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A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients
OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patie...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7975236/ https://www.ncbi.nlm.nih.gov/pubmed/33740092 http://dx.doi.org/10.1007/s00330-020-07629-4 |
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author | Gang, Yadong Chen, Xiongfeng Li, Huan Wang, Hanlun Li, Jianying Guo, Ying Zeng, Junjie Hu, Qiang Hu, Jinxiang Xu, Haibo |
author_facet | Gang, Yadong Chen, Xiongfeng Li, Huan Wang, Hanlun Li, Jianying Guo, Ying Zeng, Junjie Hu, Qiang Hu, Jinxiang Xu, Haibo |
author_sort | Gang, Yadong |
collection | PubMed |
description | OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. RESULTS: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. CONCLUSION: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae. |
format | Online Article Text |
id | pubmed-7975236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-79752362021-03-19 A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients Gang, Yadong Chen, Xiongfeng Li, Huan Wang, Hanlun Li, Jianying Guo, Ying Zeng, Junjie Hu, Qiang Hu, Jinxiang Xu, Haibo Eur Radiol Imaging Informatics and Artificial Intelligence OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. RESULTS: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. CONCLUSION: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae. Springer Berlin Heidelberg 2021-03-19 2021 /pmc/articles/PMC7975236/ /pubmed/33740092 http://dx.doi.org/10.1007/s00330-020-07629-4 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Imaging Informatics and Artificial Intelligence Gang, Yadong Chen, Xiongfeng Li, Huan Wang, Hanlun Li, Jianying Guo, Ying Zeng, Junjie Hu, Qiang Hu, Jinxiang Xu, Haibo A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title | A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title_full | A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title_fullStr | A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title_full_unstemmed | A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title_short | A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients |
title_sort | comparison between manual and artificial intelligence–based automatic positioning in ct imaging for covid-19 patients |
topic | Imaging Informatics and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7975236/ https://www.ncbi.nlm.nih.gov/pubmed/33740092 http://dx.doi.org/10.1007/s00330-020-07629-4 |
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