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. 2021 Dec 11;8:81. doi: 10.1186/s40658-021-00426-y

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© The Author(s) 2021

Open AccessThis 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. 12 — Illustration of the deep image prior training procedure for dynamic PET denoising. A static image is used as the input $z$ to a network f, initialized with random weights $θ$ . The network parameters are then iteratively optimized to produce the dynamic image x. After a certain number of iterations, denoised versions of the dynamic PET images are obtained as output. Image from [83]