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President’s Symposium
The Future of Physics Research in Cancer Therapy
and Imaging
The Role of Informatics in Medical Physics and Vice Versa
The Role of Informatics in Medical Physics and Vice Versa
Katherine P. Andriole
Brigham & Women’’’’s HospitalDepartment of Radiology
Center for Evidence-Based ImagingHarvard Medical School
Boston, MA
Katherine P. Andriole
Brigham & Women’’’’s HospitalDepartment of Radiology
Center for Evidence-Based ImagingHarvard Medical School
Boston, MA
Page 2
Like Medical Physics
Imaging Informatics encompasses
concepts touching every aspect of the
imaging chain from image creation,
acquisition, management, and archival,
to image processing, analysis, display
and interpretation.
Medical Physicists and Imaging Informaticists
Are concerned with 3 areas of activity:
–Clinical Service and consultation,
–Research and Development, and
–Teaching.
Complimentary Disciplines
With specific goals to:
–Improve quality of care provided to
patients using an evidence-based
approach
–Assure safety in the clinical and
research environments
–Facilitate efficiency in the workplace
–Accelerate knowledge discovery.
Page 3
Outline
• What is Imaging Informatics?
• Case Scenarios in which Imaging
Informatics & Medical Physics impact
Safety, Quality, Efficiency, Discovery
— Promise & Potential
— Current Barriers
— How Informatics Might Help
Case Scenarios: Informatics & Physics
• Radiation Exposure (Safety)
• Exam Protocoling (Quality)
• Signature Times (Efficiency)
• Quantitative Imaging Data Warehouse
(Knowledge Discovery)
• Informatics Concepts: BA, NLP, Data Mining,
Standards, Integration, Cloud, Context
Sensitivity, Ontologies, OCR, Decision Support
What is Imaging Informatics?
• Information Science
–the collection, classification,
storage, retrieval, and
dissemination of recorded
knowledge treated both as a
pure and as an applied science.Merriam-Webster Dictionary
Page 4
Biomedical Informatics
• Interdisciplinary science that deals with
biomedical information, its structure,
acquisition and use.
• Includes research, education and service in
health-related basic sciences, clinical
disciplines and heath care administration.
Vanderbilt University Department of Biomedical Informatics
Biomedical Informatics• Is grounded in the principles of
computer science, information science,
cognitive science, social science, and
engineering, as well as the clinical and
basic biomedical sciences.
Vanderbilt University Department of Biomedical Informatics
• For Imaging Informatics – include
Medical Physics
Other Definitions
• Biomedical Informatics… is the interdisciplinary, scientific field that studies and pursues the effective uses of biomedical data, information and knowledge for scientific inquiry, problem solving and decision making motivated by efforts to improve human health.
AMIA evolving definition August 6, 2010 https://www.amia.org/files/shared/e_Competencies_-_Definition_and_Competencies.pdf.
Page 5
INFORMATICSINFORMATICS
, Information
is delivered ,
, and to .
, Information
is delivered ,
, and to .
Informatics
• HOW
• WHAT
• WHERE
• WHEN
• WHOM
� Image, Graph, Value, Sound
�Relevant Prior Imaging Exam
�Radiology RR, ICU, Pager, PDA at
the point of care
� Immediately upon Request, Only
when Abnormal Result
�Context-Sensitive for the End-User
Major Informatics Functions
• Knowledge Representation
• Information Extraction & Structuring
• Information Distribution
• Information Architectures
• Information Retrieval
• Communication of Knowledge &
Information
Page 6
Bedside
HEALTH SERVICES
RESEARCH
Policy
OutcomesCost-Effectiveness
IMAGINGINFORMATICS
INFORMATION
ANALYSIS &
PRESENTATIONInformatics
ComputationStatistics
HEALTH
Genetics
Structural BiologyNeuroscience
BIOLOGICAL SCIENCESBench
Translation
BIOMEDICAL
INFORMATICS
• Bioinformatics: molecular & cellular processes
• Imaging Informatics: tissues & organ systems
• Clinical Informatics: individuals & patients
• Public Health Informatics: populations & society
Biomedical Informatics Four Major Areas of Application
Shortliffe and Cimino. Biomedical Informatics: Computer Applications in Health Care and
Biomedicine 3rd Edition. Springer 2006.
