Research progress on cross-modality generation of CT and PET images using generative adversarial networks
10.3760/cma.j.cn321828-20250212-00034
- VernacularTitle:生成对抗网络在CT与PET影像跨模态生成中的研究进展
- Author:
Xiaonan SHAO
1
;
Rong NIU
;
Jianxiong GAO
;
Xinyu GE
;
Yuetao WANG
;
Jun ZHOU
Author Information
1. 苏州大学附属第三医院、常州市第一人民医院核医学科，苏州大学核医学与分子影像临床转化研究所，常州市分子影像重点实验室，常州 213003
- Publication Type:Journal Article
- Keywords:
Algorithms;
Positron-emission tomography;
Tomography, X-ray computed;
Generative adversarial networks;
Trends
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2025;45(12):765-768
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.
