Visualization of MRI Diffusion Data by a Multi-Kernel LIC Approach with Anisotropic Glyph Samples

Abstract

In diffusion weighted magnetic resonance imaging (DW-MRI), high angular resolution imaging techniques have become available, allowing a voxel’s diffusion profile to be measured and represented with high fidelity by a fiber orientation distribution function (FOD), even in situations of crossing and branching white matter fibers. Fiber tractography algorithms, such as streamline tracking, are used for visualizing global relationships between brain regions. However, they are prone to errors, e.g., may miss to visualize relevant fiber branches or provide incorrect connections. Line integral convolution (LIC), when applied to diffusion datasets, yield a more local representation of white matter patterns, and due to the local restriction of its convolution kernel is less susceptible to visualizing erroneous structures. In this paper we propose a multi-kernel LIC approach, which uses anisotropic glyph samples as an input pattern. Derived from FOD functions, multi-cylindrical glyph samples are generated by analysis of a highly-resolved FOD field. This provides a new sampling scheme for the anisotropic packing of samples along integrated fiber lines. Based on this input pattern two- and three-dimensional LIC maps can be constructed, depicting fiber structures with excellent contrast and resolving crossing and branching fiber pathways. We evaluate our approach by simulated DW-MRI data as well as in vivo studies with a healthy volunteer and a brain tumor patient.