Journal
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Volume 53, Issue -, Pages 1-8Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2016.06.004
Keywords
Global regularization; Image reconstruction; Magnetic resonance; Sparsity; Transform learning
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Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. (C) 2016 Elsevier Ltd. All rights reserved.
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