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. 2021 Nov 1;11:21361. doi: 10.1038/s41598-021-00898-z

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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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CT image preprocessing pipeline for GAN training. The HU values of input CT images (a) were clipped to the range $[- 100, 400]$ HU and normalized to the unit range [0, 1] (b). To generate the low resolution CT image counterpart, the image was perturbed by noise addition (c) and Gaussian blurring (d), downsampled by a factor of $2 \times$ (e) and then upsampled to the original dimension (f) using a nearest neighbor interpolation method. Finally, the HRCT patch and LRCT patch were extracted from the lesion bounding box crops (g).