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. 2021 Apr 15;11:8248. doi: 10.1038/s41598-021-87482-7

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© The Author(s) 2021

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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Image metric analysis on the 3D human brain dataset. (a–d) Image metric analysis of AUTOMAP and IFFT reconstruction with- and without the DnCNN step or the BM3D denoiser following transformation of the raw k-space data—The mean overall SNR in the whole-head ROI across all the 15 slices is shown in (a) for IFFT (filled circle), denoised IFFT with BM3D (filled square) with DnCNN (filled triangle), AUTOMAP (open circle). Three additional metrics are computed: PSNR (b), RMSE (c), and SSIM (d). (e) The table summarizes the mean PSNR, SSIM, RMSE, SNR and SNR gain values across all the slices. The SNR gain was calculated with respect to the conventional IFFT.