Wang Zi, Qian Chen, Guo Di, Sun Hongwei, Li Rushuai, Zhao Bo, Qu Xiaobo
IEEE Trans Med Imaging. 2023 Jan;42(1):79-90. doi: 10.1109/TMI.2022.3203312. Epub 2022 Dec 29.
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI.
深度学习在加速磁共振成像（MRI）中展现出了惊人的性能。大多数最先进的深度学习重建方法采用强大的卷积神经网络并执行二维卷积，因为许多磁共振图像或其相应的k空间是二维的。在这项工作中，我们提出了一种探索一维卷积的新方法，使得深度网络更易于训练和泛化。我们进一步将一维卷积集成到所提出的深度网络中，命名为一维深度低秩稀疏网络（ODLS），它展开了低秩和稀疏重建模型的迭代过程。在体内膝盖和脑部数据集上的大量结果表明，所提出的ODLS非常适用于训练对象有限的情况，并且在视觉和定量方面都比最先进的方法提供了更好的重建性能。此外，ODLS对不同的欠采样场景以及训练和测试数据之间的一些不匹配也表现出良好的鲁棒性。总之，我们的工作表明一维深度学习方案在快速MRI中具有内存效率高和鲁棒性强的特点。
