Eom Yujin, Park Yong-Jin, Lee Sumin, Lee Su-Jin, An Young-Sil, Park Bok-Nam, Yoon Joon-Kee
Department of AI Mobility Engineering, Ajou University, Suwon 16499, Republic of Korea.
Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Tomography. 2024 Dec 23;10(12):2144-2157. doi: 10.3390/tomography10120151.
BACKGROUND/OBJECTIVES: Calculating the radiation dose from CT in 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.
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).
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, = 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, = 0.8957). A very strong correlation was observed in both the CT ED and total ED between the two measurements (r = 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, < 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, < 0.0001).
We successfully developed an automated program for calculating the ED of torso F-PET/CT. By integrating a deep learning model, the program effectively eliminated inter-operator variability.
背景/目的：计算F - PET/CT检查中CT的辐射剂量是一项重大挑战。本研究的目的是开发一种基于深度学习的自动化程序，以规范辐射剂量的测量。
使用TotalSegmentator将躯干CT分割为六个不同区域。采用自动化程序提取必要信息并计算PET/CT的有效剂量（ED）。通过将程序计算的ED与核医学医师确定的ED进行比较（n = 30），验证了我们自动化程序的准确性。此外，我们比较了从较旧的PET/CT扫描仪和较新的PET/CT扫描仪获得的ED（n = 42）。
自动化程序计算的CT ED与核医学医师计算的CT ED无显著差异（分别为3.67±0.61 mSv和3.62±0.60 mSv，P = 0.7623）。同样，两种计算方法的总ED也无显著差异（分别为8.10±1.40 mSv和8.05±1.39 mSv，P = 0.8957）。两种测量之间在CT ED和总ED中均观察到非常强的相关性（r分别为0.9981和0.9996）。自动化程序显示出出色的重复性和再现性。比较较旧和较新的PET/CT扫描仪时，较新扫描仪的PET ED显著低于较旧扫描仪（分别为4.39±0.91 mSv和6.00±1.17 mSv，P < 0.0001）。因此，较新扫描仪的总ED显著低于较旧扫描仪（分别为8.22±1.53 mSv和9.65±1.34 mSv，P < 0.0001）。
我们成功开发了一种用于计算躯干F - PET/CT有效剂量的自动化程序。通过集成深度学习模型，该程序有效消除了操作者间的变异性。
