BLIND COMPRESSED SENSING WITH SPARSE DICTIONARIES FOR ACCELERATED DYNAMIC MRI.
BLIND COMPRESSED SENSING WITH SPARSE DICTIONARIES FOR ACCELERATED DYNAMIC MRI.
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用于加速动态 MRI 的稀疏字典盲压缩感知。
DOI：
10.1109/isbi.2013.6556398
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Jacob,Mathews
中科院分区：
文献类型：
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作者：
Lingala,SajanGoud;Jacob,Mathews
介绍了几种将体素时间序列建模为固定字典中基函数的稀疏线性组合的算法，以从欠采样的傅立叶测量中恢复动态MRI数据。我们最近已经证明，字典基和k空间数据的稀疏系数的联合估计可以改善重建。在这篇文章中，我们研究了附加先验在学习基函数上的使用。具体地说，我们假设基函数在预先指定的变换或算符域中是稀疏的。我们的实验表明，这种约束能够抑制噪声基函数，从而进一步提高重建的质量。通过不同的重构实例，验证了该方法的有效性。
Several algorithms that model the voxel time series as a sparse linear combination of basis functions in a fixed dictionary were introduced to recover dynamic MRI data from under sampled Fourier measurements. We have recently demonstrated that the joint estimation of dictionary basis and the sparse coefficients from the k-space data results in improved reconstructions. In this paper, we investigate the use of additional priors on the learned basis functions. Specifically, we assume the basis functions to be sparse in pre-specified transform or operator domains. Our experiments show that this constraint enables the suppression of noisy basis functions, thus further improving the quality of the reconstructions. We demonstrate the usefulness of the proposed method through various reconstruction examples.