Mean apparent propagator (MAP) reconstructs the diffusion pdf using a dictionary based on Hermite functions. The first element corresponds to a tensor approximation; and the following elements add non-gaussian components. To improve non-gaussian accuracy, one needs to increase the size of the dictionary, but it also increases the number of q-space samples for a robust optimisation. We propose the use of compressed sensing to efficiently increase the number of atoms in the dictionary by exploiting its sparsity for a better reconstruction.
This abstract and the presentation materials are available to members only; a login is required.