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. 2022 Mar 21;49(9):3098–3118. doi: 10.1007/s00259-022-05746-4

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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. 3 — Images from Mehranian et al. comparing reference thirty minute maximum a posteriori expectation maximisation reconstruction with a 2-min reconstruction with FBSEM-Net using PET and MR inputs (FBSEM-pm), standard data consistency-based reconstructions (MAPEM) and deep learning-based post-processing with PET and MR inputs (Unet-pm) (images from [68])