title: Adaptive sampling design for compressed sensing MRI.
abstract: Compressed Sensing (CS) takes advantage of the sparsity of MR images in certain bases or dictionaries to obtain accurate reconstructions from undersampled k-space data. The (pseudo) random sampling schemes used most often for CS may have good theoretical asymptotic properties; however, with limited data they may be far from optimal. In this paper, we propose a novel framework for improved adaptive sampling schemes for highly undersampled CS MRI. While the proposed framework is general, we apply it with a recently proposed MRI reconstruction algorithm employing adaptive image-patch based sparsifying dictionaries. Numerical experiments demonstrate up to 7 dB improvements in reconstruction PSNR using the adapted sampling scheme, on top of the large improvements reported in our previous work for the adaptive patch-based reconstruction scheme over analytical sparsifying transforms.
authors.affiliation: Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, IL 61801, USA. ravisha3@illinois.edu
authors.forename: Saiprasad
authors.initials: S
authors.lastname: Ravishankar
authors.forename: Yoram
authors.initials: Y
authors.lastname: Bresler
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D060388:Support Vector Machine