Physiologically based modeling of 3-D vascular networks and CT scan angiography

Abstract

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.

Parallel Implementation of Vascular Network Modeling

“…In its generic form [8], the discussed model was constructed for the modeling of internal organs which develop by a division of their structural elements. But it should be emphasized that it is oriented towards an image generation.…”

“…When there are more than one tree, the algorithm chooses all possible combinations of candidate vessels (a single combination consists of one vessel from each tree). The spatial position of the bifurcation is controlled by the Downhill Simplex procedure [10] (minimization of additional blood volume needed for the new MFU perfusion [8]). …”

“…In the research we also made use of the sequential algorithm to generate a virtual organ of liver (represented by three vascular trees and parenchyma) [8], CT simulator and MRI virtual scanner implemented in a parallel environment. In this paper, however, we propose a parallel algorithm of the vascular network growth, based on the previously used sequential algorithm.…”

Blood Flow Simulation in Patient-Specific Segmented Hepatic Arterial Tree

“…Clearly the fluid and microspheres propagation in small vessels is far more complex to analyze, and for this reason we cannot expect to have access to a precise vessels network shape: based on angiographic data we can only expect, at this level, to segment the tumor regions, and this is not in the scope of this paper. Let us only indicate here that coupling real data and simulated data based on image characteristics could help solving this scale limitation, that could be achieved using a computational model of the finer vascular network, like the one we previously developed [19,16].…”

Modelling of blood flow resistance for an atherosclerotic artery with multiple stenoses and poststenotic dilatations

“…This data is collected by invasive or non-invasive means. Angiographic machinery [6,7,26] assesses the reduction in flow due to these obstructions so as to provide useful physiological data for cardiologists during cardiac diagnosis. Since the vessels are opaque to light, well established flow measurement techniques like laser Doppler velocimetry [21,10] or particle image velocimetry [10] cannot be applied.…”