Abstract #2056

Flow-based White Matter Supervoxel Parcellation using Functional Bregman Divergence between Orientation Distribution Functions

Teng Zhang¹, Kai Liu¹, Lin Shi^2,3, and Defeng Wang^4,5

¹Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ²Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ³Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ⁴Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ⁵Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China, People's Republic of

We propose a flow-based supervoxel parcellation method to split white matter into supervoxels with homogeneous diffusion property. In particular, we defined a new similarity metric between orientation distribution functions derived from q-ball imaging according to functional Bregman divergence. The proposed method was applied to high quality data from Human Connectome Project. Our work demonstrated a methodological feasibility to generate supervoxel approach tractography, construction of WM connectivity network, etc.

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