professor girish dwivedi md, mrcp (uk), phd (uk), fase
TRANSCRIPT
Professor Girish DwivediMD, MRCP (UK), PhD (UK), FASE, FESC, FRACPWesfarmers Chair in Cardiology & Consultant Cardiology (Fiona Stanley hospital)
Faculty Disclosure
•Speaker Bureau fee for Amgen, Pfizer and Astra Zeneca
•Advisory capacity for Artyra Pty Ltd and also equity interest.
•Many of the AI slides are provided by Artrya Pty Ltd and research is done in collaboration with Artrya
Tools
Tools
❑ Review the CT techniques and literature- why I think it
has enormous potential.
❑ Why AI suited for Cardiac CT interpretation
❑ Identify future perspectives
Objectives
Stable Chest Pain
• New onset stable chest pain is a common clinical problem
• USA: 4 million stress tests / year
• Australia: 233,000 Stress tests in 2014 (6m)
• EST, Myocardial Perfusion and Stress Echo
• Only 2-3% led to revascularization
• Patients with “non-cardiac” chest pain: make up 1/3 of
patients dying from CVD at 5 years
Need for improvement in:
Diagnostic Accuracy
Risk Stratification
Maurovich-Horvat, P. et al.(2014) Nat. Rev. Cardiol.
Mourovich-Horvat et al., JACC Imaging 2012
❑ Nonobstructive disease
❑ “Vulnerable” plaques.
- The majority of lesions that rupture and lead to ACS werenon-obstructive (<50%) on antecedent coronaryangiography.- 50% men and 64% women ACS or death is the first
manifestation
❑ CT Myocardial Perfusion Imaging
❑ CT FFR
Prognostic Value of Cardiac CT: High Grade Stenosis
Min JK et al. J Am Coll Cardiol 2011
0
2
4
6
8
10
12
14
16
1VD 2VD 3VD
HR 1.93 HR 2.74 HR 6.09
n=2,583, all with <50% stenosis
Followed for 3.1 years for ACM
>6-fold higher mortality for patients with 3V “mild” CADLin FY et al. J Am Coll Cardiol 2008
Prognostic Value of Cardiac CT: ’Mild’ Stenosis
Prognostic Value of Stenosis
Extensive non-obstructive CAD (SIS >4) had a similar rate of cardiovascular death or MI compared with less extensive obstructive
disease (SIS <4).
Bittencourt et al. Circ Imaging 2014
High Quantitative High Risk Plaque Features
Motoyama et al, JACC 2009
Qualitative High Risk Plaque Features: Independent Predictor of ACS
ICONIC: 25,251 patients undergoing CT, 3.4 years
Propensity Score
Age and Gender
Site
CAD Risk Factors
Angiographic CAD extent &
severity^
Patient who experienced ACS after CCTA
Case (n=234)
Patient who did not experience ACS after CCTA
Control (n=234)
When angiographic CAD extent and severity is the same, do atherosclerotic plaque characteristics matter?
Chang et al, JACC 2018
ICONIC Results: Maximal % stenosis at time of CT
<50% stenosis
65.4%21.8%
12.8%
Patient
(n=234)
50-70% stenosis >70% stenosis
75.2%20.1%
4.7%
Culprit Lesion
(n=129)
Chang et al, JACC 2018
Quantitative Plaque Analysis
Voros et al, JACC imaging 2011; Liu…dwivedi et al, JTI 2016, Gahungu et al, IJCVI 2020
Quantitative Plaque Assessment: Plaque Volume, Plaque Burden, Plaque Composition
Composition: Non-Calcified vs Calcified NCP: Necrotic Core, Fibrofatty, Fibrous
STRESSRESTSTRESSREST
MPR 7mm, MiniP 7mmWL/WW 150/200 with free user manipulation
Inacio, dwivedi…et al, RSNA 2016
Physiological Principles of CT-FFR
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8 CFCB6 FMGMGH: A G
; ; F8 H
% %- %. %/ %0 ( % BCH: G
2 F CG - F CFA
2 C LE DN
8CA G GF O
A 6 M7: ; I B8 H>CB6 @@MG>< B>; >8 6 BH
( )
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; ; F8 H dOcSa OS a SQWTWSR RWabO b RS SR abS aSa 4 %
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% %- %. %/ %0 ( %
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A 6 MBCH7:
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0 . 5 /
Maurovich-Horvat, P. et al.(2014) Nat. Rev. Cardiol.
• Stenosis severity grade
• Plaque burden
• Plaque volume
• Plaque composition
• Diffuseness
• Remodeling
• Distance from ostium
• Distance from bifurcation
• Ischaemia (CT-FFR)
• Myocardium at risk
• Myocardial mass
• Perfusion
• Vascular Inflammation
• Epicardial fat
Cardiac CT Metrics
Object Recognition(Automated Multi-Metric CT Analysis)
Real-time Decision Making(Changes in Care)
Machine Learning
Full Disease and Plaque Analysis across entire CT scan
Idhayhid….Dwivedi et al. SCCT, CSANZ 2020
Feasibility and Performance of Fully Automated Coronary Artery Calcium Scoring
Using Deep Machine Learning Abdul Rahman Ihdayhid1, Casey Lickfold2, Julien Flack2, Brendan Adler3, Lawrence
Dembo3,6, Jack Joyner2, Benjamin J Chow4, Girish Dwivedi4,5,6
1 Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Australia2 Artyra Pty Ltd., Perth, Australia
3 Envision Medical Imaging, Perth, Australia4 Division of Cardiology, University of Ottawa Heart Institute, University of Ottawa, Ottawa, Canada.
