Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deep-learning-based image reconstruction methods for MRI. Two types of deep-learningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

中文翻译：

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深度磁共振图像重建：逆问题满足神经网络

来自欠采样 k 空间数据的图像重建在快速磁共振成像 (MRI) 中发挥着重要作用。最近，深度学习在各个领域都取得了巨大的成功，并且在以更少的测量显着加速 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，一种是基于展开算法的方法，另一种是基于非展开算法的方法，并解释了两者的主要结构。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，从理论的角度来看，这可能有助于网络的进一步发展和性能分析。