Efficient MR inhomogeneity correction by regularized entropy minimization and proximal alternations

Magnetic Resonance (MR) images usually exhibit intensity inhomogeneity (bias field) due to non-uniformity generated from RF coils or radiation-patient interactions. This inhomogeneity could lead to undesired perception and difficult diagnosis. In this work, we propose a nonparametric retrospective bias corrector by minimizing a regularized-entropy criterion through a proximal alternating scheme. Numerically, the proposed solver is particularly efficient, scalable and parallelizable compared to existing approaches. Additionally, the effectiveness and the robustness of the corrector have been validated by simulations and vast clinical tests.