Robust SENSE Reconstruction Using Non-Local Regularization

Sheng Fang¹, Li Zhao¹, Xinlu Xu¹, Kui Ying¹, Jiangping Cheng¹

A new regularization technique named non-local regularization is proposed for robust SENSE reconstruction. Unlike current regularization methods, the proposed method does not rely on any specific image model or prior image acquisition. It utilizes the information redundancy of an image and maximizes the consistence and similarity of pixel values within the image. The regularizing functional can automatically adapt to different local structures within an image. The phantom simulation and MR experiments results demonstrate that this method can effectively suppress noises in SENSE reconstruction with well-preserved image details. The image quality is better than the popular Total Variation regularization.