正电子发射断层扫描/计算机断层扫描(PET/CT)越来越多地用于肿瘤学,神经学,心脏病学,新兴的医疗领域。成功源于混合PET/CT成像提供的内聚信息,当单独用于不同的恶性肿瘤时,超越了个体模式的能力。然而,手动图像解释需要广泛的疾病特异性知识,这是一个耗时的方面的医生\'日常工作。深度学习算法,类似于培训期间的从业者,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。这种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。关于PET/CT成像的现有评论论文有一个缺点,因为它们包括额外的模式或检查了各种类型的AI应用。然而,一直缺乏专门针对人工智能高度特定用途的全面调查,和深度学习,在PET/CT图像上。这篇综述旨在通过调查使用深度学习进行PET/CT成像的论文中使用的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报告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。我们的评论强调了深度学习(DL)在PET/CT成像中的潜力,在病变检测中的成功应用,肿瘤分割,以及正弦图和图像空间中的疾病分类。还讨论了常见和特定的预处理技术。DL算法擅长提取有意义的特征,提高诊断的准确性和效率。然而,局限性是由于注释数据集的稀缺性以及可解释性和不确定性方面的挑战。最近的DL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,影像组学在肿瘤分类和预测患者预后方面引起了关注。在这个快速发展的领域中,不断进行的研究对于探索新的应用和提高DL模型的准确性至关重要。
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.