Different Scales
Spectrum of Research Topics• Methodology – Basic Informatics Research
– Content-Based Image Retrieval, Image
Processing, Decision Support, Evidence-Based
Imaging
• Applied Informatics Research
– New GUI, Data Visualization Techniques, CAD
• Design – Development
– New Standard, Benchmark, Technical Guideline
• Engineering Evaluation
• Clinical Evaluation
– Outcomes, Workflow Management
Page 7
Imaging
Informatics
Image Creation Image Display
Image Management
Technology
Assessment
-Modalities
*Digital Radiography
-Spiral CT
-Multimodality (CT/PET)
- Molecular Imaging
- Image Quality
*New Display Paradigm
*Processing/Analysis
-*GUI Design
-*Visual Perception
- 3-D Visualization
-Image-Guided Surgery
-CAD
- Outcomes Research
-Cost-Effectiveness Studies
Errors, Research & Education*Mining / Evidence-Based Medicine
-Decision Support / Expert Assist
*Teaching Files / MIRC / NLP
*Filmless & Paperless
*Database Integration / IHE
*Point-of-Care Delivery /Wireless
-ArchitecturesQuality-Safety*Decision Support
*IT Interventions
*Best Practices
*Meaningful Use
Special Issues
• Multidisciplinary Teams
–Need for Collaborative Culture
–Requires Clinical Acumen
• Translational Research
• Requires Technology Infrastructure
–Developmental Costs
• Need to Research & Test in Clinical Arena
– Implementation, Validation & Impact
• Unique Education & Training
Imaging Informatics• Touches every aspect of the imaging
chain from
– Image Creation & Acquisition
– Image Distribution & Management
– Image Storage & Retrieval
– Image Processing, Analysis &
Understanding to
– Image Visualization & Data
Navigation.
Page 8
PACS
Database Server
RIS
HIS
ModalityArchive
HL7
DICOM
DICOM
NetworkGateway
Verify
Prefetch
Autoroute
Send
DICOMStore
Q/R
Cached DS
Cachless DS
Query-on-
Demand
IS Gateway
Case Scenarios: Radiation Exposure1
• CT Dose Index Metrics
— Automate Extraction
— Assign Anatomical Region
— Examine Protocol Variation
— Estimate Patient Size
• Nuclear Medicine Imaging1Sodickson, Warden, Ikuta, Prevedello, Wasser, Andriole, Gerbaudo, Khorasani
Sodickson A et al. Radiology 2012;264:397-405Ikuta I et al. Radiology 2012;264:406-413
Case Scenarios: Radiation Exposure
• Safety
• Informatics Concepts
— Data Mining
— Business Analytics
— Natural Language Processing
— DICOM Standards CT RDSR (Rad Dose SR) since 2007
— Optical Character Recognition
— Tools have been made Open Source
Page 9
Case Scenarios: Radiation Exposure• Developed an informatics toolkit that
automatically extracts anatomy-specific
CT radiation exposure metrics from
existing enterprise image archive
― CTDIvol – volume CT Dose Index
― DLP – Dose-Length Product
― Optical Character Recognition on Dose
Report Screen Captures*
― Table start and stop positions
― DICOM Attributes eg, protocol/series name*Clunie D. PixelMed DICOM Toolkit
Case Scenarios: Radiation Exposure
• CT Examinations in BWH enterprise
archive from 2000 – 2010
— Cohort of 54,549 CT encounters
— 29,948 had Dose Screens
• Algorithm Validation: 150 randomly
selected encounters for each major CT
scanner manufacturer
• 99% Dose Screen Retrieval Rate 95% CI
Dose Screens
Contents and formatting differ; CTDIvol, DLP per dose
event of an encounter; Series or Scan Descriptions.
Page 10
Contents and formatting differ; CTDIvol, DLP per dose
event of an encounter; Series or Scan Descriptions.
Dose Screens
Dose Screens
This manufacturer stores dose report content in private DICOM attributes.
