Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions

Transcription

1 Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions February 8 Matthias Schneider Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Imaging and Visualization Department (IM) Siemens Corporate Research, Princeton, NJ, USA

2 Outline Medical Background Motivation Breathing Motion Compensation Results (Vessel Segmentation)

3 Medical Background

4 >30 days Chronic Total Occlusion 20-30%

5 <10% Percutaneous Coronary Intervention

6 CT Guidance Cardiac CT + Angiogram

7 ECG Gating ECG BP ECG Gated Sequence

8 Live CTO Crossing

9 Static Workflow

10 Breathing Motion [Segers1999]

11 Breathing Motion Model

12 PCA

13

14 Model Estimation

15 Training Samples A B

16 10.4 ModelSpectrum 98.6% 99.7%

17 MotionModel first mode plane A plane B second mode

18 Extended Clinical Workflow

19 Results

20 Phantom Experiments

21 Cardiac&Respiratory Motion A B

22 Respiratory Motion A B

23 Error Over Breathing Cycle mean: 0.84±0.19 mm mean: 0.81±0.27 mm

24 MonoPlane

25 Error Over Breathing Cycle mean: 10.4±2.15 mm mean: 0.89±0.22 mm

26 CostFunction unconstrained

27 CostFunction model-based

28 CaptureRange 100% model-based 50% unconstrained 0% 3D TRE of initial guess

29 636 ConvergenceSpeed cost function evaluations 3 modes 2 modes unconstrained model-based

30 Clinical Da a

31 Accuracy 3D TRE [mm] unconstrained 3 modes 2 modes Case 2 Case 1

32 Guidewire A B

33 Guidewire Simulation

34 Guidewire Simulation Simulation 1 Simulation 2

35 Conclusion Breathing Motion Model provides better robustness and faster convergence CT guidance under fluoroscopy with breathing motion compensation becomes feasible Downside: motion model requires proper training data Outlook: Further clinical validation required Hardware-based guidewire tracking Breathing phase prediction Affine registration to further improve results

36 ThankYou

37

38 References Alejandro F. Frangi, Wiro J. Niessen, Koen L. Vincken, and Max A. Viergever. Multiscale vessel enhancement filtering. In MICCAI, volume 1496/1998, pages , 1998 Yoshinobu Sato, Shin Nakajima, Nobuyuki Shiraga, Hideki Atsumi, Shigeyuki Yoshida, Thomas Koller, Guido Gerig, and Ron Kikinis. 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In Medical Image Analysis, volume 2, pages , Jun Tony Lindeberg. Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision, 30(2): , W.P. Segars,et al. A realistic spline-based dynamic heart phantom. In IEEE Trans. Nucl. Sci., H. Sundar, A. Khamene, Ch. Xu, F. Sauer, and Ch. Davatzikos. A novel 2D-3D registration algorithm for aligning fluoroscopic images with pre-operative 3D images. In SPIE Medical Imaging, San Diego, USA, volume 6141, Feb G. Shechter and et al. Respiratory motion of the heart from free breathing coronary angiograms. Medical Imaging, IEEE Trans. on, 23(8): , Aug G. Shechter and et al. Displacement and velocity of the coronary arteries: cardiac and respiratory motion. Medical Imaging, IEEE Trans. on, 25(3): , March K. McLeish and et al. A study of the motion and deformation of the heart due to respiration. Medical Imaging, IEEE Trans. on, 21(9): , Sept D. Manke and et al. Model evaluation and calibration for prospective respiratory motion correction in coronary MR angiography based on 3-D image registration. Medical Imaging, IEEE Trans. on, 21(9): , Sept A. P. King and et al. A technique for respiratory motion correction in image guided cardiac catheterisation procedures. Medical Imaging, 6918(1):691816, 2008.

39 ModelEstimation Training Data (rigid transform per frame) Normalization (component-wise) Covariance Matrix Eigenanalysis Motion Model (model dimension )

40 Training Samples Case 1, LCA A B

41 Case 2 RCA A B

42 Static Workflow

43 Vessel Enhancement

44 Methods Matched filter Directional filter Shape driven Hessian measures

45 Hessian Eigenanalysis

46 Geometric Interpretation [Frangi1998]

47 Vesselness Measures

48 Results Original Frangi Sato

49

50 Geometric Interpretation [Frangi1998]

51

52 Global Vessel Segmentation 1. Hessian based second order information 2. Vesselness measure (arbitrary) 3. Vector field integration inside vessel Seed point Smoothness + Vesselness constraint Streamline bundles

53 Results Original Vesselness Streamlines

54 Geometric Post-Processing

55 Length & Density Streamlines Length Map Density Map

56 Centerline Extraction Original Streamlines Centerline

57 Catheter Removal Original Streamlines Catheter Correction

58 Robustness Original + noise Vesselness Streamlines

59 Conclusion (2) Automatic global vessel segmentation Applicable for any local probability-like vesselness map Global geometric shape information allows for advanced post-processing Future Work: Classification of the main branches