression and regression.

55

2.5 Conclusion

This semi-automated method for carotid artery IPH quantification has high accuracy compared

with expert readers for areas >0.1mm2 on 3D-MRI using a vessel wall/SCM ratio of 1.5. The

SCM intensity was defined by an ROI at the level of the carotid artery bifurcation.

56

Chapter 3

Intraplaque hemorrhage in Type 2 Diabetic Patients and its

Association with Non-Stenotic Carotid Artery Wall

Volume

57

Intraplaque hemorrhage in Type 2 Diabetic Patients 3

and its Association with Non-Stenotic Carotid Artery Wall Volume

3.1 Introduction

Diabetes is a burden on healthcare systems with a prevalence of 9% worldwide and responsible

for a death toll of around 5 million72

. Approximately 50% of deaths are related to end organ

events of cardiovascular disease, specifically from heart disease and stroke occurrence. Diabetic

patients are predisposed to ischemic stroke occurrence, related to large artery atherosclerosis, and

have higher mortality rates than the general population102

. Prevention of these events can be

achieved by improving risk quantification, such as detection of predictive biomarkers of carotid

artery atherosclerotic disease at an earlier stage.

Current guidelines for treatment of carotid artery disease rely heavily on criteria developed from

the North American Symptomatic Carotid Endarterectomy Trial (NASCET), where the degree of

stenosis plays the major role in determination of risk and subsequent treatment14

. Ischemic stroke

occurrence, however, has been described across all levels of carotid stenosis. The carotid artery

wall thickness, represented by intima-media thickness (IMT) on ultrasound, was shown to be

useful in assessment of the risk of future cardiovascular events, including ischemic strokes33

. With

the advent of magnetic resonance imaging (MRI), characterization and measurement of carotid

artery atherosclerotic plaque became possible. Evidence has emerged, suggesting that certain

plaque features also indicate an increased risk of having a cerebrovascular event61

. Measurements

are performed using 2-dimensional (2D) and/or 3-dimensional (3D) acquired MRI sequences,

with the convention focused mostly on 2D methods16

.

Intraplaque hemorrhage (IPH) is a feature of complicated atherosclerotic plaque, which is thought

to drive progression leading to plaque destabilization and disruption, triggering an ischemic

stroke53

. Its role in atherothrombosis has been studied since the 1930s and has become more

frequent with increasing MRI availability. Its presence in carotid artery disease is predictive of

future cerebrovascular events, conveying a 5-6 fold increased risk in patients with >30%

stenosis48

. Even in the absence of stenosis, it has been implicated in cryptogenic stroke

occurrence103

. The presence of IPH may contribute to an increased size of the carotid wall, which

could provide an indicator of risk at an earlier stage than stenosis52

.

58

In this study, we hypothesized that IPH could be found in the carotid arteries of asymptomatic

type 2 diabetic patients, in the absence of stenosis, and would be associated with an increased size

of the vessel wall. We aimed to identify the prevalence of IPH within a cohort of type 2 diabetic

patients without carotid artery stenosis and observe the effect of IPH presence on the volume of

the vessel wall as well as carotid IMT.

59

3.2 Methods

3.2.1 Study Sample

The Low Glycemic Index for Type 2 Diabetics study (clinical trial number NCT01063374;

clinicaltrials.gov) is an ongoing prospective clinical trial designed to study the effects of a low

glycemic index diet versus a high cereal fibre diet on carotid artery plaque volume in type 2

diabetic patients over a 3 year period. Between March 2010 and June 2013, 169 patients with type

2 diabetes were recruited from the general public, diabetes clinics and education programs

through advertisements. Patients were deemed eligible if they were on oral hypoglycemic agents

with an HbA1c between 6.5 and 8.0%, had diabetes > 6 months, maintained a stable weight over

the previous 2 months and were on stable blood pressure and anti-lipid medication doses. A

maximum carotid artery IMT >1.2mm at the common carotid artery, carotid bulb or internal

carotid artery was also required32

. Exclusion criteria included participants on insulin, steroids or

warfarin, having a major cardiovascular event in the past 6 months, those with significant liver or

gastrointestinal diseases, and conditions preventing them from having an MRI. Clinical

assessments were performed at a single institution with all imaging performed at a second

institution. This study was approved by the respective research ethics boards of the participating

facilities and all patients involved gave written consent.

3.2.2 Carotid Artery IMT Measurement

Two-dimensional (2D) B mode carotid ultrasound (CUS) was used to measure the carotid IMT104

with the Philips iU22 Ultrasound system (Philips Healthcare, Andover, MA, USA). All imaging

was performed by a single, trained and certified sonographer, obtaining 6 measurements per

carotid artery from the near and far walls of the common carotid artery (CCA), carotid bulb and

internal carotid artery (ICA)105,106

. A maximal IMT >1.2mm at any of these locations was needed

for enrollment, while the mean maximal carotid IMT (mm-CIMT), which averaged the 6

measurements per carotid, was used to represent the entire carotid segment.

60

3.2.3 MRI Protocol

MR imaging was performed using a 3-Tesla MR scanner (Philips Achieva) with a 16-channel

neurovascular coil (16-NV-SENSE). The index carotid artery was defined by the carotid IMT

measurement >1.2mm, or the larger IMT when both satisfied this condition. Patients were

centered at the index carotid artery bifurcation and shimming done for a 10cm region over the

neck to ensure B0 homogeneity. The 3-dimensional (3D) sequences acquired were a T1-weighted

gradient recalled echo sequence in the coronal plane used for IPH detection (3D-MRIPH) and a

time-of-flight (TOF) sequence in the axial plane. Imaging parameters were as follows- 3D-

MRIPH: number of slices 100; slice thickness 0.5mm; repetition time 11ms; echo time 4ms;

inversion time 560ms; flip angle 150; number of excitations 2; field of view 270mm; matrix size

272x224. 3D-TOF: number of slices 160; slice thickness 1.4mm; repetition time 26ms; echo time

3ms; flip angle 180; number of excitations 1; field of view 190mm; matrix size 360x232. 2D

imaging parameters are described in Table 2.

3.2.4 Carotid Artery Image Analysis

Carotid artery MRI scans for 169 participants were available for analysis using a semi-automated

image processing protocol with the software VesselMASS (Medis, Netherlands). Bilateral carotid

arteries were analyzed independently for each participant by a single reader, who was blinded to

the clinical data. Image quality was assessed using a 5 point scale (Appendix 1). Carotid arteries

rated above 2 were included in the final analysis.

The carotid artery bifurcation was identified on the 3D-TOF sequence and the reformatting

parameters centered at this point. A 16 slice, 2mm thickness axial reformat was created for each

of the 3D-TOF and 3D-MRIPH sequences. This covered a 32mm segment with 16mm proximal

and distal to the carotid artery bifurcation for every carotid artery. Inter-sequence alignment was

automated before manual confirmation using the carotid bifurcation as the reference slice.

Adjustments were performed where necessary.

The 16 slice reformats created for each carotid artery were used to obtain vessel wall

measurements and for detection of IPH. The lumen was automatically generated on the 3D-TOF

sequence after identification of its midpoints. Manual correction of the contours was performed

61

where necessary. These contours were then registered to the lumen of the 3D-MRIPH sequence,

which was used to contour the outer vessel wall. This was detected automatically by the software,

and manual correction performed where necessary. The procedure is summarized in Figure 11.

For IPH detection, the mean intensity of the adjacent sternocleidomastoid muscle was obtained at

the level of the carotid artery bifurcation within a 0.20mm2 region of interest (ROI). A threshold

for IPH detection was set to 150% of this mean intensity and all areas falling above that were

detected and measured within the vessel wall (Figure 12). The volume of the vessel wall (VWV)

and any detected IPH (Figure 13) were generated.

3.2.5 Covariates and Factors

Hypertensive patients were defined as individuals who were on anti-hypertensive medication, or

those not on medications with at least 3 successive systolic blood pressure readings >130 mmHg.

Dyslipidemia was defined as participants with an LDL-C >2.0mmol75

at any of their previous 3

visits regardless of statin use, because most were using statins as part of diabetes treatment.

Patients on statin therapy were described separately. Smoking was categorized into 3 groups –

non-smokers, former smokers and current smokers, relative to the time of recruitment. Current

smokers included those who had smoked within the past year, past smokers included those who

quit for more than a year but smoked within the last 15 years, while non-smokers included those

who quit more than 15 years ago and persons who never smoked107

. All other parameters used lab

results and measurements from the last clinical visit, closest to the time of MR imaging.

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Figure 11. Summary of image processing protocol. Carotid artery bifurcation located on 3D-TOF image

(A), corresponding to red centering line on 3D-MRIPH (B) used to create 16-slice axial reformat of 32 mm

segment. Lumen (red) contoured on 16 axial slices of 3D-TOF (C) before outer wall (green) contoured on

3D-MRIPH (D) where IPH was detected (blue highlighted region).

