Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with thousands of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.
翻译:在广泛应用的深层学习取得成功之后,神经网络的机器学习技术在加速磁共振成像(MRI)的获取和重建战略方面受到极大兴趣,在计算机视觉和图像处理的深层学习技术启发下,一些想法已经成功地应用到非线性图像重建中,本着压缩感应的精神,加速MRI。鉴于该领域迅速增长的性质,必须合并和总结文献中所报告的大量深层学习方法,以便更好地了解整个领域。本文章概述了专门为改进平行成像而提出的基于神经网络的方法的最新发展情况。从基于K空间的古典重建方法中也提供了一种一般背景和对平行MRI的介绍。采用改良调控器的图像领域技术与以K-空间为基础的方法一起得到覆盖,这些方法侧重于使用神经网络进行更好的内插战略。虽然实地正在迅速发展,每年发表数千篇论文,但在本审查中,我们试图涵盖显示在公开数据集上良好表现的广泛方法类别。对限制和开放问题进行了讨论,最近还审查了为社区制定数据基准而进行的努力。