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.
MICCAI