Regularized super-resolution for diffusion MRI

In this paper, we present a new regularized super-resolution method for finding accurate fibre orientations and volume fractions of fibre populations on a sub-voxel scale from a 3D diffusion MRI acquisition in order to distinguish between various fibre configurations such as fanning and bending, and ameliorate partial volume effects. We treat this task as a general inverse problem, which we solve by regularization and optimization, and demonstrate the method on human brain data.