Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low-Rank Model
Joint Authors
Chen, Lixia
Yang, Bin
Wang, Xuewen
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The quality of dynamic magnetic resonance imaging reconstruction has heavy impact on clinical diagnosis.
In this paper, we propose a new reconstructive algorithm based on the L+S model.
In the algorithm, the l1 norm is substituted by the lp norm to approximate the l0 norm; thus the accuracy of the solution is improved.
We apply an alternate iteration method to solve the resulting problem of the proposed method.
Experiments on nine data sets show that the proposed algorithm can effectively reconstruct dynamic magnetic resonance images.
American Psychological Association (APA)
Chen, Lixia& Yang, Bin& Wang, Xuewen. 2017. Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low-Rank Model. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1192734
Modern Language Association (MLA)
Chen, Lixia…[et al.]. Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low-Rank Model. Mathematical Problems in Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1192734
American Medical Association (AMA)
Chen, Lixia& Yang, Bin& Wang, Xuewen. Dynamic Magnetic Resonance Imaging Reconstruction Based on Nonconvex Low-Rank Model. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1192734
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192734