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 - <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>