5 Harry Perkins Institute of Medical Research and University of Western Australia, Perth, Australia6 Fiona Stanley Hospital, Murdoch, WA, Australia
Idhayhid….Dwivedi et al. SCCT, CSANZ 2020
Method A
N=1055
Non-Contrast CTCA Siemens SOMATOM, Prospective ECG Gated, 120 kV
Manual CACS: Per-Patient + Per-VesselExperienced CTCA Readers
Development of Radiomics + ML CAC Algorithm: Artrya, University of Western Australia, Monash University
Method B
N=4807Method C
N=4807
ICC
Diagnostic Accuracy – Precision – Analysis Time
Suspected CADAge > 18 years
Bland-AltmannKappaweighted
Inclusion Criteria
PCI, Grafts, PPM, Metal Implants
Excluded
Methods
*Testing data separate from training + validation
Cases divided: Training | Validation | Testing
1. Input Non-Contrast CTCA 2. Identify Pixels ≥130 HU 3. 3D Map of Connected Voxels ≥130 HU
CAC CAC
Principles of Automated CAC
4. Extraction of Radiomic Features
Train Standard Neural Network
Hybrid Neural Network for CAC Prediction
Image Patch
5. Image Patch Around Component
Train Convolution Neural Network
LMCA
Aortic
Ground Truth Prediction
0.45 1.59 0.68 1.82
Non-V
esse
l
LMCA
LAD
LCX
RCA
0
20
40
60
80
100
Rela
tive F
req
uen
cy (
%)
‘Connected’ Voxels Labelled as Same Structure Challenging to differentiate LMCA from Aortic Calcification >99% of voxels ≥ 130 HU are not coronary
• Inefficiency in analysis time
• Risk of misclassification
Distribution of ≥ 130 HU
Automatic CAC: The Challenge
Automated CAC: Method A Input CT Method A ❌
Method B ✅
Aortic Segmentation CNN
Dice Score 94
Automated CAC: Method B Method B: Updated + Custom CNN• Method A: Image patch was analyzed with a standard AlexNET
• Analyses image patches in 2D (i.e axial plane)
• Method B
a) Custom CNN: analyze image patch in multiple dimensions
• Coronal + Sagittal Planes in addition to axial plane
b) Increased training data set from N=606 to N=2135
Custom CNN
Custom CNN AlexNET CNN
CNN trained on larger and more complex image features
Automated CAC: Method C Ground Truth Method B: Incorrect Prediction
Non-cardiaccalcium
IncorrectLCX
Cardiac ROI CNNDice Score 92
Method C: Improves Prediction
CorrectNon-CAC
Method C: Results
CA
C
0.85
0.93
0.98
A B C
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Method
Kap
pa
8.7
3.8
1.2
10.4
5.1
1.9
A B C
0
2
4
6
8
10
12
Mis
cla
ssif
icati
on
(%
)
Shift Up
Shift Down
Large improvement in Kappa with low % of misclassification of risk category
0 1-10 11-100 101-400 >400 Total Diagnostic Accuracy (%) Shift Up (%) Shift Down (%)0 880 5 0 0 0 885 99.44 0.56 0.00
1-10 21 233 11 0 0 265 87.92 4.15 7.92
11-100 4 5 375 6 0 390 96.15 1.54 2.31
101-400 0 0 2 267 2 271 98.52 0.74 0.74
>400 0 0 1 4 142 147 96.60 0.00 3.40
Total 905 243 389 277 144 1958 96.88 1.23 1.89
CACML Reclassification
0 250 500 750-400
-200
0
200
400
Average
CA
C -
CA
CM
L
0 250 500 750-400
-200
0
200
400
Average
Dif
fere
nce
0 250 500 750-400
-200
0
200
400
Average
Dif
fere
nce
Mean Diff.: 2.3 ± 129.2 LOA: -250.9 to 255.6
Mean Diff.: 4.7 ± 129.2 LOA: -173.2 to 182.6
Mean Diff.: 2.4 ± 47.0 LOA: -89.7 to 94.6
Method A Method B Method C
Method C: Precision
Method C associated with narrowest limits of agreement andoverall improved precision
Non-Vessel LMCA LAD LCX RCA
0
20
40
60
80
100
Rela
tive F
req
uen
cy (
%)
Method A Method C
Distribution of ≥ 130 HU
Improved Discrimination of Non-vessel vs Coronary Calcium40% Reduction in Analysis Time
Method C: Analysis Efficiency
3.7
4.0
2.4
0 1 2 3 4 5
A
B
C
Time (min)
Analysis Time
Met
ho
d
Artery Tracking and Labeling
Artery Tracking and Labeling
AI artery analysis: Part 2
36
Tracked Arteries
Extracted walls and disease detected
Patient specific model
High-Risk
Plaque
Shear
Stress
Myocardium
At Risk
AMI
Death
Ischaemia
(FFR)
Cardiac CT
Cardiac CT
Cardiac CT
Cardiac CT
Non-Invasive Risk Assessment- CT and AI
Summary
❑CCTA now allow coronary atheroma to be visualized directly
❑Detailed information adverse plaque characteristics,
perfusion, FFR etc. possible with CCTA.
❑ However, for CCTA to achieve its potential when need AI.
❑AI is the future but considerable work remains to be done to
translate this information risk-prediction tools and improved
clinical care.
Thank You