In this paper we present visualization approaches for HARDI-based neuronal pathway representations using fiber encompassing hulls. We introduce novel bundle visualization techniques to indicate characteristics, such as information about tract integrity and multiple intra-voxel diffusion orientations. To accomplish this task, we developed an intra-bundle raycasting approach and use color mappings to encode diffusion characteristics on the bundle's surface. Additionally, we implemented a slicing approach using a plane orthogonal to the centerline of a bundle which reveals intra-bundle diffusion characteristics as well as the local bundle shape. With the presented approaches, we simultaneously reveal features of fiber bundles such as integrity or information about the underlying diffusion profile as well as context information, the shape of a current tract.
Magnetic resonance diffusion imaging provides a unique insight into the white matter architecture of the brain in vivo. Applications include neurosurgical planning and fundamental neuroscience. Contrary to diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI) is able to characterize complex intra-voxel diffusion distributions and hence provides more accurate information about the true diffusion profile. Anisotropy indices aim to reduce the information of the diffusion probability function to a meaningful scalar representation that classifies the underlying diffusion and thereby the neuronal fiber configuration within a voxel. These indices can be used to answer clinical questions such as the integrity of certain neuronal pathways. Information about the underlying fiber distribution can be beneficial in tractography approaches, reconstructing neuronal pathways using local diffusion orientations. Therefore, an accurate classification of diffusion profiles is of great interest. However, the differentiation between multiple fiber orientations and isotropic diffusion is still a challenging task. In this work, we introduce ISMI, an index which successfully differentiates isotropic diffusion and single and multiple fiber populations. The classifier is based on the orientation distribution function (ODF) resulting from Q-ball imaging. We compare our results with the well-known general fractional anisotropy (GFA) index using a fiber phantom comprising challenging diffusion profiles such as crossing, fanning and kissing fiber configurations and a human brain dataset considering the centrum semiovale. Additionally, we visualize the results directly on the fibers represented by streamtubes using a heat color map.
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