63

3.2.6 2D and 3D Wall Volume Measurement Comparison

Current carotid wall quantification measurements focus on 2D imaging, so volume measurements

of the defined carotid artery segment were compared based on the independently analyzed 2D and

3D sequences. All available 2D- T1 weighted, post contrast T1 weighted and T2 weighted

sequences were examined (n=159) and those where the bifurcation was truly centered on the

index carotid artery, on axial slice 9, were included. Outer wall and lumen contours were

delineated using the available 2D sequences and the volumes of the lumen, outer wall and vessel

wall were generated. These values were compared with the corresponding results of the 3D image

analysis.

3.2.7 Statistical Analysis

Demographic data was expressed as the means and standard deviations for continuous variables,

with comparisons between IPH positive and IPH negative groups performed using the two sided t-

test. Discrete data was expressed as a percentage and comparisons made using the Fisher’s Exact

test. The prevalence of IPH was reported as a percentage of the cohort. Statistical significance was

defined by a p value < 0.05.

A Generalized Estimating Equation (GEE) with an unstructured correlation matrix was used to

determine the association of predetermined parameters with IPH, accounting for 2 carotid arteries

per participant. Triglyceride levels and vessel wall volume (VWV), which were significant in the

unadjusted comparison, were included in both models, as well as factors previously described to

be associated with IPH, namely age, sex, diastolic blood pressure and smoking45,46

. The second

model added variables that are known cardiovascular risk factors such as BMI, hypertension,

dyslipidemia and anti-platelet medication use. Results were expressed as odds ratios with their

95% confidence intervals. Comparison of mean VWVs and mm-CIMTs between IPH positive and

IPH negative sides was performed within the 32 patients who had unilateral IPH.

GEEs were used to determine the association of cardiovascular risk factors with the outcomes of

VWV and mm-CIMT. The cardiovascular risk factors of age, sex, hypertension, BMI,

dyslipidemia, smoking status as well as IPH presence and disease duration were included. The

Pearson correlation coefficient was also used to compare VWV and IMT measurements and a

64

multivariate linear regression model was used to determine the predictive value of mm-CIMT for

VWV.

Reliability statistics in the form of the intraclass correlation coefficients (ICCs)97

, using absolute

agreement, were performed between the 2D and 3D volumetric measurements. A mean-difference

plot was used to show the bias between these 2 methods for comparing the volume of the vessel

wall. Statistical analyses were performed using IBM SPSS Statistics (Version 22.0. Armonk, NY).

65

Figure 12. Detection of IPH in consecutive axial images. Images 1-4 show consecutive sections of the

ICA before (A) and after (B) contouring and detection. Lumen (red) and outer wall (green) contours shown

in B with blue highlighted region showing detected IPH as intensities >1.5 times the SCM.

66

Figure 13. Axial images with detected IPH from 3 different carotid arteries in the absence of stenosis.

Lumen (red) and outer wall (green) contours shown with blue highlighted regions depicting high signal

compared with the SCM intensity. Image 1 shows a right CCA. Image 2 shows a right carotid bulb. Image

3 shows a left ICA.

67

3.3 Results

3.3.1 Patient Characteristics

A total of 159 patients were included in the final analysis after exclusion of 10 patients based on

poor image quality (n=8), missing sequences (n=1) and missing clinical data (n=1).

Characteristics of this cohort are described in Table 6. The average age was 62 years and males

accounted for 62% of the cohort. The mean BMI was 29.7 kg/m2, suggesting patients tended to be

overweight. Duration of diabetes was around 9 years with considerable variability, while HbA1c

displayed low variability due to the strict inclusion criteria.

There were 37 participants with carotid artery IPH detected, with 5 of these having the feature

bilaterally. This gave a prevalence of 23.3% within the cohort of 159 patients. The locations of

detected IPH are depicted based on the right and left carotid arteries (Figure 14). IPH occurred

more frequently in the left carotid artery (p=0.07), and predominated in the ICA among the 3

defined regions.

3.3.2 Intraplaque Hemorrhage

There was little difference noted in the patient characteristics between the IPH positive and

negative groups (Table 7). Age, male sex, blood pressure parameters and smoking status were not

significantly associated with IPH in the unadjusted comparison. The mean triglyceride level,

however, was significantly higher in the non-IPH group. When comparing the carotid artery

characteristics, both volume and thickness measurements were significantly higher in the IPH

positive group.

Model 1, which compared the triglyceride level and VWV measurement, and the factors of age,

sex, diastolic blood pressure and smoking history with the outcome of IPH, showed that when

these other factors were accounted for, the triglyceride level was not significantly associated with

IPH but VWV was (Table 8). Each standard deviation increase in VWV (258mm3) meant the

subject was twice as likely (p<0.01) to have IPH in their carotid wall.

Model 2 used the same parameters of Model 1, and included the other cardiovascular risk factors

of BMI, hypertension, dyslipidemia and anti-platelet medication use. Again, only VWV

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measurement showed a significant difference, increasing the likelihood of having IPH by 2 times

(p<0.01) for each standard deviation increase in volume when accounting for these factors.

3.3.3 Effect of IPH on Carotid Wall Volume

Mean VWV measurements were compared within participants with unilateral IPH (n=32)

between the IPH positive and IPH negative carotid arteries. Error bars (Figure 15) reflect the

significantly larger wall volume (p<0.05) on the IPH positive sides. The IPH negative carotids in

those patients with disease showed no difference against the mean VWV of the rest of the study

cohort IPH negative carotids (n=244).

The measured volume of IPH was subtracted from the total VWV in the IPH positive carotids and

the mean comparison with the IPH negative carotids repeated. A significant difference between

these values was again seen (p<0.05), suggesting that there were other processes which

contributed to the expanding arterial wall besides the volume of hemorrhage.

The means of the mm-CIMT measurements were also compared within the participants with

unilateral IPH between the IPH positive and negative sides. The results were similar to those of

the VWV comparison, showing that mm-CIMT was larger in the IPH positive side (p<0.05),

while the IPH negative side was no thicker than the arteries in the rest of the study cohort.

69

Characteristics Value

Patients, n 159

Age, y 61.9 + 7.7

Male Sex, n (%) 99 (62.3)

BMI, kg/m2 29.7 + 5.0

Disease Duration, y 8.7 + 7.1

HbA1c, % 7.1 + 0.6

Waist circumference, cm 103.4 + 11.6

Blood pressure, mmHg Systolic Diastolic Pulse Pressure

124.5 + 13.0 71.8 + 9.4 52.6 + 10.3

Total Cholesterol, mmol/l HDL LDL Triglycerides

3.9 + 1.0 1.1 + 0.3 2.1 + 0.9 1.5 + 0.9

Hypertension, n (%) 118 (74.2)

Statin use, n (%) 116 (73.0)

Dyslipidemia, n (%) 98 (61.6)

Anti-platelet medication, n (%) 95 (59.7)

Smoking history, n (%) Current smoker Ex-smoker Non-smoker

11 (6.9) 20 (12.6) 128 (80.5)

IPH Positive, n (%) 37 (23.3)

Table 6. Demographic data and characteristics of study population.

70

Figure 14. Distribution of IPH by carotid artery side and location. Shown within the internal carotid artery

(ICA), carotid bulb and common carotid artery (CCA). IPH occurred more frequently on the left (p=0.07)

and in the ICA.

71

Characteristics by Patient (n=159)

With IPH (n=37)

Without IPH (n=122)

p value

Age, y 61.4 + 7.7 63.5 + 7.6 0.16

Males, n (%) 22 (59.5) 77 (63.1) 0.70

BMI, kg/m2 30.0 + 5.9 29.6 + 4.7 0.71

Disease Duration, y 8.6 + 5.7 8.7 + 7.5 0.92

HbA1c, % 7.1 + 0.5 7.1 + 0.6 0.95

Waist Circumference, cm 104.6 + 12.2 103.0 + 11.5* 0.47

Systolic, mmHg 123.5 + 13.5 124.7 + 12.8 0.63

Diastolic, mmHg 69.5 + 10.2 72.5 + 9.1 0.10

Pulse Pressure, mmHg 54.1 + 8.7 52.2 + 10.8 0.29

Total Cholesterol, mmol/l 3.8 + 0.8 4.0 + 1.1 0.15

HDL, mmol/l 1.2 + 0.3 1.1 + 0.3 0.09

LDL, mmol/l 2.0 + 0.7 2.1 + 0.9 0.19

Triglycerides, mmol/l 1.2 + 0.6 1.6 + 1.0 0.01

Hypertension, n (%) 31 (83.8) 87 (71.3) 0.14

Statin Use, n (%) 31 (83.8) 85 (69.7) 0.14

Dyslipidemia, n (%) 20 (54.1) 78 (63.9) 0.30

Anti-platelet medication, n (%) 21 (56.8) 74 (60.7) 0.71

Smoking, n (%) Non-smoker Former smoker Current smoker

29 (78.4) 4 (10.8) 4 (10.8)

99 (81.1) 16 (13.1) 7 (5.7)

0.56 0.31 0.33

Characteristics by Carotid (n=318)

With IPH (n=42)

Without IPH (n=276)

p value

Mean-Max IMT, mm 1.35 + 0.36 1.12 + 0.31 <0.01

VWV, mm3 1370 + 296 1229 + 247 <0.01

Table 7. Risk factors and measurements associated with patient and carotid artery characteristics. BMI –

body mass index, HDL – high density lipoprotein, LDL – low density lipoprotein, IMT – intima media

thickness, VWV – vessel wall volume.

