title: Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging.
abstract: Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved.
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China; The Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing 100048, China.
authors.forename: Shanshan
authors.identifier: 0000-0002-0575-6523
authors.initials: S
authors.lastname: Wang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China; The Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing 100048, China.
authors.forename: Jianbo
authors.identifier: 0000-0002-5953-044X
authors.initials: J
authors.lastname: Liu
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China; The Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing 100048, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Peng
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Pei
authors.initials: P
authors.lastname: Dong
authors.affiliation: Nanchang University, Nanchang, Jiangxi, China.
authors.forename: Qiegen
authors.identifier: 0000-0003-4717-2283
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China; The Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing 100048, China.
authors.forename: Dong
authors.identifier: 0000-0001-6257-0875
authors.initials: D
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D044962:Data Compression; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio; D013997:Time Factors