Anatomy-Focused Volume Line Integral Convolution for Brain White Matter Visualization

Abstract

3D visualization of volumetric line integral convolution (LIC) datasets has been a field of constant research. So far, most approaches have focused on finding suitable transfer functions and defining appropriate clipping strategies in order to solve the problem of occlusion. In medicine, extensions of the LIC algorithm to diffusion weighted magnetic resonance imaging (dwMRI) have been proposed, allowing highly resolved LIC volumes to be generated. These are used for brain white matter visualization by LIC slice images, depicting fiber structures with good contrast. However, 3D visualization of fiber pathways by volume rendering faces the problem of occlusion of anatomic regions of interest by the dense brain white matter pattern. In this paper, we introduce an anatomy focused LIC algorithm, which allows specific fiber architectures to be visualized by volume rendering. It uses an anatomical atlas, matched to the dwMRI dataset, during the generation of the LIC noise input pattern. Thus,anatomic fiber structures of interest are emphasized, while surrounding fiber tissue is thinned out and its opacity is modulated. Additionally, we present an adaptation of the orientation-dependent transparency rendering algorithm, which recently has been proposed for fiber streamline visualization, to LIC data. The novel methods are evaluated by application to dwMRI datasets from glioma patients, visualizing fiber structures of interest in the vicinity of the lesion.