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Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging

Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medi...

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Detalles Bibliográficos
Autores principales: Hu, Jianhua, Zheng, Shuzhao, Wang, Bo, Luo, Guixiang, Huang, WoQing, Zhang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754833/
https://www.ncbi.nlm.nih.gov/pubmed/36531651
http://dx.doi.org/10.1155/2022/4431536
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author Hu, Jianhua
Zheng, Shuzhao
Wang, Bo
Luo, Guixiang
Huang, WoQing
Zhang, Jun
author_facet Hu, Jianhua
Zheng, Shuzhao
Wang, Bo
Luo, Guixiang
Huang, WoQing
Zhang, Jun
author_sort Hu, Jianhua
collection PubMed
description Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.
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spelling pubmed-97548332022-12-16 Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging Hu, Jianhua Zheng, Shuzhao Wang, Bo Luo, Guixiang Huang, WoQing Zhang, Jun Biomed Res Int Research Article Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction. Hindawi 2022-12-08 /pmc/articles/PMC9754833/ /pubmed/36531651 http://dx.doi.org/10.1155/2022/4431536 Text en Copyright © 2022 Jianhua Hu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hu, Jianhua
Zheng, Shuzhao
Wang, Bo
Luo, Guixiang
Huang, WoQing
Zhang, Jun
Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title_full Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title_fullStr Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title_full_unstemmed Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title_short Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
title_sort super-resolution swin transformer and attention network for medical ct imaging
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754833/
https://www.ncbi.nlm.nih.gov/pubmed/36531651
http://dx.doi.org/10.1155/2022/4431536
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