Estimation of multiple fiber orientations using nonconvex regularized spherical deconvolution

In diffusion magnetic resonance imaging, the fiber tractography generally desires the estimation of intravoxel multiple fiber orientations (MFOs) with high accuracy and reliability. In general, spherical deconvolution (SD) based methods have many advantages for MFOs estimation. However, these methods are lowly immune to noise. To cope with this problem, regularization techniques were introduced in SD-based methods to reduce noise artifacts. But, the regularizers were often defined as a convex function to make the model resolving simpler, which limits their effect of regularization. In this work, we introduce a nonconvex regularizer in the Richardson-Lucy based SD framework for estimating MFOs. The results on synthetic phantom and physical phantom images demonstrate that the proposed method is superior to existing SD-based methods in terms of mean angular errors, edge preservation and computation time.