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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