Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks.
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks.
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DOI：
10.1109/msp.2019.2950557
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发表时间：
2020-01
影响因子：
14.9
通讯作者：
Ying L
中科院分区：
文献类型：
--
作者：
Liang D;Cheng J;Ke Z;Ying L
欠采样k空间数据的图像重建在快速MRI中起着重要的作用。最近，深度学习在各个领域都取得了巨大的成功，并且在减少测量量的情况下显着加速MRI重建方面也显示出潜力。本文综述了基于深度学习的MRI图像重建方法。本文回顾了两种基于深度学习的方法：基于展开算法的方法和不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中最大化深层重建潜力的几个信号处理问题。本文的讨论有助于网络的进一步发展和从理论角度分析绩效。
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.