Geometric flows for segmenting vasculature in MRI: Theory and validation

Abstract

Often in neurosurgical planning a dual spin echo acquisition is performed that yields proton density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. The development of vessel segmentation algorithms for PD images is of general interest since this type of acquisition is widespread and is entirely noninvasive. Whereas vessels are signaled by black blood contrast in such images, extracting them is a challenge because other anatomical structures also yield similar contrasts at their boundaries. In this paper, we present a novel multi-scale geometric flow for segmenting vasculature from standard MRI which can also be applied to the easier cases of angiography data. We first apply Frangi's vesselness measure to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which is orthogonal to vessel boundaries so that the flux maximizing geometric flow algorithm of can be used to recover them. We perform a quantitative cross validation on PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, all obtained for the same subject. A significant finding is that whereas more than 80% of the vasculature recovered from the angiographic data is also recovered from the PD volume, over 25% of the vasculature recovered from the PD volume is not detected in the TOF data. Thus, the technique can be used not only to improve upon the results obtained from angiographic data but also as an alternative when such data is not available. © Springer-Verlag Berlin Heidelberg 2004.