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

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

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

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

Define two measures for propagation direction V regional (v1) local (v2) angular deviation

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

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

Mean diameter & mean direction contribution of current front direction:

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

Stopping criteria Growth rate Minimum diameter Maximum angular deviation

Aortic Root Extraction planar front propagation watershed segmentation adaptive erosion

Algorithm Demonstration

Clinical Evaluation