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Weighted-average alternating minimization method for magnetic resonance image reconstruction based on compressive sensing

Yonggui Zhu Communication University of China Yuying Shi North China Electric Power University Bin Zhang Communication University of China Xinyan Yu Communication University of China

Numerical Analysis and Scientific Computing mathscidoc:1702.25076

Inverse Problems and Imaging, 8, (3), 925-937, 2014.8
The problem of compressive-sensing (CS) L2-L1-TV reconstruc- tion of magnetic resonance (MR) scans from undersampled k-space data has been addressed in numerous studies. However, the regularization parameters in models of CS L2-L1-TV reconstruction are rarely studied. Once the regu- larization parameters are given, the solution for an MR reconstruction model is xed and is less e ective in the case of strong noise. To overcome this shortcoming, we present a new alternating formulation to replace the standard L2-L1-TV reconstruction model. A weighted-average alternating minimization method is proposed based on this new formulation and a convergence analysis of the method is carried out. The advantages of and the motivation for the pro- posed alternating formulation are explained. Experimental results demonstrate that the proposed formulation yields better reconstruction results in the case of strong noise and can improve image reconstruction via exible parameter slection.
Compressive sensing; alternating minimization method; weighted average; magnetic resonance image reconstruction.
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@inproceedings{yonggui2014weighted-average,
  title={WEIGHTED-AVERAGE ALTERNATING MINIMIZATION METHOD FOR MAGNETIC RESONANCE IMAGE RECONSTRUCTION BASED ON COMPRESSIVE SENSING},
  author={Yonggui Zhu, Yuying Shi, Bin Zhang, and Xinyan Yu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210024050001814419},
  booktitle={Inverse Problems and Imaging},
  volume={8},
  number={3},
  pages={925-937},
  year={2014},
}
Yonggui Zhu, Yuying Shi, Bin Zhang, and Xinyan Yu. WEIGHTED-AVERAGE ALTERNATING MINIMIZATION METHOD FOR MAGNETIC RESONANCE IMAGE RECONSTRUCTION BASED ON COMPRESSIVE SENSING. 2014. Vol. 8. In Inverse Problems and Imaging. pp.925-937. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170210024050001814419.
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