Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images

Abstract

This paper presents a method for the detection, representation and visualisation of the cerebral vascular tree and its application to magnetic resonance angiography (MRA) images. The detection method is an iterative tracking of the vessel centreline with subvoxel accuracy and precise orientation estimation. This tracking algorithm deals with forks. Centrelines of the vessels are modelled by second-order B-spline. This method is used to obtain a high-level description of the whole vascular network. Applications to real angiographic data are presented. An MRA sequence has been designed, and a global segmentation of the whole vascular tree is realised in three steps. Applications of this work are accurate 3D representation of the vessel centreline and of the vascular tree, and visualisation. The tracking process is also successfully applied to 3D contrast enhanced MR digital subtracted angiography (3D-CE-MRA) of the inferior member vessels. In addition, detection of artery stenosis for routine clinical use is possible due to the high precision of the tracking algorithm.

Angiographic Image Analysis

“…Despite a few attempts to deal with the case of bifurcations, which can enable the recursive processing of a whole vascular tree [9,13,31], vessel tracking is especially wellfit for the segmentation of single vessels. In this case, the termination has to be considered.…”

“…In [10,68], authors used such operators for 3D vessel segmentation, including brain, liver and heart vessels. [110], (c) [31], (d) [68].…”

Vessel Segmentation Using a Shape Driven Flow

“…Since these methods rely on the intensity of the image, a noisy intensity map may result in incorrect filter response and additional shape information might be needed for a correct segmentation. Skeletons [2,3] can be used as a basis of graph analysis of vessels, and further processing is needed to extract the 3D shape of the vessel. Krissian et al use multiscale filtering, based on a set of gaussian kernels and their derivatives to extract a skeleton of vasculature [5].…”

“…Figure 5(a) shows an example of very noisy image data where areas of pixels close to the vessel have very similar image statistics. This results in a leak when segmented with the type of flow (2). More sophisticated algorithms can be devised based on image statistics or prior knowledge such as multiscale filter responses tuned to detect vessels [13,6,14], but these algorithms will be very specific to the type of data and image acquisition.…”

Statistical-Based Approach for Extracting 3D Blood Vessels from TOF-MyRA Data

“…A more generalized technique approximating the vessel cross section by a polygon has been developed in [4]. The Vessel centerlines can be detected using a multi-scale 3D filters [5], and has been modeled by a second order Bspline, and then extracted using iterative tracking technique [6]. A geodesic active contour and level set method has been proposed to segment MRA speed images [7], [8].…”