GEOMETRY-CONSTRAINED CORONARY ARTERIES MOTION ESTIMATION FROM 2D ANGIOGRAMS - APPLICATION TO INJECTION SIDE RECOGNITION

This paper deals with the 2D motion estimation of coronary arteries. It exploits the geometry of acquisition to strongly constrain the problem, thereby ensuring smooth and robust motion fields. The main contribution of this paper is to formally associate the C-arm angulation from which the heart is observed to the expected parametric motion field. Once this learning phase performed, every new sequence is associated to a fixed motion model, known up to a scale and an origin. The quality of the extracted velocities is visually evaluated over a large representative database. We also present an application of our approach to the motion-based classification of an angiographic exam into right or left artery tree.