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. 2022 Dec 15;12:21679. doi: 10.1038/s41598-022-22778-w

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© The Author(s) 2022

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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Two examples showing the “plug-and-play” modular nature of PnP-ADMM reconstruction algorithm. Reconstructions from 3 different denoisers, namely ACVA, BM3D and BM4D with GBM. The location of the tumor is indicated by the arrow in the magnitude image. The non-local patch-based denoisers perform well at preserving features such as bright vessels and fine textures, especially in the grey matter cortical regions.