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A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images
BACKGROUND: Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of synthetic 4...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991563/ https://www.ncbi.nlm.nih.gov/pubmed/35392947 http://dx.doi.org/10.1186/s13014-022-02042-1 |
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author | Usui, Keisuke Ogawa, Koichi Goto, Masami Sakano, Yasuaki Kyougoku, Shinsuke Daida, Hiroyuki |
author_facet | Usui, Keisuke Ogawa, Koichi Goto, Masami Sakano, Yasuaki Kyougoku, Shinsuke Daida, Hiroyuki |
author_sort | Usui, Keisuke |
collection | PubMed |
description | BACKGROUND: Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of synthetic 4D-CBCT (sCT) images created by a cycle generative adversarial network (CycleGAN) for ART for lung cancer. METHODS: Unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images of 20 lung-cancer patients were used for training. High-quality sCT lung images generated by the CycleGAN model were tested on another 10 cases. The mean and mean absolute errors were calculated to assess changes in the computed tomography number. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to compare the sCT and original 4D-CBCT images. Moreover, a volumetric modulation arc therapy plan with a dose of 48 Gy in four fractions was recalculated using the sCT images and compared with ideal dose distributions observed in 4D-MSCT images. RESULTS: The generated sCT images had fewer artifacts, and lung tumor regions were clearly observed in the sCT images. The mean and mean absolute errors were near 0 Hounsfield units in all organ regions. The SSIM and PSNR results were significantly improved in the sCT images by approximately 51% and 18%, respectively. Moreover, the results of gamma analysis were significantly improved; the pass rate reached over 90% in the doses recalculated using the sCT images. Moreover, each organ dose index of the sCT images agreed well with those of the 4D-MSCT images and were within approximately 5%. CONCLUSIONS: The proposed CycleGAN enhances the quality of 4D-CBCT images, making them comparable to 4D-MSCT images. Thus, clinical implementation of sCT-based ART for lung cancer is feasible. |
format | Online Article Text |
id | pubmed-8991563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89915632022-04-09 A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images Usui, Keisuke Ogawa, Koichi Goto, Masami Sakano, Yasuaki Kyougoku, Shinsuke Daida, Hiroyuki Radiat Oncol Research BACKGROUND: Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of synthetic 4D-CBCT (sCT) images created by a cycle generative adversarial network (CycleGAN) for ART for lung cancer. METHODS: Unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images of 20 lung-cancer patients were used for training. High-quality sCT lung images generated by the CycleGAN model were tested on another 10 cases. The mean and mean absolute errors were calculated to assess changes in the computed tomography number. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to compare the sCT and original 4D-CBCT images. Moreover, a volumetric modulation arc therapy plan with a dose of 48 Gy in four fractions was recalculated using the sCT images and compared with ideal dose distributions observed in 4D-MSCT images. RESULTS: The generated sCT images had fewer artifacts, and lung tumor regions were clearly observed in the sCT images. The mean and mean absolute errors were near 0 Hounsfield units in all organ regions. The SSIM and PSNR results were significantly improved in the sCT images by approximately 51% and 18%, respectively. Moreover, the results of gamma analysis were significantly improved; the pass rate reached over 90% in the doses recalculated using the sCT images. Moreover, each organ dose index of the sCT images agreed well with those of the 4D-MSCT images and were within approximately 5%. CONCLUSIONS: The proposed CycleGAN enhances the quality of 4D-CBCT images, making them comparable to 4D-MSCT images. Thus, clinical implementation of sCT-based ART for lung cancer is feasible. BioMed Central 2022-04-07 /pmc/articles/PMC8991563/ /pubmed/35392947 http://dx.doi.org/10.1186/s13014-022-02042-1 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Usui, Keisuke Ogawa, Koichi Goto, Masami Sakano, Yasuaki Kyougoku, Shinsuke Daida, Hiroyuki A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title | A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title_full | A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title_fullStr | A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title_full_unstemmed | A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title_short | A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
title_sort | cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991563/ https://www.ncbi.nlm.nih.gov/pubmed/35392947 http://dx.doi.org/10.1186/s13014-022-02042-1 |
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