Case Scenarios: Radiation Exposure
• Automatically assign Anatomic Region
— Using DICOM Attributes
— Protocol Anatomy
— Anatomy Map Definition
— Number of Acquisitions
— Table Position
• 94% Anatomic Assignment Precision 95% CI
Page 11
Anatomic Assignment
Protocol Anatomy Maps
• Chest/Abdomen/Pelvis
• Head/Neck
• Neck/CAP
• Neck/Chest
• Abdomen/Pelvis
• Chest/Abdomen
Anatomy Maps
Chest, Abdomen, and Pelvis
Page 12
Chest, Abdomen, and Pelvis 33% each
33%
Chest, Abdomen, and Pelvis33% each
33%
33%
Chest, Abdomen, and Pelvis33% each
Page 13
Head and Neck
Head and Neck40% / 75%
40%
75%
Head and Neck40% / 75%
Page 14
Business Analytics
Identify variation within our imaging centers,
standardize protocols and optimize dose
Body Habitus• Estimate Patient Size from axial CTs
• Model patient as a cylinder of water
• Image attenuation converted to equivalent
water diameter
• Limitations: all image attenuation
attributed to the patient; assumes whole
cross-section of patient is included on
image (problematic for large patients for
whom FOV is truncated).
Body Habitus Calculations
Ikuta, Andriole, Sodickson, Warden. To be published.
Page 15
HU HU HU
HU HU HU HU HU
HU
HU HU HU HU
HU
HU
Patient Axial CT Image
Effective Diameter (Deff)
Cylinder of Water
Water-Equivalent Diameter (DW)
X
Y
DW
AP
Lateral
Deff = AP * LateralHU
i+ 1000 * X * Y * 4
1000 πDW = ∑
n = 262,144
i = 1
0 10 20 30 40 50 600
10
20
30
40
50
60
Water Phantom Linear Regression Model
AAPM Report 204 Manual Method Deff (cm)
GR
OK
Auto
mat
ed M
etho
d D
W(c
m) y = 0.926x + 3.60
p < 1 x 10-15
R2 = 1.00
n = 430
Range = 5.8 – 50.3cm
Upper 95%CI
y = 0.929x + 3.69
Lower 95% CI
y = 0.923x + 3.51
DW and Deff were assessed for the same CT slice.
Body Habitus Calculations
Page 16
y = 0.660x + 9.79
p < 1 x 10-15
R2 = 0.51
n = 200
y = 0.760x + 8.47
p < 1 x 10-15
R2 = 0.90
n = 150
AAPM204 Manual Deff (cm) AAPM204 Manual Deff (cm)
GR
OK
Auto
mate
d D
W(c
m)
GR
OK
Auto
mate
d D
W(c
m)
CT Thorax CT Abdomen/Pelvis
0 10 20 30 400
10
20
30
40
0 10 20 30 400
10
20
30
40
Case Scenarios: Radiation Exposure
• All Nuclear Medicine reports in BWH
enterprise archive 1985 - 2011
— Cohort of 204,561 NM reports mined
in 11 minutes using Natural Language
Processing tool written in Perl
• 97.6% Recall Rate 95% CI (Sensitivity)
• 98.7% Precision (Positive Predictive Value)
Data Fields Parsed from NM Reports• Example: 12 mCi F-18 FDG
–Unit of Radioactivity: mCi
–Quantity Administered: 12
–Radiopharmaceutical: F-18 FDG
• Conversion factors are specific to the radiopharmaceutical administered
– Based on biodistribution, pharmacokinetics, radioactive decay
–Weighting factors depend on organ-
specific radiation sensitivity
Page 17
1. Single radiopharmaceutical, single administration
Tc-99m MDP bone scan
2. Single radiopharmaceutical, multiple administrations
Tc-99m sestamibi rest and stress cardiac exam
3. Multiple radiopharmaceuticals, multiple administrations
Xe-133 gas/Tc-99m MAA V/Q scan
Administration Scenarios
HISTORY : 23 y/o with central T6/T7 disk protrusion,
r/o facet disease
RADIOTRACER : 27 mCi Tc-99m MDP
Study/Images : Planar whole body imaging and thoracic
SPECT
INTERPRETATION : BONE SCAN 2/19/02
Planar whole body images are within normal limits.
