Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Joint Authors
Zhao, Di
Du, Huiqian
Han, Yu
Mei, Wenbo
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI.
The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain.
Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate.
In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains.
The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations.
In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV).
Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency.
Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively.
American Psychological Association (APA)
Zhao, Di& Du, Huiqian& Han, Yu& Mei, Wenbo. 2014. Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016845
Modern Language Association (MLA)
Zhao, Di…[et al.]. Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1016845
American Medical Association (AMA)
Zhao, Di& Du, Huiqian& Han, Yu& Mei, Wenbo. Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016845
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016845