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

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• 作者：Qiegen Liu 刘且枿/a> ; Hongyang Lu 卢红閿/a> ; Minghui Zhang 张明輿/a>
• 关键词：magnetic resonance imaging ; graph regularized sparse coding ; dictionary learning ; Bregman iterative method ; alternating direction method
• 刊名：Transactions of Tianjin University
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：22
• 期：1
• 页码：24-34
• 全文大小：1,489 KB
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• 作者单位：Qiegen Liu 刘且根 (1)
  Hongyang Lu 卢红阳 (1)
  Minghui Zhang 张明辉 (1)
  
  1. Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China
  
• 刊物类别：Engineering
• 刊物主题：Chinese Library of Science
  
• 出版者：Tianjin University
• ISSN：1995-8196

文摘

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.