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Deep-learning-based ring artifact correction for tomographic reconstruction
X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161896/ https://www.ncbi.nlm.nih.gov/pubmed/36897392 http://dx.doi.org/10.1107/S1600577523000917 |
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author | Fu, Tianyu Wang, Yan Zhang, Kai Zhang, Jin Wang, Shanfeng Huang, Wanxia Wang, Yaling Yao, Chunxia Zhou, Chenpeng Yuan, Qingxi |
author_facet | Fu, Tianyu Wang, Yan Zhang, Kai Zhang, Jin Wang, Shanfeng Huang, Wanxia Wang, Yaling Yao, Chunxia Zhou, Chenpeng Yuan, Qingxi |
author_sort | Fu, Tianyu |
collection | PubMed |
description | X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost. |
format | Online Article Text |
id | pubmed-10161896 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-101618962023-05-06 Deep-learning-based ring artifact correction for tomographic reconstruction Fu, Tianyu Wang, Yan Zhang, Kai Zhang, Jin Wang, Shanfeng Huang, Wanxia Wang, Yaling Yao, Chunxia Zhou, Chenpeng Yuan, Qingxi J Synchrotron Radiat Research Papers X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost. International Union of Crystallography 2023-03-10 /pmc/articles/PMC10161896/ /pubmed/36897392 http://dx.doi.org/10.1107/S1600577523000917 Text en © Tianyu Fu et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Fu, Tianyu Wang, Yan Zhang, Kai Zhang, Jin Wang, Shanfeng Huang, Wanxia Wang, Yaling Yao, Chunxia Zhou, Chenpeng Yuan, Qingxi Deep-learning-based ring artifact correction for tomographic reconstruction |
title | Deep-learning-based ring artifact correction for tomographic reconstruction |
title_full | Deep-learning-based ring artifact correction for tomographic reconstruction |
title_fullStr | Deep-learning-based ring artifact correction for tomographic reconstruction |
title_full_unstemmed | Deep-learning-based ring artifact correction for tomographic reconstruction |
title_short | Deep-learning-based ring artifact correction for tomographic reconstruction |
title_sort | deep-learning-based ring artifact correction for tomographic reconstruction |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161896/ https://www.ncbi.nlm.nih.gov/pubmed/36897392 http://dx.doi.org/10.1107/S1600577523000917 |
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