title: MRI reconstruction with joint global regularization and transform learning.
abstract: 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.
authors: Department of Electrical and Electronics Engineering, Kirklareli University, Kayali, 39100 Kirklareli, Turkey. Electronic address: korhan.tanc@kirklareli.edu.tr.
authors: A Korhan
authors: AK
authors: Tanc
authors: Department of Electronics and Communications Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey. Electronic address: eksioglue@itu.edu.tr.
authors: Ender M
authors: EM
authors: Eksioglu
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging
keywords: Global regularization; Image reconstruction; Magnetic resonance; Sparsity; Transform learning