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

Abstract 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 ima...

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Autores principales:	Allard A. Hendriksen, Minna Bührer, Laura Leone, Marco Merlini, Nicola Vigano, Daniël M. Pelt, Federica Marone, Marco di Michiel, K. Joost Batenburg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fa2acf2d66bb4960a173a69d96a7235f
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spelling	oai:doaj.org-article:fa2acf2d66bb4960a173a69d96a7235f2021-12-02T15:02:40ZDeep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data10.1038/s41598-021-91084-82045-2322https://doaj.org/article/fa2acf2d66bb4960a173a69d96a7235f2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91084-8https://doaj.org/toc/2045-2322Abstract 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.Allard A. HendriksenMinna BührerLaura LeoneMarco MerliniNicola ViganoDaniël M. PeltFederica MaroneMarco di MichielK. Joost BatenburgNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Allard A. Hendriksen Minna Bührer Laura Leone Marco Merlini Nicola Vigano Daniël M. Pelt Federica Marone Marco di Michiel K. Joost Batenburg Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
description	Abstract 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.
format	article
author	Allard A. Hendriksen Minna Bührer Laura Leone Marco Merlini Nicola Vigano Daniël M. Pelt Federica Marone Marco di Michiel K. Joost Batenburg
author_facet	Allard A. Hendriksen Minna Bührer Laura Leone Marco Merlini Nicola Vigano Daniël M. Pelt Federica Marone Marco di Michiel K. Joost Batenburg
author_sort	Allard A. Hendriksen
title	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_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_sort	deep denoising for multi-dimensional synchrotron x-ray tomography without high-quality reference data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fa2acf2d66bb4960a173a69d96a7235f
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Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data

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