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

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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

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Cardiac CT & X-ray

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Coronary centerlines

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2D-3D Registration

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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

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Measurements

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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

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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

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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!!!