1. Single-Seeded Coronary Artery Tracking in CT Angiography
Guy Lavia, Jonathan Lessicka,b, Peter C. Johnsonc and Divya Khullarc
aPhilips Medical Systems Technologies, Haifa, Israel
bRambam Medical Center, Haifa, Israel
cPhilips Medical Systems, Cleveland, OH

2. Objective
Extract the coronary artery tree from a CT volume dataset in a fast, robust and accurate manner.

3. Potential Pitfalls Touching veins Proximal chambers
Changing gray level along vessel path (down to muscular HU in distal parts)
Branching

4. Compromise: One Click per Branch
Conforms with user’s native workflow – handling one vessel at a time
Easier registration of results – each branch is labeled by user (for retrieving, reporting etc.)
Increased robustness
Faster – simple decisions; 2-3 sec per vessel

6. Define two measures for propagation directionV
regional (v1)
local (v2)
angular deviation

7. Initial filtering
The fat layer is firstly isolated by a predefined threshold
A morphological bottom-hat filter is applied to the fat layer wrapping the heart
A bottom-hat filtering is achieved by subtracting the morphological closing of the binary image (isolated fat) from the binary image itself
Used to enhance non-fatty troughs within the fat layer, i.e. mainly the coronaries

8. Adaptive threshold Adapt threshold by testing
front size
local angular deviation
regional angular deviation
Allow gradual decrease in gray level up to 50 HU
Minimum allowable threshold – 50 HU
Avoid overshooting by sorting voxels

9. Mean diameter & mean direction
contribution of current front
direction:

10. Decision making on front splits – a credit system
Diameter
Minimum angular deviation
Avoid backwards propagation
Gray level decrease

11. Stopping criteria Growth rate Minimum diameter
Maximum angular deviation

12. Aortic Root Extraction
planar front propagation
watershed segmentation
adaptive erosion

14. Algorithm Demonstration

15. Clinical Evaluation