An Efficient Variable Splitting Based Algorithm for Regularized SENSE Reconstruction with Support Constraint

Mai T. Le¹, Sathish Ramani¹, Jeffrey A. Fessler¹

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.