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Abstract #2827

Multi-Kernel Estimation of Fiber Orientation Distribution Functions With L0-Norm Induced Group Sparsity

Pew-Thian Yap 1 , Yong Zhang 2 , and Dinggang Shen 1

1 Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, United States, 2 Department of Psychiatry & Behavioral Sciences, Stanford University, California, United States

An inherent limitation of Spherical deconvolution (SD) in estimating the fiber orientation distribution function (FODF) is that the fiber kernel is assumed to be spatially invariant. This has been shown to result in spurious FODF peaks. This abstract describes a multi-kernel approach for robust estimation of the fiber orientation distribution function. We show that instead of restricting ourselves to one kernel per compartment, it is possible to employ a group of kernels per compartment to cater to possible data variation across voxels. Our results demonstrate that the proposed method significantly improves microstructural and tract estimates.

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