Compressed Sensing MRI Reconstruction with Multiple Sparsity Constraints on Radial Sampling
Joint Authors
Wang, Lihui
Huang, Jianping
Zhu, Yuemin
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique for accelerating MRI acquisitions by using fewer k-space data.
Exploiting more sparsity is an important approach to improving the CS-MRI reconstruction quality.
We propose a novel CS-MRI framework based on multiple sparse priors to increase reconstruction accuracy.
The wavelet sparsity, wavelet tree structured sparsity, and nonlocal total variation (NLTV) regularizations were integrated in the CS-MRI framework, and the optimization problem was solved using a fast composite splitting algorithm (FCSA).
The proposed method was evaluated on different types of MR images with different radial sampling schemes and different sampling ratios and compared with the state-of-the-art CS-MRI reconstruction methods in terms of peak signal-to-noise ratio (PSNR), feature similarity (FSIM), relative l2 norm error (RLNE), and mean structural similarity (MSSIM).
The results demonstrated that the proposed method outperforms the traditional CS-MRI algorithms in both visual and quantitative comparisons.
American Psychological Association (APA)
Huang, Jianping& Wang, Lihui& Zhu, Yuemin. 2019. Compressed Sensing MRI Reconstruction with Multiple Sparsity Constraints on Radial Sampling. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1195233
Modern Language Association (MLA)
Huang, Jianping…[et al.]. Compressed Sensing MRI Reconstruction with Multiple Sparsity Constraints on Radial Sampling. Mathematical Problems in Engineering No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1195233
American Medical Association (AMA)
Huang, Jianping& Wang, Lihui& Zhu, Yuemin. Compressed Sensing MRI Reconstruction with Multiple Sparsity Constraints on Radial Sampling. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1195233
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195233