Normal tracer uptake is seen on SPECT images of the
lumbar spine, with no evidence of facet arthropathy.
Scenario 1
Dear Dr. Xavier,
Your patient, Mr. Jones, is a 48 year old male with
known CAD and prior PCI was referred to us for an
exercise myocardial perfusion SPECT study….
….
Stress Protocol (One-day study): Your patient
exercised for 13:03 minutes of a Bruce protocol
(15.3 METS). The patient was injected with 11 mCi
and 33 mCi of Tc-99m Sestamibi at rest and during
peak stress, respectively….
Scenario 2
Page 18
HISTORY: Pre-operative assessment of lung
ventilation and perfusion. History of squamous cell
carcinoma the left lower lobe.
RADIOTRACERS: Xe-133 gas (mCi) : 15
Tc-99m MAA (mCi) : 4.5
Study/Images: Ventilation projection - Posterior.
Perfusion -
Six standard planar lung views.
INTERPRETATION: QUANTITATIVE
VENTILATION-PERFUSION LUNG SCAN 14
September, 2007….
Scenario 3
Toolkit Mechanics for a NM Cardiac Stress Test
Ikuta I et al. Radiology 2012;264:406-413
©2012 by Radiological Society of North America
Dose Metrics Over Time
Ikuta I et al. Radiology 2012;264:406-413
©2012 by Radiological Society of North America
• April 2012 implemented more sensitive imaging detector;
transition decreased patient dose.
• Above 25 mCi highlighted by algorithm prompted manual
inspection and were found to be dose reporting errors.
Page 19
Patient-Specific Exam Timeline and Cumulative Organ Dose Heatmap
Based on knowledge of radiopharmaceutical biodistribution, pharmacokinetics,
radioactive decay – can assign cumulative organ dose.
• Quality
• Informatics Concepts
— IT Integration
— Reminders / Alerts / Decision Support
— Business Analytics
— Context Sensitivity
— Web Services
— GUIs / FUIs
Case Scenarios: Exam Protocoling
“Clinical Decision Support systems link
health observations with health knowledge
to influence health choices by clinicians for
improved health care.”
Dr. Robert Hayward, Centre for Health Evidence
Clinical Decision Support (CDS)
Page 20
• Knowledge-Based CDS
–Consists of knowledge base, inference
engine, output communication
–Knowledge base with rules and
associations (eg, IF-Then rules)
• Non-Knowledge-Based CDS
–Use Artificial Intelligence (eg, machine
learning, neural networks)
• Watson uses both
Clinical Decision Support (CDS)
• Historically, the healthcare provider entered the
patient data and the CDS system output the
“right” decision, that the provider would simply
act upon.
• Today, the provider interacts with the CDS
utilizing both the clinician’s knowledge and the
CDS suggestions; the provider decides what
information is useful, erroneous, etc., and makes
the final management decision.
CDS Systems
• Interactive decision support designed to
assist healthcare professionals with
decision making tasks.
• Uses patient data to generate case-
specific advice.
• Presented at the point-of-care
Current CDS Systems
Page 21
• Integrated into the Clinical Workflow
• Fast and Efficient
• Intuitive, Ease-to-Use GUIs
• Context-Sensitive
• Based on Evidence; Dynamically Updated
Components of Successful Implementations
Healthcare Enterprise Information Management System
DICOM
PatientHospital
Registration
Schedule
Exam
GatewayHL7 HL7
DICOM
Worklist
DICOM
HIS RIS
WorkstationReporting
System
DataBaseDemographicsMRN
Location
HL7
ResultsReporting
HL7
MRNAccNum
ExamMNE
ReportHL7 DICOM
PACS
Event Event
SQL
SQL
Archive
ModalityOrder
Exam
• Examination Ordering – Appropriateness
• Image Acquisition – Optimal Protocol
• Diagnostic Interpretation – Increase Conspicuity
– Processing, Analysis, Understanding
– Visualization of Representative Comparative Cases
• Reporting, Communication, Follow-up
Recommendations – Reminders and Alerts
Medical Imaging Chain
Page 22
CT LiverUS Abdomen RUQ MRI Liver
Age Gender
Screening HCC
As ian men
>40yo
As ian
women
>50yo
African
>20yo
Ultrasound and AFP 2x/year
No Surveillance
Recommendation: According to the AASLD*
guidelines Ultrasound and Alpha-fetoprotein (AFP) are recommended for screening patients with liver
cirrhosis due to hepatitis B and C.
