A total variation based regularization strategy in Magnetic Resonance imaging

In this paper we present some variational functionals for the regularization of Magnetic Resonance (MR) images, usually corrupted by noise and artifacts. The mathematical problem has a Tikhonov-like formulation, where the regularization functional is a nonlinear variational functional. The problem is numerically solved as an optimization problem with a quasi-Newton algorithm. The algorithm has been applied to MR images corrupted by noise and to dynamic MR images corrupted by truncation artifacts due to limited resolution. The results on test problems obtained from simulated and real data are presented. The functionals actually reduce noise and artifacts, provided that a good regularizing parameter is used.