Greedy NNLS: Fiber Orientation Distribution From Non-Negatively Constrained Sparse Recovery

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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