24 February 2012 ISMI: a classification index for high angular resolution diffusion imaging
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Abstract
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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D. Röttger, D. Röttger, D. Dudai, D. Dudai, D. Merhof, D. Merhof, S. Müller, S. Müller, "ISMI: a classification index for high angular resolution diffusion imaging", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144E (24 February 2012); doi: 10.1117/12.911277; https://doi.org/10.1117/12.911277
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