Rician noise and intensity nonuniformity correction (NNC) model for MRI data

Lu Liu, Huan Yang, Jiyun Fan, Ryan Wen Liu, Yuping Duan

Abstract: Highlights•We propose a novel image restoration model based on maximum a posteriori estimation for MRI data, which are corrupted by both Rician noise and intensity nonuniformity.•We develop an efficient optimization algorithm based on ADMM, where all subproblems can be solved by either Newton's method or closed-form solution.•We disclosure that the existence of intensity nonuniformity may lead to an underestimation of the noise, which illustrates the importance of simultaneously dealing with Rician noise and intensity nonuniformity.•Numerical experiments on both synthetic and real MR data confirm the robustness of the proposed method and its better performance for MR data restoration.