ArticleJournal of applied clinical medical physics2025
UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.Journal of applied clinical medical physics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeThis study aims to develop a CycleGAN based denoising model to enhance the quality of low-dose PET (LDPET) images, making them as close as possible to standard-dose PET (SDPET) images.
methodsUsing a Philips Vereos PET/CT system, whole-body PET images of fluorine-18 fluorodeoxyglucose (18F-FDG) were acquired from 37 patients to facilitate the development of the UR-CycleGAN model. In this model, low-dose data were simulated by reconstructing PET images with a 30-s acquisition time, while standard-dose data were reconstructed from a 2.5-min acquisition. The network was trained in a supervised manner on 13 210 pairs of PET images, and the quality of the images was objectively evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
resultsCompared to simulated low-dose data, the denoised PET images generated by our model showed significant improvement, with a clear trend toward SDPET image quality.
conclusionThe 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.
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