Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, People's Republic of China.
Phys Med Biol. 2020 Dec 2;65(23):235041. doi: 10.1088/1361-6560/abaffa.
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersampled k-space to the fully sampled images. Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied. In this paper, we propose an unsupervised deep learning method for multi-coil cine MRI via a time-interleaved sampling strategy. Specifically, a time-interleaved acquisition scheme is utilized to build a set of fully encoded reference data by directly merging the k-space data of adjacent time frames. Then these fully encoded data can be used to train a parallel network for reconstructing images of each coil separately. Finally, the images from each coil are combined via a CNN to implicitly explore the correlations between coils. The comparisons with classic k-t FOCUSS, k-t SLR, L+S and KLR methods on in vivo datasets show that our method can achieve improved reconstruction results in an extremely short amount of time.
深度学习在心脏磁共振成像（MRI）重建中取得了很好的效果，其中卷积神经网络（CNNs）学习从欠采样 k 空间到完全采样图像的映射。虽然这些深度学习方法可以提高重建质量，与不需要复杂参数选择或冗长重建时间的迭代方法相比，但仍需要解决以下问题：1）所有这些方法都基于大数据，需要大量的完全采样 MRI 数据，这对于心脏 MRI 来说通常很难获得；2）在动态 MRI 成像的深度学习方法中，线圈相关性对重建的影响从未得到研究。在本文中，我们提出了一种基于时间交错采样策略的多通道电影 MRI 的无监督深度学习方法。具体来说，利用时间交错采集方案，通过直接合并相邻时间帧的 k 空间数据，构建一组完全编码的参考数据。然后，这些完全编码的数据可用于训练并行网络，分别重建每个线圈的图像。最后，通过 CNN 将每个线圈的图像进行组合，以隐式探索线圈之间的相关性。与经典的 k-t FOCUSS、k-t SLR、L+S 和 KLR 方法在体内数据集上的比较表明，我们的方法可以在极短的时间内实现更好的重建结果。
