A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model
Fig 3
Automatic detection of vascular networks is divided into two consecutive workflows.
(I) In the first part, (A) a connected, binary mask of the vascular network is generated from the input image (i.e. the ToF or the QSM in the case of a real application), here represented as a synthetic image demonstrating a small network of vessels. (B) Segmentation by adaptive thresholding creates a first approximation to the vascular network. However, due to local dropout in the signal, the segmented map also contains a satellite structure disconnected from the root structure. Computing the distance function around the root structure with the image itself as a speed function generates a favorable map which can be used for backtracing from the satellite structure to the root structure. This procedure generates a most probable path connecting these two structures (green path). (C) End points of the resulting, connected vascular network are either root points or leafs. (II) In the second part, from the connected network in (I), we identify leafs, root points, the skeleton, as well as network nodes. (D) Computing a distance function around the binary segmentation generates a map for a second backpropagation. A consecutive backpropagation from leaf 1 and 2 towards the root ensures a connected skeleton of the network. In addition, the procedure provides the nodes as the points of intersection of two paths of backpropagation, here indicated by the red arrow intersecting the green path.