* Data for 1 patient was missing.

72

Parameter MODEL 1 MODEL 2

OR (95% CI) p value OR (95% CI) p value

Age, years 1.02 (0.96 – 1.07) 0.58 1.02 (0.96 – 1.08) 0.52

Sex (Female), n 1.96 (0.86 – 4.51) 0.11 1.74 (0.69 – 4.35) 0.24

Diastolic 0.97 (0.93 – 1.02) 0.22 0.98 (0.93 – 1.02) 0.31

Triglycerides, mmol 0.77 (0.40 – 1.45) 0.41 0.69 (0.36 – 1.32) 0.26

Smoking history

Current smoker

Former smoker

1.62 (0.45 – 5.82) 0.57 (0.18 – 1.86)

0.46 0.35

1.62 (0.48 – 5.45) 0.56 (0.17 – 1.86)

0.44 0.34

VWV (per 1 sd), mm3 2.11 (1.35 – 3.30) <0.01 1.93 (1.22 – 3.06) <0.01 BMI, kg/m2 --- 1.02 (0.94 – 1.12) 0.58

Hypertension --- 1.76 (0.70 – 4.40) 0.23

Dyslipidemia --- 0.88 (0.36 – 2.15) 0.78

Anti-platelet Medication --- 0.94 (0.39 – 2.27) 0.90

Table 8. Associations between risk factors and IPH. Model 1 is adjusted for age, sex, diastolic blood

pressure, triglycerides, smoking history and vessel wall volume (VWV). Model 2 includes the risk factors

in Model 1 plus body mass index (BMI), hypertension, dyslipidemia and anti-platelet medication.

73

Figure 15. Error bars comparing mean carotid wall volumes and mean carotid IMTs. Solid bar at centre

represents means for 32 patients with unilateral IPH positive carotid artery, and X represents mean for the

remainder of the non-IPH cohort. Whiskers represent 95% confidence intervals.

Figure A compares the mean volumes of the IPH positive side (a), the IPH positive side after deduction of

the measure IPH volume (b) and the IPH negative side (c). The mean volume of the carotid arteries in the

non-IPH cohort (n=244) is shown as (d).

Figure B compares the means of the mean-maximum carotid IMT (mm-CIMT) showing the mean values

for the IPH positive side (a) and the IPH negative side (b). The mean value for the carotid arteries in the

non-IPH cohort (n=244) is shown in (c).

A Ba

a

b

c db

c

p=0.95

p<0.05

p=0.32

p=0.49

p<0.05

p<0.05

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3.3.4 Vessel Wall Volume and Carotid IMT

The volume of the defined segment of carotid artery wall was used as the outcome for Model 1,

where the associations with various cardiovascular risk factors were determined. As shown in

Table 9, the VWV was significantly affected by a patient’s age, sex, BMI and the presence of

IPH. This held true after adjusting for these variables, as well as other cardiovascular risk factors

including hypertension, dyslipidemia, smoking status and duration of diabetes, which did not

demonstrate significant associations. This suggested that the volume of the vessel wall increased

by 7mm3 (p<0.01) for each additional year of age, with an 8mm

3 (p<0.01) increase for each kg/m

2

unit rise. The average difference between IPH positive and negative carotid arteries was 150mm3

(p<0.01) and females had a 207mm3 (p<0.01) smaller vessel wall volume than males.

The mm-CIMT for each carotid segment was also compared with the same cardiovascular risk

factors with both age and IPH presence showing significant associations (Table 9). This suggested

that the mm-CIMT increased by 0.01mm for each additional year of age (p<0.01) and the average

difference between IPH positive and negative carotids was 0.21mm (p<0.01).

Figure 16 shows the comparison of VWV with mm-CIMT producing a moderate correlation with

r=0.36 (p<0.01). Average measures for both sides were used to assess the predictive capacity of

the mm-CIMT using a multivariate linear regression model, adjusting for the significant factors

shown to affect VWV, namely age, sex and BMI. This produced the equation below, with each of

the included variables proving significant (p<0.05) as shown in Table 10, with R2=0.35

(bootstrapped 95% CI: 0.26, 0.44)

Mean carotid wall volume (mm3) = 443.3 + [293.4*Mean mm-CIMT (mm)] – [220.6*Female

sex] + [5.4*Age (years)] + [7.4*BMI (kg/m2)]

75

Parameter

Model 1: Vessel Wall Volume Model 2: Mean-Maximum Carotid IMT

B SE p 95% CI

B SE p 95% CI

Lower Upper Lower Upper

Intercept 612.0 168.7 <0.01 281.4 942.6 0.29 0.21 0.17 -0.13 0.70

Age, years 7.0 2.4 <0.01 2.4 11.6 0.01 0.00 <0.01 0.01 0.02

Sex (Female) -206.5 32.3 <0.01 -269.7 -143.3 0.02 0.04 0.57 -0.06 0.11

BMI, kg/m2 8.3 3.0 <0.01 2.3 14.2 0.01 0.00 0.14 -0.00 0.01

Duration, years 1.9 2.3 0.41 -2.6 6.4 0.00 0.00 0.88 -0.01 0.01

Hypertension 1.9 38.6 0.96 -73.8 77.6 -0.03 0.05 0.51 -0.14 0.07

Dyslipidemia -19.9 34.2 0.56 -87.0 47.1 0.02 0.04 0.64 -0.06 0.10

Smokers Former Current

56.4 19.3

52.1 66.0

0.28 0.77

-45.8 -110.1

158.5 148.7

0.11 0.12

0.08 0.08

0.16 0.16

-0.04 -0.05

0.25 0.29

IPH (positive) 150.3 36.3 <0.01 79.2 221.4 0.21 0.06 <0.01 0.09 0.32

Table 9. Associations between risk factors and the outcomes of: Model 1 - vessel wall volume (VWV),

Model 2 - mean-maximum carotid intima media thickness (mm-CIMT).

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Figure 16. Scatterplot of mean-maximum carotid IMT versus vessel wall volume. There is moderate

correlation, shown by r=0.36 (p<0.01), between the mean thickness and volume of each carotid segment.

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Parameter B Standard

Error

95% CI

Significance

Lower Upper

Constant 443.3 167.1 113.1 773.4 <0.01

Mean IMT 293.4 59.0 176.8 410.0 <0.01

Sex (Female) -220.6 32.5 -284.8 -156.4 <0.01

Age 5.4 2.1 1.2 9.5 <0.05

BMI 7.4 3.2 1.0 13.7 <0.05

Table 10. Multivariable linear regression model for prediction of VWV from mm-CIMT, sex, age and

BMI.

3.3.5 3D and 2D MRI Volume Comparison

There were 46 2D-MR imaged carotid arteries that captured the segment measured by the 3D

sequences and volume measurements were compared. Intraclass correlation statistics were near-

perfect for all parameters with lumen volume at 0.86, outer wall volume at 0.95 and vessel wall

volume at 0.87 (Appendix 2). There was minimal bias between measurements, with 3D VWV

measurements falling at 38mm3 below the 2D VWV (Figure 17). The distribution of the carotid

artery bifurcation on 2D acquisition was also plotted (Figure 18). This highlighted the benefit of

3D measurements, because even though the aim is to center the patient so the bifurcation falls at

slice 9 (beginning of ICA), there is a considerable variation in the actual position obtained,

making it difficult to compare a standard segment across all patients.

78

Figure 17. Mean difference plot showing 3D to 2D comparisons of the measured carotid wall volume.

Data was available for 46 cases and showed a minimal mean bias (bold line) of 38mm3 (p=0.11) within 2

standard deviations in each direction (dotted lines).

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Figure 18. Location of the carotid artery bifurcation with 2D MRI acquisition. The bifurcation of the

index carotid artery should fall on slice 9 (n=46), where the internal carotid artery (ICA) begins. For these

159 cases, the acquisition of the carotid bifurcation peaks on slice 8 (n=64) for the index carotid artery.

