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. 2021 Apr 22;11:8727. doi: 10.1038/s41598-021-88158-y

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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/.

PMC Copyright notice

Two proposed implementations for the D-UNet architecture are shown. (A) A non-uniformly sampled L-COSY experiment is reconstructed into the fully sampled spectrum. While under-sampling can be performed in both the $t_{2}$ and $t_{1}$ dimensions, this study analyzes the reconstruction of L-COSY spectra acquired using non-uniform sampling along only the $t_{1}$ dimension. (B) Several metabolite spectra are identified from a fully sampled L-COSY spectrum using a D-UNet model. The intensities of the metabolite spectra directly correlate to concentration values, and therefore this study also investigates the potential application of deep learning to quantify L-COSY spectra. In total, 17 metabolites were quantified in this simulation study.