Deep Learning Quantitative Analysis of Cardiac Function

Transcription

1 Deep Learning Quantitative Analysis of Cardiac Function Validation and Implementation in Clinical Practice A L B E R T H S I A O, M D, P H D A S S I S TA N T P R O F E S S O R O F R A D I O LO G Y, I N - R E S I D E N C E D I R E C TO R, A U G M E N T E D I M A G I N G D ATA A N A LY T I C S ( A I D A ) L A B O R ATO R Y A S S O C I AT E D I R E C TO R, C A R D I O VA S C U L A R I M A G I N G, C T I P M U N I V E R S I T Y O F C A L I F O R N I A, S A N D I E G O F O U N D E R, A R T E R Y S I N C.

2 Disclosure Statement of Financial Interest Within the past 12 months, I have had a financial interest/arrangement or affiliation with the organization(s) listed below. Affiliation/Financial Relationship Ownership/Founder Major Stock Shareholder/Equity Intellectual Property Rights Research Grant Support Consultant Company Arterys, Inc. GE Healthcare GE Global Research

3 Learning Objectives By the end of this session, learners should be able to better: 1. Understand the principles and concepts underlying convolutional neural networks, so-called Deep Learning. 2. Develop a framework for assessing new technologies and implementing them in clinical practice.

4 Healthcare Landscape Man versus machine?

5 Machine Learning, Deep Learning and AI An Ocean of Solutions in Search of a Problem

6 Healthcare Landscape Consolidation of providers and insurers

7 Healthcare Landscape Looming physician shortage Kirch DG, Henderson MK, Dill MJ Physician Workforce Projections in an Era of Health Care Reform. Annu. Rev. Med. 63:

8 Deep Learning Representations NVIDIA, Mountain View, CA Learns from data Creates representations at different scales Paradigm shift from optimizing features to optimizing models / networks

9 In vivo neural network Rat cerebellum

10 In vivo neural network Rat cerebellum

11 Case Example Cardiac size and function Essential for patient management Assess longitudinal change Chemotherapy Myocardial infarct Very labor-intensive Manual contouring / segmentation Few skilled experts Cardiac radiologists Imaging cardiologists Highly-trained lab technologists

12 Network architecture Heart segmentation cardiac size and function ~10 7 nodes J Lieman-Sifry, M Le, F Lau, S Sall, D Golden. FastVentricle: Cardiac Segmentation with ENet. arxiv:

13 Network architecture Heart segmentation cardiac size and function Large volume of labeled data shown to network Weights updated via backpropagation of errors Inference: uses final weights with forward pass NVIDIA, Mountain View, CA

14 Heart segmentation Cardiac size and function Background LV Epi LV Endo RV Endo J Lieman-Sifry, M Le, F Lau, S Sall, D Golden. FastVentricle: Cardiac Segmentation with ENet. arxiv:

15 Images courtesy of Arterys, Inc. Heart segmentation Cardiac size and function

16 Heart segmentation Deep dream J Lieman-Sifry, M Le, F Lau, S Sall, D Golden. FastVentricle: Cardiac Segmentation with ENet. arxiv:

17 Validation and Implementation How do we go about validating new technologies? How do we implement them in clinical practice? Case example: 4D Flow MRI

18 Validation of Technology 4D Flow MRI 4-components Anatomic data (m) Velocity field (v x, v y, v z ) 4-dimensional Volumetric (x, y, z) Time-resolved (t) Multiple names 4D Flow Volumetric phase-contrast 3D time-resolved phase-contrast

19 4D Flow MRI Flow Quantification Hsiao, et al. Pediatr Radiol Jun;41(6):711-20

20 4D Flow MRI Flow Quantification vs Conventional 2D-PC Q p and Q s are expected to match exactly in the absence of a shunt. Balance of Q p, Q s provides an internal control. Improved precision! 2D PC-MRI 4D PC-MRI Tighter Q p /Q s correlation with 4D Flow than with 2D PC (p<0.05, F-test; p<0.05, Brown-Forsythe) Hsiao, et al. Pediatr Radiol Jun;41(6):711-20

21 4D Flow MRI Ventricular Volume Quantification Hsiao, et al. Am. J. Roentgenol. 2012; 198(3), W250 9

22 4D Flow MRI Ventricular Volume Quantification vs Conventional SSFP Comparable accuracy End-systolic LV volume RV volume End-diastolic LV volume RV volume Hsiao, et al. Am. J. Roentgenol. 2012; 198(3), W250 9

23 4D Flow MRI Ventricular Volume Quantification Ejected volume and outlet valve flow should match! Hsiao, et al. Am. J. Roentgenol. 2012; 198(3), W250 9

24 4D Flow MRI Ventricular Volume Quantification 2DPC SSFP Overestimation of flow by 2D-PC relative to SSFP Hsiao, et al. Am. J. Roentgenol. 2012; 198(3), W250 9

25 4D Flow MRI Ventricular Volume Quantification 4DPC Unbiased flow and volume Better precision! measurements by 4D-PC Hsiao, et al. Am. J. Roentgenol. 2012; 198(3), W250 9

26 4D Flow MRI Diagnostic Visualization Aortic Pulmonic Mitral Tricuspid Hsiao, et al. Radiology. 2012; 265(1), 87 95

27 Repaired Tetralogy of Fallot Pulmonary regurgitation Tricuspid regurgitation

28 Repaired Tetralogy of Fallot Excess volume loading Net flow: L/min Regurgitant fraction: 48% Regurgitant volume: L/min

29 Internal Iliac Flow Fraction (I/E) Total Internal Iliac Flow Fraction I/E T2w DISCO Pre-embo Post-embo Fibroid Volume (ml) Right 4D-Flow Left 4D-Flow Volume of PVA to Stasis (ml)

30 Right Ovarian Artery Left Ovarian Artery DISCO 4D-Flow DISCO 4D-Flow

31 1 mo after SRS 6 mo after SRS 12 mo after SRS L MCA L MCA L MCA

32 Flow (L/min) R ICA L ICA 0.6 Blood flow vs. time R MCA L MCA 0.55 L ICA 0.5 L MCA R ICA 0.3 R MCA Months after SRS

33 Heart segmentation Cardiac size and function

34 Heart segmentation Cardiac size and function

35 Images courtesy of Arterys, Inc. Cardiac landmark localization

36 Images courtesy of Arterys, Inc. Cardiac landmark localization Automatically defining cardiac imaging planes

37 Albert Hsiao, MD, PhD