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. 2020 Nov 5;10:19144. doi: 10.1038/s41598-020-76126-x

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© The Author(s) 2020

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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Comparison of the monoexponential T_1ρ mapping error (MNAD) using (a) only the noiseless synthetic dataset, (b) only the noisy synthetic dataset, (c) all the training datasets, and (d) all the testing datasets. Representative monoexponential T_1ρ maps of the noisy synthetic case, using AF = 6, are shown in (e)–(g), and the fully-sampled reference in (h), and their error (NAD) against the ground truth maps are shown in (i)–(l).