Variability in the distribution of the acquired non-index carotid artery bifurcation is noted.

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3.4 Discussion

This study showed that in a cohort of asymptomatic diabetic patients, 23% of participants had

carotid artery intraplaque hemorrhage in the absence of stenosis and an IMT measurement

>1.2mm. The presence of IPH was associated with a higher carotid artery wall volume

represented by a 32 mm segment centered at the bifurcation and measured using two 3D-MRI

sequences. This difference was not accounted for by the measured volume of IPH. The volume of

the vessel wall was found to be influenced by the presence of IPH, which also affected the

measured mm-CIMT. However, there was only moderate correlation noted between these 2

measures.

The results of this study provide some insight into the predisposition of diabetic patients to

advanced vascular disease, as a type VI atherosclerotic lesion was found in these patients in the

absence of any luminal occlusion measured using the technique outlined in the NASCET. The

prevalence of IPH was found to be almost 1 in 4 subjects in this diabetic cohort. This is a

surprising result, but there is a lack of comparable data to conclude whether it is indeed an

abnormal one. Considering the implications of cerebrovascular disease in this population related

to increased risk, morbidity and mortality, the early identification of IPH could prove to be a

predictive biomarker in this population, which may assist efforts in management of carotid artery

atherosclerotic disease.

The carotid artery measurement in this study was novel, with the use of 3D MR sequences for

volumetric quantification of a specific segment. This technique allowed for comparison of both

carotid arteries across all patients, with the advantage of 3D imaging apparent, as a constant

defined segment could be processed post-acquisition, versus the variability of the segment

acquired on 2D imaging. The excellent measurement reproducibility between the 2 techniques

showed that they can perform similar measurements. Many studies have focused on the carotid

bifurcation, as atherosclerotic lesion formation is thought to begin at such areas with eccentric

intimal thickening. Additionally, the prevalence of atherosclerotic lesions classified as type VI,

which includes IPH, was found to be highest within the 32mm segment around the bifurcation.

Increased age, male sex, positive smoking status as well as lower diastolic blood pressure and

wider pulse pressures are considered to be positively associated with IPH. Pulse pressure was

excluded, as diastolic blood pressure formed part of the primary model and hypertension status

81

was added to the secondary model with none of these factors showing a significant difference in

the unadjusted comparison. In addition, the significance attributed to the unadjusted levels of

serum triglycerides led to its inclusion. The eventual model showed that there was an association

with VWV without a significant effect of these included variables.

The presence of IPH was significantly associated with a higher VWV and mm-CIMT of the

defined carotid wall segment compared with non-IPH carotids. IPH may be directly related to

processes that contribute to an increased size of the vessel wall. Even with the exclusion of the

detected hemorrhage volume, the difference was still noted, which suggests that IPH either

triggers or results from local processes that increase the size of the carotid artery wall.

Age, male sex, BMI and presence of IPH were found to be associated with increased VWVs.

These changes might reflect global vasculature changes which would be important when

considering vessel volumes between male and female patients, as well as those with differing

BMIs. Further development of this concept could lead to formulation of a standard baseline

carotid artery measurements before the onset of disease is suspected.

The relationship of IMT measurements may prove beneficial in the determination of patients who

may benefit from further imaging for risk of more advanced disease, such as IPH. This could

impact management guidelines and would place less emphasis on stenosis measurements, as

patients can exhibit complicated atherosclerotic lesions without stenosis.

3.4.1 Limitations

Limitations of this study included the lack of a control group, or a non-diabetic population, to

compare with this cohort. This would have provided a better perspective on the measurements and

results obtained. A second limitation included the size of the sample cohort used. There are many

other cardiovascular risk factors that are thought to impact the arterial wall and atherosclerotic

disease, but could not be included in the eventual models. Also, the measurement of IPH was

done using an imaging standard and not histological correlation. The imaging diagnosis of IPH is

well accepted, however, and though histological processing for measurement of IPH is shown to

have many challenges, these patients were not candidates for carotid endarterectomy due to the

non-stenotic disease and asymptomatic nature. Current clinical guidelines do not take IPH

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presence into account, and only patients with moderate to severe carotid stenosis have been shown

to benefit from such procedures. Sampling of patients was quite stringent therefore the results

may be applicable to patients without carotid stenosis who have relatively well controlled

diabetes, based on HbA1c, and other co-morbidities. Results would not reflect the findings in the

carotid arteries of all diabetic patients.

3.4.2 Future Directions

Follow up imaging for these patients in the near future will provide some insight into the effects

of the MR-detected IPH, related to progression of the carotid plaque. Any cerebrovascular event

occurrence would also be important and could possibly establish causal relationships. These

results will provide a baseline comparator for future studies aiming to identify IPH in non-stenotic

carotid artery disease. A healthy population would be a useful comparison for these results to

further indicate whether IPH prevalence in diabetes is higher than normal. A benefit of this study

was the advantage of 3D MRI sequence use over 2D use in carotid artery measurement, which

could be applied to a dedicated carotid artery imaging protocol. Volumetric results showed some

correlation with the mm-CIMT, and as shown by the predictive model, further emphasis could be

placed on using mm-CIMT measures to anticipate patients suspected of having IPH who would

benefit from MR-imaging of the carotid arteries.

83

3.5 Conclusion

Intraplaque hemorrhage can be found in carotid arteries without stenosis in an asymptomatic

diabetic population with a prevalence of 23% in this cohort. Its presence within a 32mm segment

centered at the bifurcation is associated with a larger carotid wall volume, not accounted for by

the volume of hemorrhage, and increased wall thickness. The volume of this carotid artery

segment is larger with the presence of IPH, male sex, increasing age and a higher BMI.

84

Chapter 4

General Discussion

85

General Discussion 4

The hypothesis of this work was that IPH, as detected by MRI (Aim 1), is present in the non-

stenotic carotid arteries of asymptomatic diabetic patients (Aim 2) and these IPH positive carotid

arteries are associated with a larger volume of the carotid artery wall (Aim 3).

The objective of this thesis was to investigate the presence of intraplaque hemorrhage in the

carotid arteries of an asymptomatic diabetic population without carotid artery stenosis (CAS).

This objective was addressed using three specific aims:

Aim 1: to develop a semi-automated method using MRI, for measuring small quantities of IPH

with the accuracy of expert readers (Chapter 2).

Aim 2: to identify the prevalence of IPH within a diabetic patient cohort (Chapter 3).

Aim 3: to investigate the associations between the volume of the carotid vessel wall and the

measured IPH (Chapter 3).

Aim 1: I found that the semi-automated method developed had the highest accuracy for MR

vessel wall high signal detection, representing IPH, above a signal intensity threshold set at 1.5

times the SCM intensity. The optimal measurement of the SCM intensity was obtained by using a

fixed ROI of 20mm2 adjacent to the carotid artery lumen, at the level of the carotid bifurcation.

This SCM measurement technique best accounted for the heterogeneous intensities within the

SCM. The semi-automated method developed achieved a sensitivity of 80% and specificity of

100% for detection of IPH compared with expert readers for IPH areas from 0.1mm2 and above.

Aim 2: The developed semi-automated method was used for the detection and measurement of

IPH within the cohort of diabetic patients. I found that 23% of the analyzed 159 patients did have

IPH present in at least 1 carotid artery, with 5 patients having the feature bilaterally. The IPH

measured included volumes of 0.1mm3

and above. None of these patients had any carotid artery

stenosis, but did have an IMT measurement of 1.2mm or greater in at least 1 of the carotid

arteries.

Aim 3: I found that IPH was associated with a larger carotid artery wall volume compared with

the non-IPH carotid arteries, using the measurement of a defined 32mm segment of carotid artery,

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for each of the right and left carotid arteries, across all patients. The larger VWV in the IPH

positive carotid arteries was independent of age, sex, diastolic blood pressure, triglyceride level

and smoking status. Of note, the volume of IPH did not account for the VWV increase, suggesting

that other factors related to IPH were contributing to the VWV increase.

In response to the hypothesis, IPH, as detected by MRI (Aim 1), was found in the carotid arteries

of 23% of asymptomatic type 2 diabetic patients in the absence of carotid artery stenosis (Aim 2)

and these IPH positive carotid arteries were associated with a larger volume of carotid vessel wall,

which was independent of the measured hemorrhage volume (Aim 3).

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4.1.1 Development of the Hypothesis and Related Aims

Intraplaque haemorrhage, classified as a type VI lesion by the American Heart Association, is

associated with the occurrence of future cerebrovascular events. A meta-analysis by Saam et al.

