title: Reference-guided sparsifying transform design for compressive sensing MRI.
abstract: Compressive sensing (CS) MRI aims to accurately reconstruct images from undersampled k-space data. Most CS methods employ analytical sparsifying transforms such as total-variation and wavelets to model the unknown image and constrain the solution space during reconstruction. Recently, nonparametric dictionary-based methods for CS-MRI reconstruction have shown significant improvements over the classical methods. These existing techniques focus on learning the representation basis for the unknown image for a synthesis-based reconstruction. In this paper, we present a new framework for analysis-based reconstruction, where the sparsifying transform is learnt from a reference image to capture the anatomical structure of unknown image, and is used to guide the reconstruction process. We demonstrate with experimental data the high performance of the proposed approach over traditional methods.
authors.affiliation: Department of Electrical andComputer Engineering, Beckman Institutefor Advanced Science and Technology and the University of Illinois at Urbana-Champaign, Urbana, IL, USA. dbabacan@illinois.edu
authors.forename: S Derin
authors.initials: SD
authors.lastname: Babacan
authors.forename: Xi
authors.initials: X
authors.lastname: Peng
authors.forename: Xian-Pei
authors.initials: XP
authors.lastname: Wang
authors.forename: Minh N
authors.initials: MN
authors.lastname: Do
authors.forename: Zhi-Pei
authors.initials: ZP
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique