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Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Conv...

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Autores principales: Hendriksen, Allard A., Bührer, Minna, Leone, Laura, Merlini, Marco, Vigano, Nicola, Pelt, Daniël M., Marone, Federica, di Michiel, Marco, Batenburg, K. Joost
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178391/
https://www.ncbi.nlm.nih.gov/pubmed/34088936
http://dx.doi.org/10.1038/s41598-021-91084-8
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author Hendriksen, Allard A.
Bührer, Minna
Leone, Laura
Merlini, Marco
Vigano, Nicola
Pelt, Daniël M.
Marone, Federica
di Michiel, Marco
Batenburg, K. Joost
author_facet Hendriksen, Allard A.
Bührer, Minna
Leone, Laura
Merlini, Marco
Vigano, Nicola
Pelt, Daniël M.
Marone, Federica
di Michiel, Marco
Batenburg, K. Joost
author_sort Hendriksen, Allard A.
collection PubMed
description Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.
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spelling pubmed-81783912021-06-08 Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data Hendriksen, Allard A. Bührer, Minna Leone, Laura Merlini, Marco Vigano, Nicola Pelt, Daniël M. Marone, Federica di Michiel, Marco Batenburg, K. Joost Sci Rep Article Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality. Nature Publishing Group UK 2021-06-04 /pmc/articles/PMC8178391/ /pubmed/34088936 http://dx.doi.org/10.1038/s41598-021-91084-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hendriksen, Allard A.
Bührer, Minna
Leone, Laura
Merlini, Marco
Vigano, Nicola
Pelt, Daniël M.
Marone, Federica
di Michiel, Marco
Batenburg, K. Joost
Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title_full Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title_fullStr Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title_full_unstemmed Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title_short Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
title_sort deep denoising for multi-dimensional synchrotron x-ray tomography without high-quality reference data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178391/
https://www.ncbi.nlm.nih.gov/pubmed/34088936
http://dx.doi.org/10.1038/s41598-021-91084-8
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