论文标题
快速MR成像的深度学习：从不完整的K空间数据中学习重建的评论
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
论文作者
论文摘要
磁共振成像是一种强大的成像方式，可以提供多功能信息，但它具有瓶颈问题“缓慢的成像速度”。减少扫描测量可以借助强大的重建方法加速MR成像，这些方法已从线性分析模型演变为非线性迭代迭代。该领域的新兴趋势是用从数据中学到的来代替人类定义的信号模型。具体来说，从2016年开始，深度学习已纳入快速MR成像任务中，该任务从大数据集中吸引了有价值的先验知识，以促进有限测量的准确MR图像重建。这项调查旨在回顾2016年至2020年6月的基于深度学习的MR图像重建工作，并将讨论与此类方法相关的优点，局限性和挑战。最后但并非最不重要的一点是，本文将为有兴趣为这一领域做出贡献的研究人员指出良好的教程资源，最先进的开源代码和有意义的数据源来为这一领域提供一个起点。
Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed". Reducing the scanned measurements can accelerate MR imaging with the aid of powerful reconstruction methods, which have evolved from linear analytic models to nonlinear iterative ones. The emerging trend in this area is replacing human-defined signal models with that learned from data. Specifically, from 2016, deep learning has been incorporated into the fast MR imaging task, which draws valuable prior knowledge from big datasets to facilitate accurate MR image reconstruction from limited measurements. This survey aims to review deep learning based MR image reconstruction works from 2016- June 2020 and will discuss merits, limitations and challenges associated with such methods. Last but not least, this paper will provide a starting point for researchers interested in contributing to this field by pointing out good tutorial resources, state-of-the-art open-source codes and meaningful data sources.
