Abstract #2610
Greedy NNLS: Fiber Orientation Distribution From Non-Negatively Constrained Sparse Recovery
Aurobrata Ghosh 1 and Rachid Deriche 1
1
Project Team Athena, Inria Sophia Antipolis
Mditerrane, Sophia Antipolis, PACA, France
The Fiber Orientation Density (FOD) is a robust method
for mapping crossing WM fibers. However, in clinical
settings with minimalistic (~30) acquisitions, the FOD
is restricted to 4th-6th order SHs, which limits its
angular resolution. We proposed a non-negatively
constrained sparse recovery of the FOD based on
Non-Negative Least-Squares (NNLS) to overcome this
limitation. We found NNLS solutions to be constrained
and sparse. Here we experimentally compare the NNLS to
l1-minimization and find NNLS superior in sparsity &
robustness. We conclude by highlighting the greedy
design of NNLS, which mirrors Orthogonal Matching
Pursuit, and is the cause of its sparsity.
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