Download - Danny Ruijters 17 April 2008 2D-3D intra-interventional registration of coronary arteries
Danny Ruijters17 April 2008
2D-3D intra-interventional 2D-3D intra-interventional registration of coronary arteriesregistration of coronary arteries
22D-3D intra-interventional registration of coronary arteries
Outline
• Introduction
• 2D-3D Registration
• Results
• Measurements
• Future work
• Conclusions
32D-3D intra-interventional registration of coronary arteries
Introduction
42D-3D intra-interventional registration of coronary arteries
What is Coronary Artery Disease?
• Coronary Artery Disease (CAD) is a condition in which plaque builds up inside the coronary arteries. These arteries supply the heart muscle with oxygen-rich blood.
• Plaque is made up of fat, cholesterol, calcium, and other substances found in the blood. When plaque builds up in the arteries, the condition is called atherosclerosis.
Source: National Heart Lung and Blood Institute, www.nhlbi.nih.gov
52D-3D intra-interventional registration of coronary arteries
Diagnosis / treatment planning: Cardiac CT
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Treatment guidance: X-ray angiography
Z
Y
X
Angulation
Rotation
L-arm
Cathlab = Catherization Laboratory
72D-3D intra-interventional registration of coronary arteries
Cardiac CT & X-ray
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Coronary centerlines
92D-3D intra-interventional registration of coronary arteries
2D-3D Registration
102D-3D intra-interventional registration of coronary arteries
2D-3D Vessel Registration
• Intensity-based methods - landmarks too small
• Iterative Closest Point - segmentation not robust
• Conclusion: new method needed
DRR X-ray
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Vesselness Filter
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Vesselness Filter
Frangi et al.: Multiscale vessel enhancement filtering. MICCAI'98, 130-137
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Projection of the coronary centerlines
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Distance Transform
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Similarity Measure
*
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Stochastic optimizationGeneration
0
1
2
n best results produce m children
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Results
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Registration
Stochastic Optimizer
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Registration
Powell Optimizer
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Overlay visualization
212D-3D intra-interventional registration of coronary arteries
Measurements
222D-3D intra-interventional registration of coronary arteries
Measuring accuracy and capture range?
• Ground truth needed!!! → not possible for real world data.
• The perfect registration does not exist:
– Cardiac motion
– Respiratory motion
– Patient pose
• Solution: use simulated data.
• Solution: use real world data, measure capture range by measuring translation and rotation of a successful registration.
• Objectivity, relevance to the clinical practice.
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Maximum capture range, using clinical data
242D-3D intra-interventional registration of coronary arteries
Simulate X-Ray, using cardiac CT
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Segmented CT
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Segmented forward projection
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Segmented forward projection
282D-3D intra-interventional registration of coronary arteries
Residual Error, using simulated data
Same set of initial transformations (translation and rotation) was used for all methods.
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Future work
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Improving the similarity measure
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Tangent: Eigen vectors of the Hessian
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Multi-scale approach
• Start with coarse scales, and refine
• Gaussian blur the 2D image / vesselness image
• Use wider distance transform by increasing constant c
• 2nd order derivative → Quasi-Newton optimization
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Conclusions
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Conclusions
• Clinical prototype installed in hospitals New York and Denver
• Very robust: accurate & large capture range
• Relatively fast: Powell 2.7 seconds, stochastic 11 seconds
Thank you!!!