正电子发射断层扫描/计算机断层扫描（PET/CT）越来越多地应用于肿瘤学、神经病学、心脏病学和新兴医学领域。其成功源于混合 PET/CT 成像提供的综合信息，超越了单独用于不同恶性肿瘤时单独使用的能力。然而，手动图像判读需要广泛的疾病特定知识，并且是医生日常工作中耗时的一个方面。深度学习算法类似于训练期间的从业者，从图像中提取知识，通过检测症状和增强图像来促进诊断过程。所获得的知识有助于通过症状检测和图像增强来支持诊断过程。现有的 PET/CT 成像评论论文有一个缺点，因为它们要么包含额外的模式，要么检查各种类型的人工智能应用。然而，缺乏专门针对人工智能和深度学习在 PET/CT 图像上的高度具体应用的全面调查。本综述旨在通过调查采用深度学习进行 PET/CT 成像的论文中所用方法的特征来填补这一空白。在审查中，我们确定了 2017 年至 2022 年间发表的 99 项研究，这些研究将深度学习应用于 PET/CT 图像。我们还确定了 PET/CT 的最佳预处理算法和最有效的深度学习模型，同时强调了当前的局限性。我们的综述强调了深度学习 (DL) 在 PET/CT 成像中的潜力，在正弦图和图像空间中的病灶检测、肿瘤分割和疾病分类中取得了成功的应用。还讨论了常见和特定的预处理技术。深度学习算法擅长提取有意义的特征，并提高诊断的准确性和效率。然而，注释数据集的稀缺以及可解释性和不确定性方面的挑战带来了局限性。最近的深度学习模型，例如基于注意力的模型、生成模型、多模态模型、图卷积网络和变压器，有望改善 PET/CT 研究。此外，放射组学在肿瘤分类和预测患者预后方面也引起了人们的关注。在这个快速发展的领域中，持续的研究对于探索新应用和提高深度学习模型的准确性至关重要。版权所有 © 2023。由 Elsevier B.V. 出版。

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.Copyright © 2023. Published by Elsevier B.V.