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Volume 17, Issue 3
CS-MRI Reconstruction Based on the Constrained TGV-Shearlet Scheme

Tingting Wu, Zhi-Feng Pang, Youguo Wang & Yu-Fei Yang

Int. J. Numer. Anal. Mod., 17 (2020), pp. 316-331.

Published online: 2020-05

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  • Abstract

This paper proposes a new constrained total generalized variation (TGV)-shearlet model to the compressive sensing magnetic resonance imaging (MRI) reconstruction via the simple parameter estimation scheme. Due to the non-smooth term included in the proposed model, we employ the alternating direction method of multipliers (ADMM) scheme to split the original problem into some easily solvable subproblems in order to use the convenient soft thresholding operator and the fast Fourier transformation (FFT). Since the proposed numerical algorithm belongs to the framework of the classic ADMM, the convergence can be kept. Experimental results demonstrate that the proposed method outperforms the state-of-the-art unconstrained reconstruction methods in removing artifacts and achieves lower reconstruction errors on the tested dataset.

  • Keywords

Magnetic resonance imaging, total generalized variation, shearlet transformation, alternating direction method of multipliers (ADMM), compressive sensing.

  • AMS Subject Headings

90C25, 49M27, 68U10, 94A08

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

wutt@njupt.edu.cn (Tingting Wu)

zhifengpang@163.com (Zhi-Feng Pang)

wyg@njupt.edu.cn (Youguo Wang)

yangyufei_hd@sina.com (Yu-Fei Yang)

  • BibTex
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  • TXT
@Article{IJNAM-17-316, author = {Wu , TingtingPang , Zhi-FengWang , Youguo and Yang , Yu-Fei}, title = {CS-MRI Reconstruction Based on the Constrained TGV-Shearlet Scheme}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2020}, volume = {17}, number = {3}, pages = {316--331}, abstract = {

This paper proposes a new constrained total generalized variation (TGV)-shearlet model to the compressive sensing magnetic resonance imaging (MRI) reconstruction via the simple parameter estimation scheme. Due to the non-smooth term included in the proposed model, we employ the alternating direction method of multipliers (ADMM) scheme to split the original problem into some easily solvable subproblems in order to use the convenient soft thresholding operator and the fast Fourier transformation (FFT). Since the proposed numerical algorithm belongs to the framework of the classic ADMM, the convergence can be kept. Experimental results demonstrate that the proposed method outperforms the state-of-the-art unconstrained reconstruction methods in removing artifacts and achieves lower reconstruction errors on the tested dataset.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/16861.html} }
TY - JOUR T1 - CS-MRI Reconstruction Based on the Constrained TGV-Shearlet Scheme AU - Wu , Tingting AU - Pang , Zhi-Feng AU - Wang , Youguo AU - Yang , Yu-Fei JO - International Journal of Numerical Analysis and Modeling VL - 3 SP - 316 EP - 331 PY - 2020 DA - 2020/05 SN - 17 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/16861.html KW - Magnetic resonance imaging, total generalized variation, shearlet transformation, alternating direction method of multipliers (ADMM), compressive sensing. AB -

This paper proposes a new constrained total generalized variation (TGV)-shearlet model to the compressive sensing magnetic resonance imaging (MRI) reconstruction via the simple parameter estimation scheme. Due to the non-smooth term included in the proposed model, we employ the alternating direction method of multipliers (ADMM) scheme to split the original problem into some easily solvable subproblems in order to use the convenient soft thresholding operator and the fast Fourier transformation (FFT). Since the proposed numerical algorithm belongs to the framework of the classic ADMM, the convergence can be kept. Experimental results demonstrate that the proposed method outperforms the state-of-the-art unconstrained reconstruction methods in removing artifacts and achieves lower reconstruction errors on the tested dataset.

Wu , TingtingPang , Zhi-FengWang , Youguo and Yang , Yu-Fei. (2020). CS-MRI Reconstruction Based on the Constrained TGV-Shearlet Scheme. International Journal of Numerical Analysis and Modeling. 17 (3). 316-331. doi:
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