Fast Non-Convex Statistical Compressed Sensing MRI Reconstruction Based on Approximated Lp(0

Il Yong Chun¹, Thomas Talavage¹, ²

¹School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States; ²Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States

we propose a fast constrained L(p,)-L2-norm (L(p,) is an approximated Lp-qusi-norm) minimization algorithm, based on 1) p- and -dependent weighting techniques, and 2) an efficient split Bregman-based (known to have rapid convergence, especially with an L1-norm ) reweighted L1-minimization algorithm. This L(p,)-L2-norm minimization achieves exact reconstruction from fewer measurements than are required for the L1-L2-norm case.

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