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. 2024 Apr 30:arXiv:2405.00241v1.
Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, 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 CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI.
磁共振成像（MRI）彻底改变了医学成像，能够对人体进行非侵入性且高度详细的观察。然而，MRI较长的采集时间带来了挑战，导致患者不适、运动伪影，并限制了实时应用。为应对这些挑战，研究人员正在探索各种技术以减少采集时间并提高MRI的整体效率。一种这样的技术是压缩感知（CS），它通过利用变换空间中的图像稀疏性来减少数据采集。近年来，深度学习（DL）已与CS-MRI相结合，形成了一个取得显著发展的新框架。基于DL的CS-MRI方法在不影响图像质量的情况下加速MR成像方面已被证明非常有效。本综述全面研究了基于DL的CS-MRI技术，重点关注它们在提高MR成像速度方面的作用。我们对基于DL的CS-MRI的每一类进行了详细分析，包括端到端、展开优化、自监督和联邦学习。我们的系统综述突出了重大贡献，并强调了DL在CS-MRI中的令人兴奋的潜力。此外，我们的系统综述有效地总结了基于DL的CS-MRI的关键结果和趋势，包括定量指标、使用的数据集、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CS-MRI在医学成像进步中的重要性。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CS-MRI出版物和公开可用的数据集 - https://github.com/mosaf/Awesome-DL-based-CS-MRI。
