Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images

► The spherical convolution model is reformulated by adopting a measure-theoretic approach. ► The fiber orientation density is represented by a Dirichlet process mixture, which may be used for realistic simulations. ► The inverse problem is solved by nonparametric Bayesian statistics under a Rician noise model. ► The density estimation framework is demonstrated with HARDI datasets.