(2013) demonstrated that the presence of IPH conveyed a 5 to 6 times increased risk of having a

future cerebrovascular event. However, the increased risk was only studied in patients with 30%

carotid stenosis and greater. There was limited data available for patients with less than 30%

stenosis, so the conclusions from this study could not be extended to patients with lesser degrees

of stenosis. However, the study by Freilinger et al. (2012) implicated a relationship between IPH

presence and cerebrovascular event occurrence in the absence of carotid artery stenosis. These

results suggested that IPH could occur at any percentage of stenosis and was still a risk factor for

cerebrovascular event occurrence.

Diabetes predisposes patients to accelerated atherosclerotic plaque development and is associated

with increased cerebrovascular events. The Canadian Diabetes Association in its 2013 guidelines

state that, in addition to the higher risk of events, the resulting morbidity and mortality rates are

higher. A biomarker that predicts cerebrovascular events has the potential to help risk stratify and

manage diabetic patients.

MRI can characterize atherosclerotic plaque, including the identification of IPH. A 3-dimensional

sequence (MRIPH) identifies IPH as an area of vessel wall high signal intensity. Compared with

histology, there is high sensitivity and specificity for hemorrhage detection as shown by Moody et

al (2003) and again by Bitar et al. (2008).

The susceptibility of diabetic patients to atherosclerotic disease and the associated increased risk

of cerebrovascular events in this population led to development of the hypothesis and aims of this

thesis. Knowing that IPH is associated with high-risk (stenotic) carotid disease, I hypothesized

that other causes of high risk (diabetes) may also be associated with IPH, and this in turn may be

related to the accelerated development of vessel wall disease. Using MRI, carotid artery IPH was

investigated within an asymptomatic diabetic population with early atherosclerotic disease, but no

measurable stenosis. This study allows the measurement of the prevalence of IPH and a

measurement of vessel wall volume to distinguish any differences that might be associated with

the effects of IPH.

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4.1.2 Measurement of MRI detected IPH

The first aim of this thesis was to develop a semi-automated method for detection and

quantification of carotid artery IPH comparable with expert readers. In a previous histological

validation study by Bitar et al. (2008), IPH was identified as a region of high signal within the

carotid artery wall at 1.5 times the intensity of the SCM, using the 3D-MRIPH sequence. This is

the most commonly used technique for IPH identification in studies (Table 3). However, inter-

reader variability may be an important factor, particularly when detecting small areas of high

signal. Therefore, the first aim was to create a semi-automated image processing protocol for

detection and measurement of high signal intensity areas in the vessel wall, representing IPH.

This should decrease user-input related steps and improve measurement consistency, while

reducing the variability associated with manual measurements. This was especially important for

identification of small quantities of IPH, represented in this study by a minimum area of 0.1mm2.

Detection of IPH is variable depending on the threshold selected. This variability was outlined in

Chapter 2 and is shown in Figure 6, where the region measured as IPH varies depending on the

intensity ratio selected. The set threshold is determined by the intensity ratio, which leads to these

variations. Selection of the ideal intensity ratio was necessary for IPH identification, as small

regions of IPH may be seen as positive at one threshold, but negative at another.

Threshold determination is dependent on the measurement of the SCM intensity. Three methods

were investigated to determine the impact of the heterogeneous nature of the SCM intensities as

well as the impact of magnetic field variations within the 3D T1W GRE sequence used. An

optimal method was found using a constant ROI on the SCM on the same axial slice as the carotid

artery bifurcation. The SCM at this axial level was least affected by magnetic field differences

due to the effects of magnet gradient shimming. A fixed ROI intensity on the SCM at this level

was also thought to be more accurate than the intensity of the entire cross–sectional SCM because

of the large variation in SCM cross-sectional area throughout the neck, as well as its difference

between patients. A standard ROI allowed the same technique to be applied to each analyzed

carotid artery for every patient eliminating the need to outline the SCM each time and simplifying

that step of the protocol.

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The 1.5 intensity ratio is well accepted, but there was a need to investigate its use and effect on

identification of individual high signal pixels when compared with the assessment by experienced

readers. Based on the variations in the intensity ratios seen in some studies, we used an

incremental range from 1.2 to 2.2 times the mean intensity obtained at the SCM. Receiver

operating characteristic (ROC) curves were constructed to identify the sensitivity and specificity

for IPH detection across the range of intensity ratios. Each ROC curve provided a graphical

representation of the change in diagnostic accuracy across the intensity ratios and allowed the

optimal one to be chosen. The ROC curves compared the intensity ratios for each ROI method,

showing the combined effects of the SCM measurement technique and intensity ratio changes on

IPH detection. These methods also demonstrated the effects of SCM intensity heterogeneity and

magnetic field inhomogeneity, or B0 field effects.

Selection of the optimal values was achieved using the Youden Index, which represents the point

closest to the upper left corner of the graph where a perfect diagnostic test would exist. Results

showed that the semi-automated quantitative method for measuring IPH compared with expert

readers had a high degree of accuracy by identifying intensities greater than 1.5 times the intensity

of the SCM ROI, at the level of the carotid artery bifurcation. As a result, areas of high signal as

small as 0.1mm2 could be identified.

The steps to IPH detection and measurement were described based on the 3D-T1w GRE MRI

(MRIPH) sequence that was used. While this result correlated with the 1.5 intensity ratio used in

many other studies, the advantage of using ROI Method 1, was that it required the least user-

related input steps. This method, within a semi-automated protocol, therefore reduced the number

of steps required for image processing and decreased the potential for human error.

The volume of IPH is automatically generated by the VesselMASS software based on the

individual area measurements made at each slice. Because of the high accuracy of high signal

measurement for areas 0.1mm2 and above, the resulting volume measurements by the software

were also highly accurate as shown by van’t Klooster et al. (2012). The minimum detectable

volume was 0.1mm3 and was useful for addressing the second and third aims of this thesis.

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4.2 IPH Prevalence in a Diabetic Cohort

The second aim of this thesis was to apply the developed technique for IPH quantification to a

cohort of type 2 diabetic patients. The diabetic patients were asymptomatic, meaning that they had

no cerebrovascular or cardiovascular events in the 6 months prior to recruitment. In addition,

there were stringent inclusion criteria: patients need to be on stable oral anti-diabetic medication,

on stable medication doses for any other comorbidities (such as hypertension or hyperlipidemia),

and have a HbA1c value between 6.5 and 8%. One of the main inclusion criteria was the carotid

IMT, which was 1.2mm or higher for either carotid arteries at the ICA, bulb or CCA. The artery

that fulfilled this requirement was considered the index carotid artery, or in the case that both met

this condition, the artery with the larger IMT value was considered the index. All patients were

assessed and had carotid MR imaging done at baseline, or before being randomized to one of the

2 dietary arms in a randomized controlled trial. Therefore, there was no need to account for the

effects of the interventions in this thesis.

Selection of the carotid artery segment for measurement with MRI was based on evidence of type

VI lesion location, as well as the theoretical origin of atherosclerotic disease. According to the

pathophysiology of atherosclerotic development, eccentric intimal thickening occurs at branch

points of an artery, such as the carotid artery bifurcation, which is thought to be a key area for

disease occurrence. In addition, a study by Saam et al. (2008) demonstrated a preponderance of

type VI lesions for a carotid artery segment 16mm proximal and distal to the carotid bifurcation,

with the maximal occurrence within the bulb. Ultrasound measurement of the carotid artery IMT

usually occurs around the bifurcation as well, as defined by the Mannheim consensus guidelines

(2011). These conclusions helped define the 32mm segment that was chosen for investigating the

prevalence of atherosclerotic disease and, therefore, IPH within the carotid artery.

Detection of IPH in the diabetic cohort included volumes of 0.1mm3 and higher. The method

developed in Aim 1 was ideal for this purpose. The carotid artery IPH prevalence was 23% in the

diabetic cohort investigated, or approximately 1 in 4 patients. This is a high percentage of patients

with advanced atherosclerosis, such as IPH, in the absence of any carotid stenosis. However, there

was no control group to compare this result to, and little data exists within the literature for a

comparable group. This is an interesting result, however, considering the susceptibility of diabetic

patients to atherosclerotic disease development and cerebrovascular event occurrence and

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suggests that further studies in this patient group would be beneficial. If reproduced, this result

demonstrates that advanced atherosclerotic disease, normally associated with significant carotid

artery stenosis, can also be found in arteries with little or no stenosis, suggesting that significant

stenosis itself is not a prerequisite for atherosclerotic plaque complication.

Although no conclusions could be made regarding the cause of IPH or its direct clinical effects in

this cohort, the associations between IPH and other factors could be investigated using this

technique. Aim 3 was undertaken to investigate the associations between IPH and vessel wall

volume changes, controlling for demographic and comorbidity factors that might be related to

IPH.

