TY - ABST

T1 - A model-free unsupervised method to cluster brain tissue directly From DWI volumes

AU - Liptrot, Matthew George

AU - Lauze, Francois Bernard

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

M3 - Conference abstract for conference

T2 - Joint Annual Meeting ISMRM-ESMRMB 2014

Y2 - 10 May 2014 through 16 May 2014

ER -