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Diffusion-weighted imaging (DWI) facilitates probing neural tissue structure non-invasively by measuring its hindrance to water diffusion. Analysis of DWI is typically based on generative signal models for given tissue geometry and microstructural properties. In this work, we generalize multi-tissue spherical deconvolution to a blind source separation problem under convexity and nonnegativity constraints. This spherical factorization approach decomposes multi-shell DWI data, represented in thedoi:10.1016/j.neuroimage.2016.10.040 pmid:27989845 pmcid:PMC5543413 fatcat:4uae2epuwve5dc44c7gklo26zi