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A fully automated 3D centerline modeling algorithm for coronary arteries is presented. It utilizes a subset of standard rotational X-Ray angiography projections that correspond to a single cardiac phase. The projection selection is based on a simultaneously recorded electrocardiogram (ECG). The algorithm utilizes a region growing approach, which selects voxels in 3D space that most probably belong to the vascular structure. The local growing speed is controlled by a 3D response computation algorithm. This algorithm calculates a measure for the probability of a point in 3D to belong to a vessel or not.
Centerlines of all detected vessels are extracted from the 3D representation built during the region growing and linked in a hierarchical manner. The centerlines representing the most significant vessels are selected by a geometry-based weighting criterion.
The theoretically achievable accuracy of the algorithm is evaluated on simulated projections of a virtual heart phantom. It is capable of extracting coronary centerlines with an accuracy that is mainly limited by projection and volume quantization (0.25 mm). The algorithm needs at least 3 projections for modeling, while in the phantom study, 5 projections are sufficient to achieve the best possible accuracy. It is shown that the algorithm is reasonably insensitive to residual motion, which means that it is able to cope with inconsistencies within the projection data set caused by finite gating accuracy, respiration or irregular heart beats. Its practical feasibility is demonstrated on clinical cases showing automatically generated models of left and right coronary arteries (LCA/RCA).
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