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Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions
Matthias Schneider Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Imaging and Visualization Department (IM) Siemens Corporate Research, Princeton, NJ, USA
February 8
Outline
� Medical Background
� Motivation
� Breathing Motion Compensation
� Results
� (Vessel Segmentation)
Medical Background
>30 days Chronic Total Occlusion
20-30%
Percutaneous Coronary Intervention
<10%
Angiogram
CT Guidance Cardiac
CT
+
ECG Gating
ECG
BP
ECG Gated Sequence
Live CTO Crossing
Static Workflow
Breathing Motion
[Segers1999]
Breathing Motion Model
PCA
Model Estimation
Training Samples
A B
ModelSpectrum
10.4
0.115 0.028 0.0077 0.0021 0.00088
98.6% 99.7%
MotionModel
plane A plane B
first mode
second mode
Extended Clinical Workflow
Results
Phantom Experiments
A B
Cardiac&Respiratory Motion
A B
Respiratory Motion
Error Over Breathing Cycle
mean: 0.84±0.19 mm mean: 0.81±0.27 mm
MonoPlane
Error Over Breathing Cycle
mean: 10.4±2.15 mm
mean: 0.89±0.22 mm
CostFunction unconstrained
0
1
0.5
CostFunction model-based
0
1
0.5
CaptureRange 100%
50%
0%
3D TRE of initial guess
model-based
unconstrained
ConvergenceSpeed 636
34 47
model-based
cost function evaluations
unconstrained
3 modes 2 modes
Da a Clinical
Accuracy 3D TRE [mm]
1.79 1.57
1.16 0.83
Case 1 Case 2
unconstrained
1.42
0.85
3 modes 2 modes
Guidewire
A B
Guidewire Simulation
Simulation 1 Simulation 2
Guidewire Simulation
§ 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
Conclusion
ThankYou
References � Alejandro F. Frangi, Wiro J. Niessen, Koen L. Vincken, and Max A. Viergever. Multiscale vessel
enhancement filtering. In MICCAI, volume 1496/1998, pages 130–137, 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 143–168, Jun 1998.
� Tony Lindeberg. Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision, 30(2):117–156, 1998.
� W.P. Segars,et al. A realistic spline-based dynamic heart phantom. In IEEE Trans. Nucl. Sci., 1999.
� 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 2006.
� G. Shechter and et al. Respiratory motion of the heart from free breathing coronary angiograms. Medical Imaging, IEEE Trans. on, 23(8):1046–1056, Aug. 2004.
� G. Shechter and et al. Displacement and velocity of the coronary arteries: cardiac and respiratory motion. Medical Imaging, IEEE Trans. on, 25(3):369–375, March 2006.
� 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):1142–1150, Sept. 2002.
� 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):1132–1141, Sept. 2002.
� A. P. King and et al. A technique for respiratory motion correction in image guided cardiac catheterisation procedures. Medical Imaging, 6918(1):691816, 2008.
(model dimension )
Training Data
Covariance Matrix
Normalization
ModelEstimation
Motion Model
(component-wise)
(rigid transform per frame)
Eigenanalysis
Training Samples Case 1, LCA
A B
Case 2 RCA
A B
Static Workflow
Vessel Enhancement
Methods
Matched filter Directional filter Shape driven Hessian measures
Hessian
Eigenanalysis
Geometric Interpretation
[Frangi1998]
Vesselness Measures
Original Frangi Sato
Results
Geometric Interpretation
[Frangi1998]
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
Results
Original Vesselness Streamlines
Geometric Post-Processing
Streamlines Length Map Density Map
Length & Density
Streamlines
Centerline Extraction
Centerline
Original
Catheter Removal
Streamlines Catheter Correction Original
Robustness
Vesselness Streamlines Original + noise
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