Version 1
: Received: 22 October 2024 / Approved: 22 October 2024 / Online: 22 October 2024 (17:03:18 CEST)
How to cite:
Eom, Y.; Park, Y.-J.; Lee, S.; Lee, S. J.; An, Y.-S.; Park, B.-N.; Yoon, J.-K. Deep Learning-based Automated Measurement of Effective Radiation Dose by 18F-FDG PET/CT. Preprints2024, 2024101739. https://doi.org/10.20944/preprints202410.1739.v1
Eom, Y.; Park, Y.-J.; Lee, S.; Lee, S. J.; An, Y.-S.; Park, B.-N.; Yoon, J.-K. Deep Learning-based Automated Measurement of Effective Radiation Dose by 18F-FDG PET/CT. Preprints 2024, 2024101739. https://doi.org/10.20944/preprints202410.1739.v1
Eom, Y.; Park, Y.-J.; Lee, S.; Lee, S. J.; An, Y.-S.; Park, B.-N.; Yoon, J.-K. Deep Learning-based Automated Measurement of Effective Radiation Dose by 18F-FDG PET/CT. Preprints2024, 2024101739. https://doi.org/10.20944/preprints202410.1739.v1
APA Style
Eom, Y., Park, Y. J., Lee, S., Lee, S. J., An, Y. S., Park, B. N., & Yoon, J. K. (2024). Deep Learning-based Automated Measurement of Effective Radiation Dose by 18F-FDG PET/CT. Preprints. https://doi.org/10.20944/preprints202410.1739.v1
Chicago/Turabian Style
Eom, Y., Bok-Nam Park and Joon-Kee Yoon. 2024 "Deep Learning-based Automated Measurement of Effective Radiation Dose by 18F-FDG PET/CT" Preprints. https://doi.org/10.20944/preprints202410.1739.v1
Abstract
Background/Objectives: Calculating the radiation dose from CT in 18F-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 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, p = 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, p = 0.8957). A very strong correlation was observed in both CT ED and total ED between the two measurements (r2 = 0.9981 and 0.9996, respectively). When comparing the older and newer PET/CT scanners, the CT ED was not significantly different. However, 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, p < 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, p < 0.0001). Conclusions: We successfully developed a deep learning-based automated program that calculates the ED of torso 18F-PET/CT, thereby eliminating inter-operator variability.
Keywords
18F-FDG; Positron Emission Tomography; Computed Tomography; Effective Dose; Deep Learning
Subject
Medicine and Pharmacology, Clinical Medicine
Copyright:
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.
