Regularized SENSE reconstruction using iteratively refined total variation method

SENSE has been widely accepted as one of the standard reconstruction algorithms for Parallel MRI. When large acceleration factors are employed, the SENSE reconstruction becomes very ill-conditioned. For Cartesian SENSE, Tikhonov regularization has been commonly used. However, the Tikhonov regularized image usually tends to be overly smooth, and a high-quality regularization image is desirable to alleviate this problem but is not available. In this paper, we propose a new SENSE regularization technique that is based on total variation with iterated refinement using Bregman iteration. It penalizes highly oscillatory noise but allows sharp edges in reconstruction without the need for prior information. In addition, the Bregman iteration refines the image details iteratively. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction.