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Abstract #2538

Automatic Regularization Parameter Selection for Iterative Nonlinear MRI Reconstruction

Sathish Ramani1, Jon-Fredrik Nielsen2, Jeffrey A. Fessler1

1EECS, University of Michigan, Ann Arbor, MI, United States; 2fMRI Laboratory, University of Michigan, Ann Arbor, MI, United States

MRI reconstruction from undersampled sampled k-space data requires regularization to reduce artifacts. Nonquadratic regularization (e.g., l1 or TV) can be used to restore image quality, but its successful application depends on proper selection of the regularization parameter. In this work, we demonstrate the applicability of generalized cross validation (GCV) and an estimate of a mean-squared error (MSE)-type measure for quantitative selection of the regularization parameter for MRI reconstruction using the iterative split-Bregman algorithm with nonquadratic regularization. GCV and the MSE-type estimate require the Jacobian matrix of the reconstruction with respect to the data that we evaluate analytically for the SB algorithm. We illustrate with experiments on real MR (phantom) data that GCV and the MSE-type estimate lead to near-optimal reconstruction results.