Hu Xueping, Zhang Han, Caobelli Federico, Huang Yan, Li Yuchen, Zhang Jiajia, Shi Kuangyu, Yu Fei
Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Institute of Nuclear Medicine, Tongji University School of Medicine, Shanghai, China.
iScience. 2024 Nov 12;27(12):111374. doi: 10.1016/j.isci.2024.111374. eCollection 2024 Dec 20.
The development of state-of-the-art algorithms for computer visualization has led to a growing interest in applying deep learning (DL) techniques to the field of medical imaging. DL-based algorithms have been extensively utilized in various aspects of cardiovascular imaging, and one notable area of focus is single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), which is regarded as the gold standard for non-invasive diagnosis of myocardial ischemia. However, due to the complex decision-making process of DL based on convolutional neural networks (CNNs), the explainability of DL results has become a significant area of research, particularly in the field of medical imaging. To better harness the potential of DL and to be well prepared for the ongoing DL revolution in nuclear imaging, this review aims to summarize the recent applications of DL in MPI, with a specific emphasis on the methods in explainable DL for the diagnosis and prognosis of MPI. Furthermore, the challenges and potential directions for future research are also discussed.
用于计算机可视化的先进算法的发展，引发了人们对将深度学习（DL）技术应用于医学成像领域的兴趣日益浓厚。基于DL的算法已在心血管成像的各个方面得到广泛应用，一个值得关注的显著领域是单光子发射计算机断层扫描（SPECT）心肌灌注成像（MPI），它被视为心肌缺血无创诊断的金标准。然而，由于基于卷积神经网络（CNN）的DL决策过程复杂，DL结果的可解释性已成为一个重要的研究领域，特别是在医学成像领域。为了更好地利用DL的潜力，并为核成像中正在进行的DL革命做好充分准备，本综述旨在总结DL在MPI中的最新应用，特别强调用于MPI诊断和预后的可解释DL方法。此外，还讨论了未来研究的挑战和潜在方向。
