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Two-level Bregman method for MRI reconstruction with graph regularized sparse coding

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Abstract

In this paper, a two-level Bregman method is presented with graph regularized sparse coding for highly undersampled magnetic resonance image reconstruction. The graph regularized sparse coding is incorporated with the two-level Bregman iterative procedure which enforces the sampled data constraints in the outer level and updates dictionary and sparse representation in the inner level. Graph regularized sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge with a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can consistently reconstruct both simulated MR images and real MR data efficiently, and outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.

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Authors and Affiliations

  1. Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China

    Qiegen Liu  (刘且根), Hongyang Lu  (卢红阳) & Minghui Zhang  (张明辉)

Authors
  1. Qiegen Liu  (刘且根)
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  2. Hongyang Lu  (卢红阳)
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  3. Minghui Zhang  (张明辉)
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Corresponding author

Correspondence to Qiegen Liu  (刘且根).

Additional information

Supported by the National Natural Science Foundation of China(No. 61261010, No. 61362001, No. 61365013, No. 61262084, No. 51165033), Technology Foundation of Department of Education in Jiangxi Province(GJJ13061, GJJ14196), Young Scientists Training Plan of Jiangxi Province (No. 20133ACB21007, No. 20142BCB23001), National Post-Doctoral Research Fund(No. 2014M551867)and Jiangxi Advanced Project for Post-Doctoral Research Fund(No. 2014KY02).

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Liu, Q., Lu, H. & Zhang, M. Two-level Bregman method for MRI reconstruction with graph regularized sparse coding. Trans. Tianjin Univ. 22, 24–34 (2016). https://doi.org/10.1007/s12209-016-2581-4

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  • DOI: https://doi.org/10.1007/s12209-016-2581-4

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