Robust compressed sensing MRI based on combined nonconvex regularization

Abstract

Compressed sensing (CS) is well known method for solving fast reconstruction of magnetic resonance (MR) images. However, the quality of the reconstructed images is sensitive to the noise. The reconstructed results often suffer from blurry or jaggy artifacts, which seriously affects the analysis of the medical image. To achieve robustness reconstruction result, in this paper, the combined nonconvex regularization model is proposed to enhance the details recovery and reduce jaggy artifacts. In the proposed framework, the bias-based sharpness enhancement prior is integrated into smoothed L0 (SL0) model, which can make SL0 reconstruction method more robust to noise. To solve the nonconvex optimization problem, a two-cycle iterative algorithm is developed to solve it. In addition, the improved Stein Unbiased Risk Estimator (SURE) is developed to automatically tune the shape parameter that to be effective for a variety of noise levels. The experimental results on MR images consistently demonstrate the excellent performance of the proposed method in both the objective quality metrics and visual inspections.