Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data
Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data
复制标题
用于从高度欠采样磁共振数据进行图像重建的稀疏性正交字典更新
DOI：
10.1088/0031-9155/60/14/5359
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发表时间：
2015-06
影响因子：
3.5
通讯作者：
Feng Yanqiu
中科院分区：
文献类型：
--
作者：
Huang Jinhong;Guo Li;Feng Qianjin;Chen Wufan;Feng Yanqiu
欠采样k空间数据的图像重建通过利用某些变换域的图像稀疏性来加速磁共振成像（MRI）。在学习字典上使用图像补丁表示具有自适应局部图像结构的优点，因此比使用固定变换（例如小波和总变分）可以更好地稀疏图像。字典学习方法最近被引入到MRI重建中，与使用固定变换的稀疏MRI重建相比，这些方法显着降低了重建误差。然而，字典学习中的综合稀疏编码问题是np困难的，并且计算成本很高。在本文中，我们提出了一种新的提高稀疏度的正交字典更新方法，用于从高度欠采样的MRI数据中高效地重建图像。学习字典的正交性使得重构中的最小化问题可以通过一种高效的优化算法来解决，该算法交替更新表示系数、正交字典和缺失的k空间数据。此外，假设字典的质量逐步提高，在迭代过程中，使用更新字典的稀疏度级别和稀疏表示贡献都会逐渐增加，以恢复更多细节。仿真和实际数据实验结果均表明，该方法比基于k - svd的字典学习MRI方法快约10 ~ 100倍，同时提高了重建精度。
Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.
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影响因子：
1.6
作者：
Mairal, Julien;Sapiro, Guillermo;Elad, Michael
通讯作者：
Elad, Michael
影响因子：
3.3
作者：
Sodickson, DK;Manning, WJ
通讯作者：
Manning, WJ
压缩感知 MRI 的组合稀疏变换
影响因子：
1.1
作者：
Hu, C.;Chen, Z.;Qu, X.;Cao, X.;Guo, D.
通讯作者：
Guo, D.
DOI：
10.1017/cbo9780511794308
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Gitta Kutyniok
通讯作者：
Gitta Kutyniok
DOI：
10.1109/jstsp.2007.910971
发表时间：
2007-12-01
影响因子：
7.5
作者：
Kim, Seung-Jean;Koh, K.;Gorinevsky, Dimitry
通讯作者：
Gorinevsky, Dimitry