Stochastic 3D motion compensation of coronary arteries from monoplane angiograms

Stochastic 3D motion compensation of coronary arteries from monoplane angiograms

Hadida, Jonathan and Desrosiers, Christian and Duong, Luc

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012

Abstract : Image-based navigation during percutaneous coronary interventions is highly challenging since it involves estimating the 3D motion of a complex topology using 2D angiographic views. A static coronary tree segmented in a pre-operative CT-scan can be overlaid on top of the angiographic frames to outline the coronary vessels, but this overlay does not account for coronary motion, which has to be mentally compensated by the cardiologist. In this paper, we propose a new approach to the motion estimation problem, where the temporal evolution of the coronary deformation over the cardiac cycle is modeled as a stochastic process. The sequence of angiographic frames is interpreted as a probabilistic evidence of the succession of unknown deformation states, which can be optimized using particle filtering. Iterative and non-rigid registration is performed in a projective manner, and relies on a feature-based similarity measure. Experiments show promising results in terms of registration accuracy, learning capability and computation time.