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Global consistency spatial model for fiber orientation distribution estimation

In this study, we propose a globally consistent, locally sparse regularized model for fiber orientation distribution (FOD) estimation with multi-shell diffusion signal. First, a novel spherical double-lobe basis function is used to form an over-complete dictionary that guarantees the local sparsity of FOD. Furthermore, a global consistency spatial model which incorporated a spatial priori information based on the Bayesian framework, is developed using the coefficients of the basis function. Results tested using synthetic data and real human brain data show that the reconstructed results of our method are significantly better than that of multi-shell constraint spherical deconvolution (MSCSD) [1] and spatial high angular resolution diffusion imaging (spatial HARDI) [2].