Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deeplearning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for both low-dose computed tomography and accelerated MRI. The additional integration of multicoil information to recover missing k-space lines in the MRI reconstruction process is studied less frequently, even though it is the de facto standard for the currently used accelerated MR acquisitions. This article provides an overview of the recent machine-learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given and structured around the classical view of image- and k-space-based methods. Linear and nonlinear methods are covered, followed by a discussion of the recent efforts to further improve parallel imaging using machine learning and, specifically, artificial neural networks. Image domain-based techniques that introduce improved regularizers are covered as well as k-space-based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed and recent efforts for producing open data sets and benchmarks for the community are examined.

中文翻译：

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并行磁共振成像重建的深度学习方法：当前方法、趋势和问题的调查

随着深度学习在广泛应用中取得成功，基于神经网络的机器学习技术作为加速磁共振成像 (MRI) 的手段而受到人们的关注。受计算机视觉和图像处理深度学习技术启发的许多想法已成功应用于低剂量计算机断层扫描和加速 MRI 压缩感知精神的非线性图像重建。尽管它是当前使用的加速 MR 采集的事实标准，但对多线圈信息的额外集成以恢复 MRI 重建过程中丢失的 k 空间线的研究较少。本文概述了最近专门为改进并行成像而提出的机器学习方法。围绕基于图像和 k 空间的方法的经典视图给出并构建了并行 MRI 的一般背景介绍。涵盖了线性和非线性方法，然后讨论了最近使用机器学习（特别是人工神经网络）进一步改进并行成像的努力。介绍了引入改进的正则化器的基于图像域的技术以及基于 k 空间的方法，其中重点是使用神经网络的更好的插值策略。讨论了问题和未解决的问题，并检查了最近为社区生成开放数据集和基准的努力。