Recommendation: According to the AASLD*
guidelines MRI is not recommended to screen patients for Hepatocellular carcinoma. Please
consider Ultrasound +AFP each 6 months.
Recommendation: According to the AASLD*
guidelines CT scan is not recommended to screen patients for Hepatocellular carcinoma. Please
consider Ultrasound + AFP each 6 months. Hepatitis B Hepatitis CYes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
From database
* American Association for the Study of Liver Diseases
Does the patient have LIVER
CIRRHOSIS?
Nodule 1-2cmNodule <1cm Nodule >2cm
Recommendation: According to the
AASLD* guidelines it is recommended to REPEAT US AT 3-4 MONTHS
INTERVALS.
Recommendation: According to the
AASLD* guidelines it is recommended to perform TWO OF THE FOLLOWING
DYNAMIC STUDIES: CT SCAN, MRI OR
CONTRAST US.
Recommendation: According to the
AASLD* guidelines it is recommended to perform ONE OF THE FOLLOWING
DYNAMIC STUDIES: CT SCAN, MRI OR
CONTRAST US.
Does the patient have LIVER
FIBROSIS GRADE iii OR IV?
Does the patient have ACTIVE
DISEASE?
Does the patient have FAMILY
HISTORY OF HCC?
PRIOR IMAGING STUDY WITH LIVER NODULE
Please select patient RACE:
AsianAfrican Other
Enlarging
Stable 18-24 m
US 2x/year
No
Yes
Treat as HCC Biopsy
Typica l vascular pattern
in one technique or
Atypica l in two
Atypica l vascular
pattern
Typica l vascular pattern
or AFP>200ng/ml
Coincidental Typical
vascular pattern
Courtesy of Cleo Maehara, MD, MSc
Formerly Brigham and Women’s Hospital
Medical Imaging ProcessImage quality is affected by the 5 major components of the medical
imaging process: the Patient, the Imaging System, the System
Operator, the Image itself, and the Observer.
� Set of instructions (recipe) for performing
an imaging examination
—Slice Thickness/ Spacing
—IV Contrast Volume / Type / Rate
—Oral Contrast Volume / Type
—3D, Axial, Coronal, Sagittal
—Modality (CT or MRI)
Optimal Imaging Protocol
Page 23
Protocolling process
4. Prior
imaging
2. Clinical history
Lab tests
3. Order
Contrast
1. Patient
worklist
Courtesy of Cleo Maehara, MD, MSc
Brigham and Women’s Hospital
Page 24
Medical Imaging ProcessImage quality is affected by the 5 major components of the medical
imaging process: the Patient, the Imaging System, the System
Operator, the Image itself, and the Observer.
Protocolling
Image
Processing
Interpretation
Reporting
Page 25
Page 26
• Efficiency
• Informatics Concepts
— Reminders / Alerts / Decision Support
— Speech Recognition
— Business Analytics and Key Performance Indicators
— Integration
Case Scenarios: Signature Time
Purpose
• Poor radiology report turn-around-time
(TAT) can adversely affect patient care –
quality, cost, efficiency
• The combination of technology adoption
with behavioral modification is evaluated to
determine if improvement in TAT can be
augmented and sustained.
Page 27
Report TAT Components
5:35 16:22 18:23
Median Times (hh:mm)
13.84% 40.59% 45.57%
C D P F
C: Completed Image
D: Dictated/Read
P: Preliminary Report
F: Finalized Report
Methods
• 3.5 year study period
• 3 interventions focused on radiologist
signature time (ST) performance
–Notification Paging Portal
–PACS-integrated SR
–Behavioral Modification (Departmental FI)
Methods
• Pre- and Post-Intervention Metrics
• Statistical Methodology: Wilcoxon /
Kruskal-Wallis Rank Sums Test and linear
regression analysis to assess significance
of trends
Page 28
Intervention Implement.
