Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion\ntensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy\nmapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an\nadvancement of a previously published multi kernel LIC approach for high angular resolution diffusion imaging visualization is\nproposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to\nthe LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which\nprovide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two and\nthree-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and\nbranching fibers. Furthermore, a color-codingmodel for the fused visualization of slices fromT1 datasets together with directionally\nencoded LICmaps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing\nand bending fibers. In addition, results from in vivo studies with a healthy volunteer and a brain tumor patient are presented to\ndemonstrate the method�s practicality.
Loading....