Skip to Main Content
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.