@Article{IJNAM-17-3,
author = {Wu, Tingting and Zhi-Feng, Pang and Youguo, Wang and Yu-Fei, Yang},
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 = {<p style="text-align: justify;">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.</p>},
issn = {2617-8710},
doi = {https://doi.org/2020-IJNAM-16861},
url = {https://global-sci.com/article/83021/cs-mri-reconstruction-based-on-the-constrained-tgv-shearlet-scheme}
}