The influence of artificial intelligence (AI) within the field of nuclear medicine has been rapidly growing. Many researchers and clinicians are seeking to apply AI within PET, and clinicians will soon find themselves engaging with AI-based applications all along the chain of molecular imaging, from image reconstruction to enhanced reporting. This expanding presence of AI in PET imaging will result in greater demand for educational resources for those unfamiliar with AI. The objective of this article to is provide an illustrated guide to the core principles of modern AI, with specific focus on aspects that are most likely to be encountered in PET imaging. We describe convolutional neural networks, algorithm training, and explain the components of the commonly used U-Net for segmentation and image synthesis.

翻译：人工智能（AI）在核医学领域的影响力正迅速增长。众多研究人员和临床医生正致力于将AI应用于PET成像，而临床医生很快将发现自身在分子影像学全链条中——从图像重建到增强报告——频繁接触基于AI的应用。AI在PET成像中的持续渗透导致对AI不熟悉者亟需更多教育资源。本文旨在提供一份图解指南，阐述现代AI的核心原理，并重点关注PET成像中最可能涉及的方面。我们描述了卷积神经网络、算法训练，并解析了常用于分割和图像合成的U-Net架构的组成部分。