Safari Mojtaba, Eidex Zach, Chang Chih-Wei, Qiu Richard L J, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.
ArXiv. 2025 Feb 1:arXiv:2501.14158v2.
Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.
磁共振成像（MRI）是一种非侵入性成像方式，可提供有关人体的全面解剖和功能信息。然而，其较长的采集时间会导致患者不适、运动伪影，并限制实时应用。为应对这些挑战，已应用了诸如并行成像等策略，该策略利用多个接收线圈来加速数据采集过程。此外，压缩感知（CS）是一种有助于从稀疏数据进行图像重建的方法，通过最小化所需的数据采集量显著减少图像采集时间。最近，深度学习（DL）已成为改善MRI重建的强大工具。它已与并行成像和CS原理相结合，以实现更快、更准确的MRI重建。本综述全面研究了基于DL的MRI重建技术。我们对各种基于DL的方法进行分类和讨论，包括端到端方法、展开优化和联邦学习，突出它们的潜在优势。我们的系统综述突出了重大贡献，并强调了DL在MRI重建中的潜力。此外，我们总结了基于DL的MRI重建的关键结果和趋势，包括定量指标、数据集、加速因子以及DL技术随时间的进展和研究兴趣。最后，我们讨论了潜在的未来方向以及基于DL的MRI重建在推进医学成像方面的重要性。为便于该领域的进一步研究，我们提供了一个GitHub仓库，其中包括最新的基于DL的MRI重建出版物和公共数据集——https://github.com/mosaf/Awesome-DL-based-CS-MRI。