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4.3 The Association between IPH and Carotid Wall Volume

The third aim of this thesis was to determine whether there was an association between IPH and

the volume of the carotid artery wall. IPH contributes to the progression of the carotid artery

atherosclerotic plaque, so part of the hypothesis was that the vessel wall would be larger in the

IPH positive than the IPH negative carotid arteries.

Defining the method for IPH detection and quantification in Chapter 2 formed a main part of

image processing protocol, but not its entirety. Aside from detecting and quantifying IPH, there

was the need to measure the volume of the carotid artery wall. This technique has been

traditionally performed using 2D sequences, for quantification and progression studies. Multiple,

differently weighted sequences of the desired segment are acquired at the same imaging session

and provide aligned, comparable images where individual plaque components can be identified

using inter-sequence intensity comparisons. The identifiable atherosclerotic plaque components

include the lipid core, intraplaque hemorrhage, calcium and the fibrous cap. However, there is a

critical shortcoming with using 2D MRI sequences for measuring the carotid arteries of multiple

patients, which is related to the acquisition.

The carotid artery bifurcation is commonly used to center the MR-imaging slices as the majority

of disease is found in that vicinity, as well for its ease of identification as a landmark. This is

performed visually and, with slice thicknesses commonly in millimeters, there is considerable

variability in the imaging segment obtained, as depicted by Figure 18. The slice location of the

bifurcation was variable across different carotid arteries. In addition, the bifurcation of the

contralateral carotid artery is often at a different axial level; therefore, using the same 2D MR

sequences images to obtain the same number of slices for both carotid arteries in each patient

would not be possible. In contrast, 3D sequences are able to negate this issue as a larger volume is

imaged ensuring the correct volume about the carotid bifurcation is imaged even with anatomical

variability. Multi-planar reformats can then be generated for the segment of interest for each

carotid artery without loss of information. Consequently, for the cohort of 169 patients studied,

the aim was to use the same pre-defined segment for all carotids to ensure comparable

measurements. The 3D technique also has the advantage of accurate IPH detection over the 2D

sequence images.

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A reliability analysis for validation of the 3D measurements was performed and the measured

volumes of carotid wall segment using the 2D and 3D sequences were compared. Results showed

a strong correlation between measurements from the 2 modalities, with an ICC of 0.87 between

vessel wall volume measurements. This suggested that both measurement types had excellent

agreement when measuring the volume of the carotid artery wall. Therefore the use of the 3D

measurements was used, with the added advantage of being able to analyze a consistent segment

of carotid artery and detect IPH.

Predetermined models, which included factors known to influence IPH, were used to compare

associations with IPH because of the relatively small sample size. An analysis was performed to

identify the clinical factors that could have implications in the identification of IPH and possibly

be linked to its initial development and included male sex, age, smoking status and diastolic blood

pressure from studies by Bouwhuijsen et al. (2012) and Sun et al. (2016). The first model

compared associations of the above factors, including VWV and triglyceride level, with IPH. The

second model included the factors from the first model, but also accounted for the other

cardiovascular risk factors of BMI, hypertension, dyslipidemia and anti-platelet medication use.

IPH presence was significantly associated with an increase of the carotid wall volume only (Table

7), for both models. When compared with a single standard deviation of VWV change (258mm3),

an increase conveyed an approximately 2 times increased risk of having IPH. Interestingly, none

of the other factors in the models showed a significant association, which could be attributed to

the small sample size, the early stage of vessel wall disease or even the variation in risk factors for

diabetic patients versus the anon-diabetic population. These findings lead to other questions and

theories for IPH occurrence within diabetic patients; a characteristic dyslipidemia is known to

occur in diabetes and a higher incidence of CVA occurrence is noted in females. This may explain

why male sex and other factors that are suspected to be related to IPH were not found to be

significant in this population.

The vessel wall volume differences between IPH positive and negative carotid arteries might be

the result of the additional volume of blood or hematoma within the vessel walls. To investigate

this concept, the 32 cases with unilateral IPH were identified and the means of the carotid wall

volumes between the IPH positive and IPH negative sides were compared (Figure 15). The IPH

positive side was significantly larger even after accounting for the volume of measured IPH. The

difference in volume was therefore not explained by the presence of blood within the wall. The

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similarity between the contralateral non-IPH carotid and the vessel wall volumes of the carotids of

the non-IPH patients also supports the theory that hemorrhage might precede vessel wall

enlargement. The evidence for this is seen with the volume of the IPH carotid being independent

of the contralateral side in the same patient. There was no additional increased volume to the IPH

negative side, seen by its similarity with the rest of the non-IPH population. Therefore the major

difference between these volumes was IPH presence, which could have led to the increased vessel

size. Explanations for the vessel wall volume differences may be attributed to additional processes

generated by IPH suggested by Michel et al (2011), including chemotactic migration of

monocytes and macrophages into the wall, deposition of free cholesterol form degradation of

RBC membranes and necrotic core enlargement from oxidative modification of lipids by free

heme. These theories correspond with the findings in this study and correlated with the evidence

that IPH increases the size of the vessel shown by Kolodgie et al. (2003).

An added benefit of using IMT as an inclusion criteria meant that comparisons could also be

drawn with the mean-maximum carotid IMT (mm-CIMT). The mm-CIMT showed a similar

difference between the IPH positive and negative sides suggesting there was some potential for

assessing the overall carotid segment using the average of carotid artery wall thickness

measurements from the ICA, bulb and CCA. The mm-CIMT increased by 0.01mm with each

additional year of age and was 0.06mm larger in IPH positive patients. Increased age and IPH

presence were each shown to have a positive association with larger mm-CIMT measurements,

though sex and BMI were not. Interestingly, however, these factors were all found to be

significantly associated with VWV. This difference might be related to IMT and VWV

acquisition, as IMT may not always capture diseased areas of the vessel because it is confined to

the near and far walls for each carotid artery, potentially missing disease on the lateral walls. IMT

provides a 2D representation of disease, while VWV reflects changes within the entire segment,

which equate to greater sensitivity to measurement differences between patients.

The measurement results were novel in this study, as similar comparisons were performed across

all patients to determine associations between the vessel wall volume and the individual patient

characteristics. It was of note that male gender, as well as increasing age and BMI predisposed

individuals to having a higher volume of the carotid artery segment measured. Each additional

year of age and each additional kg/m2 increase in BMI conveyed a higher VWV by 7mm

3 and

8mm3 respectively. Also, males had a higher VWV than females by 207mm

3 on average, while

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IPH positive carotids were 150mm3 larger. Whether this result suggests that a higher threshold for

volumes in specific demographics should be allowed, or whether they do indeed indicate

abnormality cannot be determined from these results. Further studies including a normal

population may be able to explain the role of diabetes in in vessel wall disease. It may also

provide some insight into the carotid artery wall itself, investigating possible fundamental

differences in vessel size depending on sex, age or BMI.

The carotid IMT has been comparable with MRI measured wall thicknesses according to studies

by Duivenvoorden et al. (2009), but its association with the volume of the vessel wall is not well

documented. This initial comparison showed a moderate correlation, with r=0.36, suggesting that

finding the mean of 6 maximum IMT measurements around the carotid bifurcation does bear

some similarity to the measured VWV. After finding which variables affected VWV, a

multivariable regression model was created to calculate the predictive potential of mm-CIMT.

This produced the described equation (Chapter 3.4.4) for which the factors of mm-CIMT, age,

gender and BMI were all strongly significant suggesting that it might be possible to estimate the

VWV from the mm-CIMT.

This thesis suggests that 23% of diabetic patients with an IMT>1.2mm possess IPH regardless of

duration of disease, symptomatology or degree of carotid artery occlusion. This feature is

indicative of advanced atherosclerotic disease that is highly correlated with future events.

Whether this actually translates into higher risks of cerebrovascular events is unknown. We can

conclude, however, that these patients would benefit from closer clinical follow up to monitor the

effects of IPH, such as increased vessel wall volume which may eventually result in carotid artery

stenosis. By extension, this result advocates for future studies aimed at improving the clinical

guidelines for diabetic patient carotid artery assessment.

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4.4 Novelty of work

This thesis used a novel approach to assess IPH of the carotid artery by developing a semi-

automated IPH detection method and applying it to a high risk patient demographic with non-

stenotic carotid artery atherosclerotic disease. The choice of a population without carotid artery

stenosis helps bridge a gap in the literature regarding the early stages of vessel wall disease and

IPH. The use of volume measurements to compare the size of a specific segment of carotid artery

wall between patients provided a new technique for monitoring vessel wall pathology.

