Magnetic resonance image reconstruction based on nonlocal augmented Lagrangian multiplier method

Total variation (TV) is unable to recover the fine details and textures of magnetic resonance(MR) images since it often suffers from staircase artifact. To reduce these drawbacks, an improved TV MR image recovery algorithm is introduced by using nonlocal regularization into the CS optimization problem. The nonlocal regularization is built on nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. On account of the complexity in implementing NLM filter, a modified MR imaging method called nonlocal Lagrange multiplier (MRNLM) is proposed to overcome the above shortcomings while boosting MR image quality. Experimental results demonstrate that the proposed algorithm shows significant improvements on the state-of-the-art TV based algorithms in both SNR and visual perception, as well as a fair balance between time and quality.