UR‐cycleGAN: Denoising full‐body low‐dose PET images using cycle‐consistent Generative Adversarial Networks
The proposed method reduces acquisition time by 80% compared to standard-dose imaging, while achieving image quality close to SDPET images. It also enhances visual detail fidelity, demonstrating the feasibility and practical utility of the model for significantly reducing imaging time while maintaining high image quality.
Authors
- Zhengxing Sun (ORCID: https://orcid.org/0000-0001-7137-6169)
- Hengjiang Liu
- Liu Yang (ORCID: https://orcid.org/0000-0003-1358-5282)
Institutions
- Zhengzhou University of Light Industry (CN)
- Fifth Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1002/acm2.70124
- Citations
- 1
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Henan Provincial Science and Technology Research Project