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A fast method for L1–L2 modeling for MR image compressive sensing

Yonggui Zhu Communication University of China Xiaoman Liu Communication University of China

Numerical Analysis and Scientific Computing mathscidoc:1702.25075

Journal of Inverse and Ill-posed Problems, 23, (3), 211-218, 2015.6
We use a positive parameter to develop a dierentiable perturbed reconstruction model to solve the L1–L2 magnetic resonance image (MRI) reconstruction problem. We use Bregman iterative formulation to solve the dierentiable perturbed L1–L2 model, and lagged diusivity xed-point iteration to solve the minimization problem in the Bregman iteration. Two Fourier transforms and an inverse Fourier transform are used to accelerate L1–L2 MRI reconstruction. Real MR images are used to test the method in numerical experiments. The results demonstrate that the proposed method is very ecient for L1–L2MRI reconstruction.
Compressive sensing, sparse transform, lagged diusivity xed-point iteration, Bregman iterative regularization
[ Download ] [ 2017-02-10 02:30:39 uploaded by ygzhu ] [ 948 downloads ] [ 0 comments ] 
@inproceedings{yonggui2015a,
  title={A fast method for L1–L2 modeling for MR image compressive sensing},
  author={Yonggui Zhu, and Xiaoman Liu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210023039911845418},
  booktitle={Journal of Inverse and Ill-posed Problems},
  volume={23},
  number={3},
  pages={211-218},
  year={2015},
}
Yonggui Zhu, and Xiaoman Liu. A fast method for L1–L2 modeling for MR image compressive sensing. 2015. Vol. 23. In Journal of Inverse and Ill-posed Problems. pp.211-218. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210023039911845418.
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