4.4.1 Semi-automated method for identification of IPH

The need for an intensity ratio to identify and measure IPH was based on previous reports of IPH

detection by expert readers. A ratio of 1.5 is commonly used but there is little evidence for this

precise value. This ratio was originally used to identify IPH when expert readers were unsure of

the presence/absence of IPH. Reading by expert readers is of course, subject to human error. The

semi-automated method that was developed compared a range of intensity ratios against expert

readers to find the most accurate ratio for identifying high signal disease. The increased

automation and objective intensity comparisons outlined in this method, therefore reduced the

human-related error, improving quantification and allowing identification of small IPH volumes

(>1mm3) that fulfilled the defined vessel wall:SCM ratio of 1.5.

To test the accuracy of the semi-automated technique, IPH area measurements derived from the

semi-automated method were compared with an expert reader and this confirmed the high

accuracy of the 1.5 intensity ratio, with an ICC of 0.99. These results showed that the semi-

automated method could achieve a level of accuracy similar to an expert reader, for plaque area

and volume measurements.

Semi-automated methods have been developed for carotid artery image assessment, as

demonstrated by van’t Klooster et al. (2012), Saba et al. (2013) and Yoneyama et al. (2016) for

2D sequence analysis but not 3D sequences, but have not been employed in large clinical studies.

These results therefore, may encourage use of a semi-automated technique for 3D MRI sequence

analysis in subsequent studies, improving IPH identification and quantification between sites and

studies.

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4.4.2 Determination of the Carotid Artery Segment

Carotid wall volume measurements and comparisons have been undertaken in previous studies,

such as Vargese et al. (2009) and Strang et al. (2015), but there were variations of the carotid

artery segment that was measured. For VWV measurement and IPH identification in this study,

the segment of carotid artery was defined based on 3 prior sources:

1. The prevalence of type VI lesions described by Saam et al. (2008) was highest around the

carotid artery bifurcation, specifically at the carotid bulb. Therefore, a 32mm segment centered at

the bifurcation would allow for capture of IPH, as a type VI lesion, while also detecting any

impact that atherosclerosis might have on the carotid artery wall in this region.

2. Ultrasound IMT measurements described within the Mannheim consensus (2004-2006-

2011), define that measurements should be taken within a region free of atherosclerotic plaque at

the CCA, carotid bulb or ICA. However, the presence of plaque conveys a higher risk, overriding

the predictive value of IMT. These criteria helped define the carotid artery segment that was

quantified by measuring the region around the bifurcation, including the CCA, bulb and ICA.

3. Stary et al. described atherosclerotic plaque formation based on the presence of eccentric

intimal thickening and turbulent flow, which occurs more often at arterial origins and branches.

This led to the focus on the carotid artery bifurcation with extension proximally and distally.

4.4.3 Carotid Artery Wall Volume Associations

Male sex, increasing age, BMI and IPH presence were found to be positively associated with

increased vessel wall volume of the defined carotid artery segment. This result has not been

previously reported and demonstrates that demographic factors such as sex, age and BMI can

influence the size of the arterial wall in diabetic patients. In a study by Strang et al. (2015) on type

2 diabetic patients, they found that BMI could be used to predict which patients would have a

favourable cardiovascular treatment response. A higher baseline BMI measurement was found to

predict the response to treatment, which was demonstrated by regression of the carotid artery wall

volume. The use of the carotid wall volume and knowledge of the factors that influence VWV

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could therefore prove useful for guiding treatment of cardiovascular and cerebrovascular disease

in diabetes.

4.4.4 Investigation of a Unique Cohort

There are few existing studies on the presence of IPH in patients with no measurable carotid

artery stenosis, and no studies investigating IPH in an asymptomatic diabetic cohort. This unique

dataset comes from a dietary clinical trial where carotid artery MR imaging is being used to

measure initial carotid artery atherosclerotic disease as well as its progression in asymptomatic

diabetic patients. The IPH prevalence is probably higher than expected for a normal population, as

the cohort of patients used was diabetic – a population known to develop atherosclerotic disease at

an earlier stage than non-diabetic individuals. There was a 23% prevalence of IPH, a plaque

feature known to predict future cerebrovascular events, in a cohort which has a higher risk of

cerebrovascular event occurrence. This raises the possibility that IPH may represent an underlying

mechanism that contributes to cerebrovascular events in this population. However, further studies

are required to investigate this before conclusions can be made. In particular, future longitudinal

studies are needed to determine whether patients with IPH at early stages eventually develop

cardiovascular and cerebrovascular events.

4.4.5 3D and 2D MRI Sequences

The 3D MRI measured VWV had strong agreement with the 2D MRI VWV, showing that both

modalities obtain similar measurements for the same segment of carotid artery. This similarity is

relevant because 2D MRI has been more commonly used for carotid artery imaging. However, an

advantage of 3D MRI was shown by the position of the carotid bifurcation when imaging the

defined carotid artery segment. With 2D MRI, there was considerable variability in the position of

the carotid bifurcation for both the index and non-index carotid arteries, meaning that the same

segment of artery could not be measured bilaterally in the same patient. Therefore, if different

segments of carotid artery are obtained, the dimensions measured will not be comparable between

patients. The carotid artery segment measured by 3D MRI, however, was obtained following

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acquisition which included a large field of view, resulting in consistent vessel segment post

processing and allowed for comparisons to be made between carotid arteries for all patients.

4.4.6 Carotid Artery Wall Volume and IMT

No previous studies have compared the value of IMT for the estimation of carotid artery wall

volume. The comparison between VWV and mm-CIMT showed a moderate correlation and

suggested some predictive value for the volume of the carotid artery wall from the IMT

measurement. Patients with high mm-CIMT were also shown to have an increased prevalence of

IPH. The mm-CIMT measurement, therefore, may be able to predict patients with an abnormal

VWV on MRI and identify those who could benefit from having a carotid MRI for VWV

measurement and detection of IPH presence.

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4.5 Limitations

The limitations for each study described in this thesis were included in the study chapters, but in

this section they will be explained further.

4.5.1 Study 1 – Measurement of MRI detected IPH

4.5.1.1 Gold standard measure of IPH

For the semi-automated method of IPH measurement, defining the optimal threshold required the

use of a gold standard comparator for assessing its diagnostic capabilities. The consensus

agreement of expert reviewers was used as the gold standard and the aim was to develop a semi-

automated technique that would perform at the same or similar level of an expert reader. The

limitation of this approach, however, was that histological verification was not performed.

Histological verification is the accepted gold standard when developing imaging techniques as it

provides a definitive result, but still has some shortcomings. In this study for example,

histological comparison was impractical as, by current clinical guidelines, the patients were not

candidates for carotid endarterectomy and therefore histology would never be available from

clinical surgical specimens. A possible option would be to use cadaveric specimens comparing

incidental findings of small volumes of carotid artery IPH disease with ex vivo, and possibly in

situ, MR imaging of the vessel. There are also well described problems encountered when

histology specimens are compared with imaging, particularly where spatial orientation and

measurement comparisons are being performed. The 3D spatial orientation changes on removal of

a specimen, which presents a problem with obtaining corresponding slices for microscopic

viewing.

Histology slices are in the order of 10µm thick, so comparison with an image slice of 2mm

becomes problematic and potentially several slices need to be combined in some way to compare

a specimen region. Visual matching of features is required in these cases, presenting further

potential for error. Visually matching histology slices with the corresponding axial MR image

may also be extremely challenging considering the small volumes of IPH measured in this study.

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Compounding this issue is the shrinkage of endarterectomy specimens on removal from the

carotid artery.

4.5.1.2 Single sequence acquisition

The semi-automated method that was developed for IPH detection and measurement analyzed

only a single 3D T1w GRE sequence. It would be specific for this sequence and would not be

applicable to other sequence acquisitions, such as 2D MRI or spin echo acquisitions, for example.

The 3D T1w GRE sequence would, however, be a useful addition to carotid artery imaging

protocols for IPH identification, because it eliminates the need for performing an intensity

comparison among multiple sequences, as is done with 2D sequences.

Measurement comparisons between carotid wall volume on the 2D and 3D MR-image sequences

proved to have strong agreement for VWV, but a limitation of the 3D sequences used is their

inability to identify atherosclerotic plaque components other than IPH. However, there may be the

potential to characterize lipid and calcium that are visible as hypointense areas on the 3D T1w

GRE sequence following contrast administration, but further study is necessary. Such a study

could involve comparisons with 2D MRI component detection, as well as with histology.

4.5.2 Study 2 – IPH Prevalence and Associations with VWV in a Diabetic Cohort

4.5.2.1 Specific patient population

The results of this study are specific for asymptomatic diabetic patients with a carotid IMT

>1.2mm and no carotid stenosis and therefore cannot be expected to apply for all diabetic patients.

