A three-dimensional (3D) reconstruction of the vessel lumen from two angiographic views, based on the reconstruction of a series of cross-sections, is proposed. Assuming uniform mixing of contrast medium and background subtraction, the cross-section of each vessel is reconstructed through a binary representation. A priori information about both the slice to be reconstructed and the relationships between adjacent slices are incorporated to lessen ambiguities on the reconstruction. Taking into account the knowledge of normal vessel geometry, an initial solution of each slice is created using an elliptic model-based method. This initial solution is then deformed to be made consistent with projection data while being constrained into a connected realistic shape. For that purpose, properties on the expected optimal solution are described through a Markov random field. To find an optimal solution, a specific optimization algorithm based on simulated annealing is used. The method performs well both on single vessels and on branching vessels possessing an additional inherent ambiguity when viewed at oblique angles. Results on 2D slice independent reconstruction and 3D reconstruction of a stack of spatially continuous 2D slices are presented for single vessels and bifurcations.

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The paper presents a method to model an arterial bifurcation from a pair of X-ray angiographic images. It is the initial step of a reconstruction process aiming at detecting and quantifying abnormal sites located on bifurcations. The method proposed consists of two steps. First, each image is independently segmented to extract the vessels in the images. The algorithm uses dynamic programming first to find the bifurcation centrelines from the original images, and secondly to extract vessel edges from the morphological gradient images, under a constraint of parallelism with the previously detected centrelines. Then, a three-dimensional bifurcation model is built by adapting cylinders around the three-dimensional bifurcation centrelines. These cylinders are obtained as a stack of binary orientable ellipses fitted to the projection densities in the corresponding cross-sections. Results obtained on simulated data, phantom and femoral bifurcations are displayed.

This paper presents a sequence of processings applied in parallel on two stereoscopic projections of angiographic images, in order to detect vascular pathologies site on bifurcations. Following directing centerlines of the bifurcation detection via a peaktracking algorithm, vascular boundaries are searched via dynamic programmation on the image of the morphological gradient. The two segmented original images are then matched in order to build a 3D bifurcation model made of generalised cylinders. Ultimately, this model will be deformed to minimize an energy function using an optimization method. Preminilary steps of projection processing are displayed on images of real bifurcations.