Dynamic MRI of the abdomen using parallel non-Cartesian convolutional recurrent neural networks
Dynamic MRI of the abdomen using parallel non-Cartesian convolutional recurrent neural networks
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DOI：
10.1002/mrm.28774
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发表时间：
2021-03-21
影响因子：
3.3
通讯作者：
Du, Yiping P.
中科院分区：
文献类型：
--
作者：
Zhang, Yufei;She, Huajun;Du, Yiping P.
目的：为提高深度学习方法重建欠采样非笛卡尔腹部动态并行MR数据的图像质量并减少计算时间，提出了一种并行非笛卡尔卷积递归神经网络（PNCRNNs）算法，利用时空域的冗余信息，实现非笛卡尔并行MR数据重建的数据保真度。PNCRNN的性能进行了评估，以各种加速度，运动模式和成像应用与最先进的动态成像算法，包括额外的多维黄金角径向稀疏并行MRI的比较（XD-GRASP）、低秩加稀疏矩阵分解（L+S）、盲压缩感知（BCS）和3D卷积神经网络（3D CNN）。在R = 16时，PNCRNN与XD-GRASP相比增加了9.07 dB的峰值SNR，与L+S相比增加了9.26 dB，与BCS相比增加了3.48 dB，与3D CNN相比增加了3.14 dB。每个面元的重建时间为18 ms，比XD-GRASP、L+S和BCS快两个数量级。PNCRNNs提供了良好的重建各种运动模式，k空间轨迹，和imaging application.Conclusion：建议PNCRNN提供了实质性的改善，与XD-GRASP，L+S，BCS，和3D CNN相比，腹部动态黄金角放射状成像的图像质量。PNCRNN的重建时间可以快到每秒50箱，由于使用了高计算效率的Toeplitz方法。
Purpose: To improve the image quality and reduce computational time for the reconstruction of undersampled non-Cartesian abdominal dynamic parallel MR data using the deep learning approach.Methods: An algorithm of parallel non-Cartesian convolutional recurrent neural networks (PNCRNNs) was developed to enable the use of the redundant information in both spatial and temporal domains, and achieve data fidelity for the reconstruction of non-Cartesian parallel MR data. The performance of PNCRNNs was evaluated for various acceleration rates, motion patterns, and imaging applications in comparison with that of the state-of-the-art algorithms of dynamic imaging, including extra-dimensional golden-angle radial sparse parallel MRI (XD-GRASP), low-rank plus sparse matrix decomposition (L+S), blind compressive sensing (BCS), and 3D convolutional neural networks (3D CNNs).Results: PNCRNNs increased the peak SNR of 9.07 dB compared with XD-GRASP, 9.26 dB compared with L+S, 3.48 dB compared with BCS, and 3.14 dB compared with 3D CNN at R = 16. The reconstruction time was 18 ms for each bin, which was two orders faster than that of XD-GRASP, L+S, and BCS. PNCRNNs provided good reconstruction for various motion patterns, k-space trajectories, and imaging applications.Conclusion: The proposed PNCRNN provides substantial improvement of the image quality for dynamic golden-angle radial imaging of the abdomen in comparison with XD-GRASP, L+S, BCS, and 3D CNN. The reconstruction time of PNCRNN can be as fast as 50 bins per second, due to the use of the highly computational efficient Toeplitz approach.