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. 2024 Feb 6;17(1):24–46. doi: 10.1007/s12194-024-00780-3

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© The Author(s) 2024, corrected publication 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — Examples of denoised ¹⁸F-FDG PET images by emerging approaches. Each column (a) to (d) indicates different patients or organs. From left to right, the sample images show the standard-count PET, low-count PET, and denoised PET images, corresponding to the enhanced deep super-resolution network (EDSR), EDSR-ViT, GAN, U-Net, and Swin Transformer image restoration network (SwinIR).

© 2023 SNCSC. Reprinted with permission from Wang et al. [159]