中文摘要：

在这份报纸，一个二水平的 Bregman 方法与图被介绍调整稀少的编码为高度 undersampled 磁性的回声图象重建。图调整了稀少的编码与在内部水平在外部水平和更改字典和稀少的表示强制取样的数据限制的二水平的 Bregman 反复的过程被合并。图调整了稀少的编码并且简单字典更新在内部最小化适用让建议算法与重复的一个相对小的数字收敛。试验性的结果证明建议算法罐头一致地重建两个都高效地模仿了先生图象和真正的先生数据，并且以视觉比较和量的措施超过当前的最先进的途径。

英文摘要：

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 up-dates 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.