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. 2018 Feb 7;8:2575. doi: 10.1038/s41598-018-19426-7

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© The Author(s) 2018

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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The simulation data to evaluate the CNN algorithm performance: (a) the synthetic phantom for the simulation, the empty space, the cubic and the particles have the gray values of 0, 0.3 and 1 respectively. The particle diameters are 4 to 20 voxels; (b) one of the original high-quality projections from the synthetic phantom, P(0); (c) the projection with 5% artificial noise, P_n05. Result: (d) the enhanced projection by the CNN algorithm, P_e05(0) showing the background noise perfectly removed and structural information in good agreement with (b).