ArticleQuantitative imaging in medicine and surgery2024
Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.EJNMMI physics · 2026Article
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9 authors.
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Abstract
Background: Deep-learning-based denoising improves image quality and quantification accuracy for low count (LC) positron emission tomography (PET). Conventional deep-learning-based denoising methods only require single LC PET image input. This study aims to propose a deep-learning-based LC PET denoising method incorporating computed tomography (CT) priors to further reduce the dose level. Methods: Fifty patients who underwent their routine whole-body 2-deoxy-2-[ Results: Mean square error and structural similarity index were the best for cGAN-2, followed by U-Net-2, cGAN-1 and U-Net-1. The errors of mean standardized uptake value (SUV) and maximum SUV were lowest for cGAN-2, followed by cGAN-1, U-Net-2 and U-Net-1. For cGAN-2, image quality and lesion detectability scores were 3.71±0.94 and 4.25±0.83 for LC-10, 3.57±0.79 and 3.61±1.21 for LC-20, while they were 3.49±0.92 and 4.42±0.08 for FC. Notably, some small lesions were "masked out" on cGAN/U-Net-1 but can be retrieved on cGAN/U-Net-2 denoised PET for LC-20. Conclusions: Deep-learning-based LC PET denoising incorporating CT priors is more effective than conventional deep-learning-based denoising with single LC PET input, especially at lower dose levels.
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