Antonio Tristn-Vega1, Carl-Fredrik Westin1
1Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Boston, MA, United States
Compressed Sensing grants the possibility of reducing the number of samples required to describe a signal below its Nyquist rate. When applied to the estimation of the diffusion propagator in diffusion spectrum imaging, it arises the need to find a basis for which the propagator is sparse, which is not trivial. We address this problem by two means: 1) mapping the propagator to a space where it is sparse, and 2) using a model that explicitly isolates a non-sparse residual. We provide two reconstruction algorithms for such model, notably improving the estimation accuracy and sparsity compared to previous approaches.