Liang Dong, Cheng Jing, Ke Ziwen, Ying Leslie
Paul C. Lauterbur Research Center for Biomedical Imaging.
Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, in Shenzhen, Guangdong, China.
IEEE Signal Process Mag. 2020 Jan;37(1):141-151. doi: 10.1109/MSP.2019.2950557. Epub 2020 Jan 20.
Image reconstruction from undersampled k-space data has been playing an important role in fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of the deep learning-based image reconstruction methods for MRI. Two types of deep learning-based approaches are reviewed: those based on unrolled algorithms and those which are not. The main structure of both approaches are explained, respectively. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed. The discussion may facilitate further development of the networks and the analysis of performance from a theoretical point of view.
从欠采样k空间数据进行图像重建在快速磁共振成像（MRI）中一直发挥着重要作用。近年来，深度学习在各个领域都取得了巨大成功，并且在以更少测量次数显著加速MRI重建方面也显示出潜力。本文概述了基于深度学习的MRI图像重建方法。回顾了两种基于深度学习的方法：基于展开算法的方法和非基于展开算法的方法。分别解释了这两种方法的主要结构。讨论了在快速MRI中最大化深度重建潜力的几个信号处理问题。该讨论可能有助于网络的进一步发展以及从理论角度对性能进行分析。
