model-based respiratory motion compensation for image ...€¦ · yoshinobu sato, shin nakajima,...

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