| A stochastic method for three-dimensional (3D) coronary motion compensation from a monoplane temporal sequence of X-Ray angiographic frames is presented. The 3D coronary centerline is segmented from a preoperative multi-sclice computed tomography (MSCT), and subject to a non-rigid deformation model with few parameters. The 2D angiographic frames are segmented as well using a multiscale vesselness filter, thresholded, and skeletonized to obtain the 2D binary centerline of the coronary artery tree at each time.;A generative model is then introduced to model the process that results in the observed angiographic frames as a stochastic process, namely a Hidden Markov model. This latter is used along with a particle filter to constrain the variations of the deformation model's parameters over time, and relies on a feature-based cosine similarity measure involving a istance transformation on the binary coronary centerline images. Three-dimensional registration is performed in a projective and iterative manner.;Validation is carried out first through a set of simulations with real 3D coronary centerlines, and eventually with one 3D centerline and the associated angiographic sequence. |
