利用结构稀疏表示加速动态心脏磁共振成像。
Accelerating Dynamic Cardiac MR imaging using structured sparse representation.
机构信息
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China ; Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China ; Shenzhen Key Laboratory for MRI, Guangdong, Shenzhen 518055, China.
出版信息
Comput Math Methods Med. 2013;2013:160139. doi: 10.1155/2013/160139. Epub 2013 Dec 18.
Compressed sensing (CS) has produced promising results on dynamic cardiac MR imaging by exploiting the sparsity in image series. In this paper, we propose a new method to improve the CS reconstruction for dynamic cardiac MRI based on the theory of structured sparse representation. The proposed method user the PCA subdictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. An accelerated iterative shrinkage algorithm is used to solve the optimization problem and achieve a fast convergence rate. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method.
压缩感知（CS）通过利用图像序列的稀疏性，在动态心脏磁共振成像方面取得了有前景的结果。在本文中，我们提出了一种新的方法，基于结构稀疏表示理论，来提高基于 CS 的动态心脏 MRI 重建。该方法使用 PCA 子字典进行自适应稀疏表示，并抑制稀疏编码噪声，以获得良好的重建。采用加速迭代收缩算法来求解优化问题，实现快速收敛速度。实验结果表明，与最先进的 CS 方法相比，该方法提高了动态心脏电影 MRI 的重建质量。
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