Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece.
Laboratory of Nuclear Medicine, University Hospital of Patras, Rio, Greece.
Eur J Nucl Med Mol Imaging. 2022 Sep;49(11):3717-3739. doi: 10.1007/s00259-022-05805-w. Epub 2022 Apr 22.
This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years.
The present work extensively describes GAN architectures and their applications in PET imaging. The identification of relevant publications was performed via approved publication indexing websites and repositories. Web of Science, Scopus, and Google Scholar were the major sources of information.
The research identified a hundred articles that address PET imaging applications such as attenuation correction, de-noising, scatter correction, removal of artefacts, image fusion, high-dose image estimation, super-resolution, segmentation, and cross-modality synthesis. These applications are presented and accompanied by the corresponding research works.
GANs are rapidly employed in PET imaging tasks. However, specific limitations must be eliminated to reach their full potential and gain the medical community's trust in everyday clinical practice.
本文回顾了生成对抗网络（GAN）在正电子发射断层扫描（PET）成像中的最新应用。深度学习（DL）和 GAN 的最新进展促进了它们在医学成像模式中的应用研究。因此，在过去两年中，已经出现了几种独特的 GAN 拓扑结构，并在实验环境中进行了评估。
本工作广泛描述了 GAN 架构及其在 PET 成像中的应用。通过已批准的出版物索引网站和存储库进行相关出版物的识别。Web of Science、Scopus 和 Google Scholar 是主要的信息来源。
研究确定了一百篇文章，涉及 PET 成像应用，如衰减校正、去噪、散射校正、去除伪影、图像融合、高剂量图像估计、超分辨率、分割和跨模态合成。这些应用程序被提出，并附有相应的研究工作。
GAN 正在被迅速应用于 PET 成像任务中。然而，要充分发挥其潜力并获得医学领域在日常临床实践中的信任，还必须消除特定的局限性。
