Mai T. Le1, Sathish Ramani1, Jeffrey A. Fessler1
1EECS, University of Michigan, Ann Arbor, MI, United States
SENSE reconstruction for parallel MRI with random undersampling requires spatial regularization for improved image quality. Compressed sensing methods utilize sparsity promoting regularizers that demand computation intensive, non-linear optimization algorithms. Previous variable splitting based algorithms ignored prior information that patients are not rectangular. We formulate a regularized SENSE reconstruction that explicitly includes a support constraint in the problem formulation. We propose a specific variable splitting strategy that when combined with the augmented Lagrangian framework and alternating minimization yields an algorithm with simple, efficient, non-iterative update steps. Experiments with in-vivo data demonstrate the improved performance of this method.