Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic MRI reconstruction
Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic MRI reconstruction
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DOI：
10.1016/j.mri.2014.08.031
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发表时间：
2015-01-01
影响因子：
2.5
通讯作者：
Majumdar, Angshul
中科院分区：
文献类型：
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作者：
Majumdar, Angshul
在盲压缩感知（BCS）中，稀疏化字典和稀疏系数在信号恢复过程中同时估计。最近的一项研究采用BCS框架从欠采样的K空间测量中恢复动态MRI序列;结果很有希望。先前在动态MRI重建中的工作表明，可以通过将低秩惩罚纳入标准压缩感知（CS）优化框架来提高恢复精度。我们的工作是出于这些研究，我们改进的基本BCS框架，将低秩处罚的优化问题。由此产生的优化问题还没有得到解决之前，因此，我们推导出一个分裂Bregman型技术来解决同样的问题。实验在真实的动态增强MRI序列上进行。结果表明，我们提出的改进，重建精度优于BCS和其他国家的最先进的动态MRI恢复算法。(C)2014爱思唯尔公司All rights reserved.
In blind compressed sensing (BCS), both the sparsifying dictionary and the sparse coefficients are estimated simultaneously during signal recovery. A recent study adopted the BCS framework for recovering dynamic MRI sequences from under-sampled K-space measurements; the results were promising. Previous works in dynamic MRI reconstruction showed that recovery accuracy can be improved by incorporating low-rank penalties into the standard compressed sensing (CS) optimization framework. Our work is motivated by these studies, and we improve upon the basic BCS framework by incorporating low-rank penalties into the optimization problem. The resulting optimization problem has not been solved before; hence we derive a Split Bregman type technique to solve the same. Experiments were carried out on real dynamic contrast enhanced MRI sequences. Results show that, with our proposed improvement, the reconstruction accuracy is better than BCS and other state-of-the-art dynamic MRI recovery algorithms. (C) 2014 Elsevier Inc. All rights reserved.