Reconstruction of vascular networks using three-dimensional models

Reconstructing vasculature in three dimensions is a challenging problem. Early approaches concentrated on coronary vasculature in X-ray images, recent work uses magnetic resonance imagery of cerebral vasculature. In both cases a priori information has been used, and often the way this is represented has proven limiting to the scope of applications supported. For example, a particular representation may be useful only for X-ray images. This paper addresses two issues: (1) representing a collection of vasculature and (2) the reconstruction of individual vasculature from images. The authors´ representation learns the variations in branching structures and vessel shapes that occur between individuals. It supports a vascular catalogue containing three-dimensional (3-D) anatomical models. The representation is task independent: here the authors use it to reconstruct vasculature from images. Their algorithm has 4 features to which they draw attention: (1) it is not premised wholly upon X-ray images (though that is the authors´ focus here); (2) it produces several feasible solutions rather than one; (3) it can generalize from the catalogue to reconstruct instances not yet learned; (4) it exhibits polynomial time complexity, reasonable memory consumption, and is reliable. Both the authors´ representation and reconstruction algorithm are new and useful approaches. In support of these claims, they present results gathered from X-rays of both simulated and real vasculature.