Dates
Metric
Period
Months
Sampled
PP October 2005 Control July-August-
September 2005
SR Rollout Begin
October 2005 –Complete
December 2006
Post-
Tech
December 2006-
January-February 2007
FI March 1, 2007 –
February 28, 2008
Post-All March-April-May
2007
Results
Signature Time Trend
0
5
10
15
20
25
30
Jun-0
5
Aug-0
5
Oct-05
Dec-0
5
Feb-0
6
Apr-
06
Jun-0
6
Aug-0
6
Oct-06
Dec-0
6
Feb-0
7
Apr-
07
Jun-0
7
Aug-0
7
Oct-07
Dec-0
7
Feb-0
8
Apr-
08
Jun-0
8
Aug-0
8
Oct-08
Hours
Median 80th Percentile
Speech Rollout Financial Incentive
Start Paging
Control PostTec
h
Post All
Page 29
Comparison
Periods
%
Reduction
for Median
p value
(Wilcoxon)
%
Reduction
for 80th
Percentile
p value
(Wilcoxon)
Control
versus
Post-Tech
85.5 <0.001 37.8 <0.001
Post-Tech
versus
Post-All
37.5 <0.05 81.3 <0.001
Control
versus
Post-All
90.0 <0.001 88.3 <0.001
Results
• Technology Adoption (PP & SR) reduced
– median ST from >5h to <1h (p<0.001)
– 80th percentile from >24h to 15-18h (p<0.001)
• Subsequent addition of FI further improved 80th
percentile to 4-8h (p<0.001)
• Gains in median and 80th percentile ST were
sustained over final 22 months of study period.
• A finalized radiology report is predominant means of
communicating radiologists’’’’ interpretative findings of
medical imaging exams to referring clinicians to inform and
affect patient care management decisions.
• Timely finalization of report improves quality of patient care;
standard by which radiology departments are assessed.
• Although ST was significantly reduced post-technology
interventions (PP & SR), behavioral (FI) was needed to further
improve and sustain the impact.
• A finalized radiology report is predominant means of
communicating radiologists’’’’ interpretative findings of
medical imaging exams to referring clinicians to inform and
affect patient care management decisions.
• Timely finalization of report improves quality of patient care;
standard by which radiology departments are assessed.
• Although ST was significantly reduced post-technology
interventions (PP & SR), behavioral (FI) was needed to further
improve and sustain the impact.
Page 30
What is Business Intelligence?• Technology, applications and practices for:
– Aggregation (Collect, Validate)
– Integration (Multiple Databases)
– Storage (Data Warehouse)
– Analysis (Data Mining)
– Presentation (Dashboards, Reports)
• For better decision making; Need input
from all constituents (med, finance, tech,
etc); what are departmental goals?
Data in a
unified and
consistent format
Business Analytics for Departmental Administration, Operations, Safety,
and Knowledge Discovery
• Combining imaging data and other
relevant non-imaging data to visualize
trends, detect gaps, draw correlations.
• Can be used for operational
performance metrics and reporting, as
well as clinical.
Page 31
Page 32
• Knowledge Discovery
• Informatics Concepts
— Data Mining
— Business Analytics
— Integration
— Cloud Computing / Data Sharing
— Standards
Case Scenarios: Quantitative Imaging Data Warehouse
Quantitative Imaging…
• Is the extraction of quantifiable features from medical
images for the assessment of normal or the severity,
degree of change, or status of a disease, injury, or
chronic condition relative to normal.
• Includes the development, standardization, and
optimization of anatomical, functional, and molecular
imaging acquisition protocols, data analyses, display
methods, and reporting structures.
Quantitative Imaging…
• These features (imaging acquisition protocols,
data analyses, display methods, and reporting
structures) permit the validation of accurately and
precisely obtained image-derived metrics with
anatomically and physiologically relevant
parameters, including treatment response and
outcome, and the use of such metrics in research
and patient care.
Page 33
Limits of Human Visual Perception
Where’s Waldo?What is the area of his face?
Limits of Human Visual Perception
• While the human visual system is very good
at pattern recognition, shape recognition and
edge detection, the human eye is limited at
making complex quantitative assessments.
• It takes time, effort and tools to make
quantitative measurements.
