Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR.
Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR.
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DOI：
10.1109/tmi.2010.2100850
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发表时间：
2011-05
影响因子：
10.6
通讯作者：
Jacob M
中科院分区：
文献类型：
--
作者：
Lingala SG;Hu Y;DiBella E;Jacob M
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We introduce a novel algorithm to reconstruct dynamic MRI data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-dependent KL transform makes our approach ideally suited to a range of dynamic imaging problems, even when the motion is not periodic. In comparison to current KLT-based methods that rely on a two-step approach to first estimate the basis functions and then use it for reconstruction, we pose the problem as a spectrally regularized matrix recovery problem. By simultaneously determining the temporal basis functions and its spatial weights from the entire measured data, the proposed scheme is capable of providing high quality reconstructions at a range of accelerations. In addition to using the compact representation in the KLT domain, we also exploit the sparsity of the data to further improve the recovery rate. Validations using numerical phantoms and in-vivo cardiac perfusion MRI data demonstrate the significant improvement in performance offered by the proposed scheme over existing methods.