Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging
Joint Authors
Liang, Dong
Liu, Qiegen
Peng, Xi
Liu, Jianbo
Wang, Shanshan
Dong, Pei
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-25
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Shanshan& Liu, Jianbo& Peng, Xi& Dong, Pei& Liu, Qiegen& Liang, Dong. 2016. Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging. BioMed Research International،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1097177
Modern Language Association (MLA)
Wang, Shanshan…[et al.]. Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging. BioMed Research International No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1097177
American Medical Association (AMA)
Wang, Shanshan& Liu, Jianbo& Peng, Xi& Dong, Pei& Liu, Qiegen& Liang, Dong. Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1097177
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097177