A Model-Free Unsupervised Method to Cluster Brain Tissue Directly From DWI Volumes

We present a simple, novel approach to the voxelwise classification of brain tissue acquired with diffusion-weighted imaging (DWI). By working directly upon the individual DWI volume data, it makes no assumption of an underlying diffusion model. In addition, by summarising statistics across the diffusion gradient directions, we obtain features that are rotationally invariant. We show an example of how well a resulting cluster spatially matches a high FA region, thereby corresponding to probable single-tract voxels. The method could have application during tractography pre-processing, and has potential as a complementary approach for analysis of DWI datasets.

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