Reconstruction of coronary artery centrelines from X-ray angiography using a mixture of student’s t-distributions

Three-dimensional reconstructions of coronary arteries can overcome some of the limitations of 2D X-ray angiography,namely artery overlap/foreshortening and lack of depth information. Model-based arterial reconstruction algorithms usually rely on 2D coronary artery segmentations and require good robustness to outliers. In this paper,we propose a novel probabilistic method to reconstruct coronary artery centrelines from retrospectively gated X-ray images based on a probabilistic mixture model. Specifically,3D coronary artery centrelines are described by a mixture of Student’s t-distributions,and the reconstruction is formulated as maximum-likelihood estimation of the mixture model parameters,given the 2D segmentations of arteries from 2D X-ray images. Our method provides robustness against the erroneously segmented parts in the 2D segmentations by taking advantage of the inherent robustness of t-distributions. We validate our reconstruction results using synthetic phantom and clinical X-ray angiography data. The results show that the proposed method can cope with imperfect and noisy segmentation data.