Qualitative versus Quantitative
• Thus useful quantitative information
contained in medical images is not routinely
included in imaging reports.
• What are the negative implications for
clinical care, research and development of
new treatments and drug development?
• What do referring clinicians what?
Page 34
• Is the tumor getting bigger or smaller, and
by how much?
• So they can decide if they should keep the patient on
their current treatment regimen or change it?
• Is that within the range of normal
physiologic activity? Is this within the
error of measurement?
Clinical Need to Know
Metric Requirements
• Accurate, precise, repeatable,
• Reliable, valid and achievable.
• Consistent results across imaging
platforms, clinical sites and time.
Challenges to Achieving QI
• Standard Image Acquisition Protocols
(eg, slice thickness)
• Uniformity across different algorithms
• Uniformity across different vendors
Page 35
Challenges to Achieving QI
• Applications and tools must be easy to
use and must be embedded into the
clinical workflow.
• Disparate health information systems (eg,
HIS, outcomes, genomics databases)
must be integrated so that the required
metadata can be used.
• As radiology is increasingly looks toward
quantitative imaging to provide evidence-based
measures for the detection, diagnosis and
treatment of disease,
• Development, validation & implementation of QI
biomarkers depend on the quality, size,
diversity, discoverability of, and accessibility to
imaging databases.
Need for Imaging Data Warehouse
• Open, growing, lasting data warehouse
with images and relevant metadata
including clinical outcomes, genomics
• That researchers, pharma, industry, NIH
awardees could submit to and retrieve
from (e.g., Craig’s List); and contribute
algorithms, metrics, etc.
• To accelerate development & scientific
acceptance of QI methods.
QIBA-RIC Collaborative Vision
Page 36
Data & Results Sharing
• Open Source Tools
• Test Datasets
– Common Acquisition Protocols
– Reduce Proprietary Formats, Processing
• Database Sharing between Academia,
Healthcare Enterprises & Industry
• QIBA Technical Committee Working Groups:
DCE-MRI, FDG-PET, fMRI, Volumetric CT,
COPD-Asthma
• Needs and Specifications
– Image and non-image data formats beyond
DICOM (eg, XML, TIFF, NiFTI)
–Wide variety of clinical metadata
–Data input, search, Q/R capabilities
QIBA Work Groups
• Needs and Specifications
– Image de-identification; data validation
–Security, user authentication, group
sharing
–Application install
–Data output statistics and analytics
functions, though not image display.
QIBA Work Group Needs
Page 37
Quantitative imaging biomarker use cases
� CT volumetric image analysis for
management of patients with lung cancer.
� Quantification of tumor metabolism using
FDG-PET standardized uptake value (SUV)
image analysis.
Clinical Examples
• Current “Gold Standard” uses 2D
RECIST (Response Evaluation Criteria In Solid
Tumors) Metric
• Would CT tumor volumes be a
“better” measure?
Clinical Examples
Promise
Provide Clinical and Research
Communities with Tools for
Quantitative Imaging Methods
with which to Detect, Diagnose
and Treat Disease.
Page 38
• Begin with existing imaging data warehouse
tool (MIDAS / QI-Bench) and enhance.
–Free, Open-Source, Modular Software
• Configure QIDW in The Cloud and Open to
DCE-MRI WG for testing and proof-of-concept
implementation.
• Measure performance, adoption, use, cost,
project support.
• Address policy issues.
Short Term Goals
Workflow Integration
Intuitiveness, Medical Legal, Policy, Billing
Quantitative Imaging Adoption
IT Infrastructure Algorithms & Tools
Multi-collaborative Environment
Summary
• Medical Physicists and Imaging
Informaticists are not all that different.
• Physics and Informatics concepts can be
applied at all points along the imaging chain.
• Intervening to improve Safety, Quality,
Efficiency in the clinical an research
environments.
• And contributing to Knowledge Discovery.
Page 39
Summary
• Quantitative Imaging research is an
exciting key area in which medical
physicists and imaging informaticists
will need to participate.
• Integration of information from multiple
disparate systems, and a multi-
disciplinary collaborative culture will be
necessary to accelerate advances.
Once again,
a medical
breakthrough
that would not
have been
possible without
the aide of a
mouse...
