Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, UK.
Department of Computing, Imperial College London, London, UK.
Magn Reson Med. 2021 Dec;86(6):3274-3291. doi: 10.1002/mrm.28917. Epub 2021 Jul 13.
To introduce a novel deep learning-based approach for fast and high-quality dynamic multicoil MR reconstruction by learning a complementary time-frequency domain network that exploits spatiotemporal correlations simultaneously from complementary domains.
Dynamic parallel MR image reconstruction is formulated as a multivariable minimization problem, where the data are regularized in combined temporal Fourier and spatial (x-f) domain as well as in spatiotemporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatiotemporal redundancies in complementary domains.
Experiments were performed on two datasets of highly undersampled multicoil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalize well to data acquired from a different scanner and data with pathologies that were not seen in the training set.
The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multicoil data ( and yielding 15 s and 10 s scan times respectively) with fast reconstruction speed (2.8 seconds). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging.
通过学习一个互补的时频域网络，从互补域中同时利用时空相关性，介绍一种新的基于深度学习的快速、高质量的动态多线圈磁共振重建方法。
动态并行磁共振图像重建被表述为一个多变量最小化问题，其中数据在联合时频域以及时空（x-f）域和时空图像（x-t）域中进行正则化。推导出了一种基于变量分裂技术的迭代算法，该算法在 x-f 和 x-t 空间中交替进行信号去混叠步骤、闭式逐点数据一致性步骤和加权耦合步骤。迭代模型被嵌入到一个深度递归神经网络中，该网络通过利用互补域中的时空冗余来学习恢复图像。
在两个高度欠采样的多线圈短轴心脏电影 MRI 扫描数据集上进行了实验。结果表明，我们提出的方法在定量和定性方面都优于当前的最先进方法。该模型还可以很好地推广到来自不同扫描仪的数据和在训练集中未见过的病变数据。
这项工作表明，在互补的时频域中使用深度神经网络重建动态并行 MRI 的益处。该方法可以有效地、稳健地从高度欠采样的动态多线圈数据（ 和 ，分别产生 15 秒和 10 秒的扫描时间）中重建高质量的图像，重建速度快（2.8 秒）。这可能有助于实现快速单次屏气的临床 2D 心脏电影成像。
