Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography
Background/Objectives: Calculating the radiation dose from CT in <sup>18</sup>F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation doses. Methods: The torso CT...
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MDPI AG
2024-12-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/10/12/151 |
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| author | Yujin Eom Yong-Jin Park Sumin Lee Su-Jin Lee Young-Sil An Bok-Nam Park Joon-Kee Yoon |
| author_facet | Yujin Eom Yong-Jin Park Sumin Lee Su-Jin Lee Young-Sil An Bok-Nam Park Joon-Kee Yoon |
| author_sort | Yujin Eom |
| collection | DOAJ |
| description | Background/Objectives: Calculating the radiation dose from CT in <sup>18</sup>F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation doses. Methods: The torso CT was segmented into six distinct regions using TotalSegmentator. An automated program was employed to extract the necessary information and calculate the effective dose (ED) of PET/CT. The accuracy of our automated program was verified by comparing the EDs calculated by the program with those determined by a nuclear medicine physician (n = 30). Additionally, we compared the EDs obtained from an older PET/CT scanner with those from a newer PET/CT scanner (n = 42). Results: The CT ED calculated by the automated program was not significantly different from that calculated by the nuclear medicine physician (3.67 ± 0.61 mSv and 3.62 ± 0.60 mSv, respectively, <i>p</i> = 0.7623). Similarly, the total ED showed no significant difference between the two calculation methods (8.10 ± 1.40 mSv and 8.05 ± 1.39 mSv, respectively, <i>p</i> = 0.8957). A very strong correlation was observed in both the CT ED and total ED between the two measurements (r<sup>2</sup> = 0.9981 and 0.9996, respectively). The automated program showed excellent repeatability and reproducibility. When comparing the older and newer PET/CT scanners, the PET ED was significantly lower in the newer scanner than in the older scanner (4.39 ± 0.91 mSv and 6.00 ± 1.17 mSv, respectively, <i>p</i> < 0.0001). Consequently, the total ED was significantly lower in the newer scanner than in the older scanner (8.22 ± 1.53 mSv and 9.65 ± 1.34 mSv, respectively, <i>p</i> < 0.0001). Conclusions: We successfully developed an automated program for calculating the ED of torso <sup>18</sup>F-PET/CT. By integrating a deep learning model, the program effectively eliminated inter-operator variability. |
| format | Article |
| id | doaj-art-1af06a2d491a4f41a3bd9b97ad0f00a9 |
| institution | DOAJ |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2024-12-01 |
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| series | Tomography |
| spelling | doaj-art-1af06a2d491a4f41a3bd9b97ad0f00a92025-08-20T02:43:41ZengMDPI AGTomography2379-13812379-139X2024-12-0110122144215710.3390/tomography10120151Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed TomographyYujin Eom0Yong-Jin Park1Sumin Lee2Su-Jin Lee3Young-Sil An4Bok-Nam Park5Joon-Kee Yoon6Department of AI Mobility Engineering, Ajou University, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of KoreaBackground/Objectives: Calculating the radiation dose from CT in <sup>18</sup>F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation doses. Methods: The torso CT was segmented into six distinct regions using TotalSegmentator. An automated program was employed to extract the necessary information and calculate the effective dose (ED) of PET/CT. The accuracy of our automated program was verified by comparing the EDs calculated by the program with those determined by a nuclear medicine physician (n = 30). Additionally, we compared the EDs obtained from an older PET/CT scanner with those from a newer PET/CT scanner (n = 42). Results: The CT ED calculated by the automated program was not significantly different from that calculated by the nuclear medicine physician (3.67 ± 0.61 mSv and 3.62 ± 0.60 mSv, respectively, <i>p</i> = 0.7623). Similarly, the total ED showed no significant difference between the two calculation methods (8.10 ± 1.40 mSv and 8.05 ± 1.39 mSv, respectively, <i>p</i> = 0.8957). A very strong correlation was observed in both the CT ED and total ED between the two measurements (r<sup>2</sup> = 0.9981 and 0.9996, respectively). The automated program showed excellent repeatability and reproducibility. When comparing the older and newer PET/CT scanners, the PET ED was significantly lower in the newer scanner than in the older scanner (4.39 ± 0.91 mSv and 6.00 ± 1.17 mSv, respectively, <i>p</i> < 0.0001). Consequently, the total ED was significantly lower in the newer scanner than in the older scanner (8.22 ± 1.53 mSv and 9.65 ± 1.34 mSv, respectively, <i>p</i> < 0.0001). Conclusions: We successfully developed an automated program for calculating the ED of torso <sup>18</sup>F-PET/CT. By integrating a deep learning model, the program effectively eliminated inter-operator variability.https://www.mdpi.com/2379-139X/10/12/151<sup>18</sup>F-FDGpositron emission tomographycomputed tomographyeffective dosedeep learning |
| spellingShingle | Yujin Eom Yong-Jin Park Sumin Lee Su-Jin Lee Young-Sil An Bok-Nam Park Joon-Kee Yoon Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Tomography <sup>18</sup>F-FDG positron emission tomography computed tomography effective dose deep learning |
| title | Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography |
| title_full | Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography |
| title_fullStr | Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography |
| title_full_unstemmed | Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography |
| title_short | Automated Measurement of Effective Radiation Dose by <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography |
| title_sort | automated measurement of effective radiation dose by sup 18 sup f fluorodeoxyglucose positron emission tomography computed tomography |
| topic | <sup>18</sup>F-FDG positron emission tomography computed tomography effective dose deep learning |
| url | https://www.mdpi.com/2379-139X/10/12/151 |
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