An accurate and efficient MR image reconstruction model

The clear magnetic resonance (MR) images play an important role in clinical medicine and diagnosis. However, its application in clinical diagnosis may be limited by the long acquisition and transformation time of MR images. To deal with this disadvantage, compressed sensing methods have been widely used in real reconstructing MR images. This paper presents an accurate and efficient compressed sensing model based on median filter for MR image reconstruction. By combining a total variation term, a median filter term which are presented in the L1 norm formulation and a data fitting term together, we propose a minimization problem for image reconstruction. The L1 norm formulation guarantees that the split Bregman method can be applied to efficiently minimize the energy functional in both the total variation term and the median filter term. We apply our model to lots of MR images to test its performance and compare it with a related method. Experimental results show the accuracy and efficiency of the proposed model.