Image processing for cardiac and vascular applications

Transcription

1 Image processing for cardiac and vascular applications Isabelle Bloch LTCI, Télécom ParisTech Cardio-vascular imaging p.1/26

2 Image processing for cardiac imaging 1. For diagnosis in cardiology: segmentation, derived measures, perfusion, movement. 2. For oncology applications (heart = organ at risk). Requirements and validation depend on the application. Cardio-vascular imaging p.2/26

3 Segmentation for diagnosis Examples from R. El Berbari s PhD (collaboration with LIF and HEGP). Contraction and late enhancement images. Evaluation of left ventricle cinetics. Quantification of transmurality of myocardium infarctus. One slice during the cardiac cycle Late enhancement Cardio-vascular imaging p.3/26

4 Segmentation method Cardio-vascular imaging p.4/26

5 Segmentation method Optimal value of λ Cardio-vascular imaging p.4/26

6 Results Cardio-vascular imaging p.5/26

7 Results Cardio-vascular imaging p.5/26

8 Results Cardio-vascular imaging p.5/26

9 Results Cardio-vascular imaging p.5/26

10 Results Cardio-vascular imaging p.5/26

11 Results Cardio-vascular imaging p.5/26

12 Results Cardio-vascular imaging p.5/26

13 Late enhancement images Cardio-vascular imaging p.6/26

14 Late enhancement images Cardio-vascular imaging p.6/26

15 Late enhancement images Cardio-vascular imaging p.6/26

16 Late enhancement images Cardio-vascular imaging p.6/26

17 Extensions Others: multi-centric evaluation... Cardio-vascular imaging p.7/26

18 Heart segmentation for oncology applications (A. Moreno, J. Wojak) Using structural constraints Cardio-vascular imaging p.8/26

19 Heart segmentation for oncology applications (A. Moreno, J. Wojak) Using structural constraints and a breathing model Cardio-vascular imaging p.8/26

20 Heart segmentation for oncology applications (A. Moreno, J. Wojak) Cardio-vascular imaging p.8/26

21 Heart segmentation for oncology applications (A. Moreno, J. Wojak) Using shape constraints Magenta = structural constraints, red = shape constraints, green = manual Cardio-vascular imaging p.8/26

22 Follow-up Heart segmentation for oncology applications (A. Moreno, J. Wojak) Cardio-vascular imaging p.8/26

23 Image processing for vascular imaging 1. High quality reconstruction from multiple MRI acquisitions. 2. Segmentation of brain vessels from MRA. 3. Segmentation of coronary vessels from high resolution CT. Cardio-vascular imaging p.9/26

24 High quality reconstruction from multiple MRI acquisitions (E. Roullot) Cardio-vascular imaging p.10/26

25 High quality reconstruction from multiple MRI acquisitions (E. Roullot) Cardio-vascular imaging p.10/26

26 High quality reconstruction from multiple MRI acquisitions (E. Roullot) result_anime Cardio-vascular imaging p.10/26

27 Vessel segmentation for... better visualization, diagnosis assistance (detection, quantification), virtual endoscopy... Some issues: classical ones: resolution, noise, partial volume effect... vessel specific: thin structures, bifurcations, anomalies... Three important components models (hypotheses), features (image information), extraction techniques. Cardio-vascular imaging p.11/26

28 Segmentation of brain vessels from MRA (B. Verdonck) Cardio-vascular imaging p.12/26

29 Segmentation of brain vessels from MRA (B. Verdonck) Cardio-vascular imaging p.12/26

30 Segmentation of brain vessels from MRA (B. Verdonck) Cardio-vascular imaging p.12/26

31 Segmentation of brain vessels from MRA (B. Verdonck) Cardio-vascular imaging p.12/26

32 Segmentation of brain vessels from MRA (B. Verdonck) Cardio-vascular imaging p.12/26

33 Segmentation of coronary vessels from high resolution CT (D. Lesage) Collaboration with Siemens Corporate Research. High resolution CT: 0.33 mm. Vessel model. Local features and measurements (flux). Segmentation expressed as a tracking process in a Bayesian framework, solved by: minimal path, particle filter. Cardio-vascular imaging p.13/26

34 Segmentation of coronary vessels from high resolution CT (D. Lesage) Cardio-vascular imaging p.13/26

35 Tracking based approach Cardio-vascular imaging p.14/26

36 Overview Cardio-vascular imaging p.15/26

37 Flux based measure Cardio-vascular imaging p.16/26

38 Comparison with other measures Cardio-vascular imaging p.17/26

39 Minimal path approach Cardio-vascular imaging p.18/26

40 Metric choice Cardio-vascular imaging p.19/26

41 Result example Cardio-vascular imaging p.20/26

42 Particle filter Cardio-vascular imaging p.21/26

43 Evolution Cardio-vascular imaging p.22/26

44 Result examples and evaluation Cardio-vascular imaging p.23/26

45 Result examples and evaluation Cardio-vascular imaging p.23/26

46 Comparison of the two approaches Evaluation on the Rotterdam database ( Measure Minimal path Particle filter (H = 4) (N = 1000) Overlap 85 % 86.2 % Distance to the central line (mm) Error on radius (mm) Computation time 1 min 4 min FP: less false positives (more robust stopping criterion). FP: more precise (no discretization of space). MP: less false negative (missing branches). Cardio-vascular imaging p.24/26

47 Conclusion Segmentation depends on: imaging data, available knowledge, requirements and final objective. Derived quantitative measures answering clinical needs. Importance of evaluation. Normal / pathological cases. Temporal / multi-modality images. Bifurcations and distal information (still open). Other applications and examples: other modalities (US, Doppler US, tagged MRI, DTI, TEP...), T1/T2 distribution, movement analysis, perfusion dynamics, 3D + t + multi-modal modeling of the heart,... Cardio-vascular imaging p.25/26

48 A few images US E. Angelini Cardio-vascular imaging p.26/26

49 A few images TEP Cardio-vascular imaging p.26/26

50 A few images Tagged MRI Cardio-vascular imaging p.26/26

51 A few images Whole heart model: Physiome project Models of electrical activation and myocardial mechanics at the whole organ level - Cardio-vascular imaging p.26/26