However, the results are a useful starting point in the investigation of the pathobiology of vessel

wall disease and the factors that potentially lead to accelerated plaque and symptom development

in this group. The absence of a control group makes it difficult to put the prevalence of IPH and

its associations into context. It would be useful to compare the characteristics and results of this

study with an age and sex matched healthy cohort, also without carotid artery stenosis, to observe

whether the prevalence obtained in this diabetic cohort was higher. The factors that were found to

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affect VWV in the diabetic cohort could also be compared, to determine whether they exerted a

similar influence on the VWV of non-diabetic individuals.

4.5.2.2 Sample size

The sample size for this study may have been too small to discover significant associations

between the presence of IPH and other cardiovascular risk factors. Finding the prevalence of IPH

within the cohort was one of the aims of this study, so it was difficult to anticipate the number of

positive IPH cases that were found. In addition, these patients were recruited as part of a

randomized controlled trial, so the sample size could not be adjusted. As a post-hoc power

calculation after obtaining a prevalence of 23%, a suitable sample size to estimate the prevalence

of IPH that would be applicable to the diabetic population is 273 (=0.05, precision=0.05).

However, at the beginning of the study, if an assumption was made from previous studies that the

actual number of IPH positive patients was closer to 10%, the sample size required would be 139

patients (=0.05, precision=0.05)108

.

Only VWV was found to have a significant association with IPH, while age, sex, BMI and IPH

were found to have a statistically significant effect on the measured VWV. However, factors that

did not have a significant effect on either IPH presence or VWV measurement may have been

subject to type II error because of the small sample size and the number of variables included in

the model. Even so, useful associations were discovered from the available data which have

potential implications for imaging and assessment of diabetic patients.

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4.6 Conclusions

The prevalence of magnetic resonance imaging detected intraplaque hemorrhage within carotid

arteries was 23% in a cohort of asymptomatic type 2 diabetic patients without carotid artery

stenosis. The presence of IPH within carotid arteries was associated with larger carotid artery wall

volumes compared with arteries where IPH was absent.

IPH can be identified as high signal on a 3D-T1w GRE MRI sequence above a set threshold,

using a semi-automated image processing protocol at a sensitivity of 80% and specificity of

100%. The threshold was defined using the ratio of 1.5 times the intensity of the

sternocleidomastoid muscle. The SCM intensity was obtained from a 20mm2 region of interest

adjacent to the carotid artery wall at the level of the bifurcation. The smallest area of high signal

detected was 0.1mm2.

In a cohort of 159 asymptomatic diabetic patients without carotid artery stenosis, 37 were found

to have IPH within a 32mm segment of carotid artery centered at the bifurcation. 32 patients had

IPH in a single carotid artery while 5 patients had the feature bilaterally. IPH presence was

significantly associated with a larger volume of the carotid wall segment, which was not attributed

to the volume of measured IPH. IPH was not significantly associated with sex, age, smoking

status or diastolic blood pressure in this cohort.

The volume of the carotid artery wall was positively influenced by increasing age, male sex,

higher BMI and IPH presence. Duration of diabetes, hypertension, dyslipidemia and smoking

status did not significantly affect the VWV. There was moderate correlation (r=0.36) noted

between the VWV and mm-CIMT measurements.

Detection of IPH within a population susceptible to atherosclerotic disease and cerebrovascular

events may provide significant information even in the absence of conventional measures of

vessel wall disease such as stenosis. IPH may represent a predictive biomarker of higher risk

individuals in this already at risk population, knowing that it is predictive of future

cerebrovascular events in patients with carotid artery stenosis.

These results suggest that greater emphasis should be placed on earlier investigation of diabetic

patients, as IPH presence may represent a more useful guide for risk stratification than measuring

104

the degree of stenosis or waiting for onset of symptoms. However, further clinical studies are

needed to determine the value of MRI identification of IPH in the management of carotid artery

disease in diabetic patients before any modifications can be made to the clinical guidelines.

105

Chapter 5

Future Directions

106

Future Directions 5

5.1.1 3D MRI Sequence Use

3D MRI sequences were able to capture the same segment of the carotid artery for image analysis

between patients compared with 2D sequences shown by the position of the bifurcation (Table

18). The ability to capture a consistent segment would benefit clinical trials where comparison of

a common carotid artery segment needs to be made across patients, or where serial measurements

of the same segment are needed. The volume measurements from 3D sequences correlated

strongly with 2D sequence measurements, showing that 3D MRI measures the same carotid wall

volumes as 2D MRI. Further development of 3D sequences could be undertaken to identify and

quantify other atherosclerotic plaque components such as lipid and calcium. This added capability

of 3D sequences would make it a complete evaluative tool for assessment of the carotid artery. A

future study comparing the 3D T1w GRE, contrast enhanced 3D T1w GRE and 3D TOF

sequences with the corresponding segment on 2D MRI sequences and histology could assist with

improving the capabilities of 3D MRI sequences for atherosclerotic plaque characterization and

quantification.

5.1.2 Fully Automated Image Processing Protocol

The semi-automated protocol can be modified to create a fully automated image processing

method. This might be achieved with better structure recognition software or edge detection

software coupled with a standard carotid artery image analysis protocol. Development of an

automated image analysis method would greatly improve image processing in clinical or research

trials, reducing the need for expert readers, and increasing the consistency and accuracy of

measurements.

5.1.3 Follow Up of the Patient Cohort

Further follow up of the recruited patients in this cohort will provide some insight into the effect

of IPH on the vessel wall. The diabetic patients in this study were scheduled to receive MR

imaging 1 year after the baseline scan reported in this work and again after 3 years from

107

enrollment in the trial. Data from this trial aim to show the impact of a dietary intervention on

vessel wall disease. However, data from these time-points will also be very useful for observing

the effects of IPH. In addition, a long-term follow-up, such as repeat imaging after 10 years,

might provide more definitive data for these patients. Of specific interest would be the patients

with IPH identified in this study with particular note made of their subsequent symptomatology

and changes in the carotid artery wall volume.

5.1.4 Comparison with a Non-Diabetic Population

This study provided results that contribute to the literature regarding IPH and non-stenotic carotid

artery disease. These results include a baseline for IPH presence in a diabetic cohort of patients

without carotid artery stenosis and could represent a reference point of IPH presence in a specific

population for further studies to be compared with. An MR-imaging study of normal individuals

to evaluate IPH presence and carotid wall volume measurements with age and sex matched

controls would shed light on the increased effect on vessel wall disease conferred by diabetes.

Such a study would determine whether the associations found in this thesis between VWV and

age, sex, BMI and IPH, were unique to the diabetic cohort, or are applicable to a wider

population.

5.1.5 Development of Carotid Artery Wall Volume Reference Value

The carotid artery wall is expected to increase in size with pathological processes such as with

atherosclerotic plaque deposition. Therefore, there is a limit to the wall volume considered to be

within a normal range. The demographic factors (such as male sex, age, BMI) known to influence

carotid artery vessel wall could be taken into account when defining a value for expected carotid

wall volume. Volumes outside the baseline would indicate disease. In addition, as age and male

sex influence the volume of the carotid wall, there could be distinct normal ranges accounting for

age groups, as well as between sexes. This would create a trend towards personalized medicine

for evaluation of carotid artery disease, where risk factors would be specific to an individual

based on personal characteristics.

108

5.1.6 Carotid IMT as a Screening Tool for Carotid MRI

IMT measurement with B-mode ultrasound is widely available and could potentially assist with

identification of patients who might benefit from more intensive imaging using MRI.

Demonstration of IPH associated with increased VWV was duplicated using mm-CIMT even

though the direct correlation between mm-CIMT and VWV measurements was moderate.

Therefore, carotid artery IMT measurements could precede MRI to identify patients who would

benefit from further atherosclerotic plaque characterization. This paradigm, however, would need

further study and evaluation before it could be effectively applied to imaging guidelines for

carotid artery disease.

109

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Appendices

Category Description

5 Wall boundaries of all slices clearly visible

4 Wall boundaries of 1-2 slices unclear

3 Wall boundaries of 3-6 slices unclear AND less than 3 consecutive slices unclear.

2 Wall boundaries of >6 slices unclear OR >3 consecutive slices unclear.

1 All wall boundaries unclear.

Appendix 1. Image quality assessment scale. The image quality of the 3D-MRIPH and 3D-TOF sequences per

carotid artery was graded using the above 5 point scale. Sequences rated >2 were used for analyses. Wall boundaries

refer to the outer wall on the 3D-MRIPH sequence and the lumen boundaries on the 3D-TOF.

Variable Volume ICC 95% CI

Sig. Lower Upper

Lumen, (mm3) 0.86 0.02 0.96 <0.01

Outer Wall, (mm3) 0.95 0.75 0.98 <0.01

Vessel Wall, (mm3) 0.87 0.78 0.93 <0.01

Appendix 2. Intraclass correlation coefficients for 2D-3D volume comparisons.

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