Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI
Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI
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DOI：
10.1002/mrm.24524
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发表时间：
2013-09-01
影响因子：
3.3
通讯作者：
Romberg, Justin
中科院分区：
文献类型：
--
作者：
Asif, M. Salman;Hamilton, Lei;Romberg, Justin
加速磁共振成像技术通过欠采样k空间来减少信号采集时间。加速磁共振成像中的一个基本问题是从欠采样的k空间数据中恢复高质量的图像。当前最先进的恢复算法利用底层图像中的空间和时间结构来提高重建质量。近年来，压缩感知理论帮助制定了数学原理和条件，确保从欠采样，不相干的测量中恢复（结构化）稀疏信号。在这篇文章中，提出了一种新的恢复算法，运动自适应时空正则化，利用压缩感知框架中的MR图像的空间和时间结构稀疏性，从高度欠采样的k空间数据中恢复动态MR图像。与现有的算法相比，我们提出的算法模型的时间稀疏使用运动自适应线性变换相邻图像之间。运动自适应时空正则化的效率证明了心脏磁共振成像的一系列减少因素的实验。结果也与k-t FOTORS与运动估计和补偿最近提出的另一种动态磁共振成像的恢复算法进行了比较。Magn Reson Med 70：800-812，2013。(c)2012 Wiley Periodicals，Inc.
Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensationanother recently proposed recovery algorithm for dynamic magnetic resonance imaging. Magn Reson Med 70:800-812, 2013. (c) 2012 Wiley Periodicals, Inc.