Coronary territory maps, which associate myocardial regions with the corresponding coronary artery that supply
them, are a common visualization technique to assist the physician in the diagnosis of coronary artery disease.
However, the commonly used visualization is based on the AHA-17-segment model, which is an empirical population
based model. Therefore, it does not necessarily cope with the often highly individual coronary anatomy
of a specific patient.
In this paper we introduce a novel fully automatic approach to compute the patient individual coronary
supply regions in CTA datasets. This approach is divided in three consecutive steps. First, the aorta is fully
automatically located in the dataset with a combination of a Hough transform and a cylindrical model matching
approach. Having the location of the aorta, a segmentation and skeletonization of the coronary tree is triggered.
In the next step, the three main branches (LAD, LCX and RCX) are automatically labeled, based on the
knowledge of the pose of the aorta and the left ventricle.
In the last step the labeled coronary tree is projected on the left ventricular surface, which can afterward be
subdivided into the coronary supply regions, based on a Voronoi transform. The resulting supply regions can be
either shown in 3D on the epicardiac surface of the left ventricle, or as a subdivision of a polarmap.