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, Our 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 we use it to reconstruct vasculature from images, Our algorithm has four features to which we draw attention: 1) it is not premised wholly upon X-ray images (though that is our 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 our representation and reconstruction algorithm are new and useful approaches, In support of these claims,,ve present results gathered from X-rays of both simulated and real vasculature.