Fast estimation of internal irradiation dose rate in PET/CT imaging using deep learning-based dose prediction combined with auto-segmentation technique(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第2期
- Page:
- 149-156
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Fast estimation of internal irradiation dose rate in PET/CT imaging using deep learning-based dose prediction combined with auto-segmentation technique
- Author(s):
- LU Yu1; PENG Zhao1; PEI Xi1; NI Ming2; XIE Qiang2; WANG Shicun2; XU Xie1; 3; CHEN Zhi1
- 1. School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230026, China 2. Department of Nuclear Medicine, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China 3. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China
- Keywords:
- Keywords: positron emission tomography deep learning internal irradiation dose auto-segmentation technique
- PACS:
- R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.02.004
- Abstract:
- Abstract: Objective To realize the fast estimation of internal irradiation dose rate in PET/CT imaging using the combination of deep learning-based dose prediction and auto-segmentation technique. Methods Based on the PET/CT images of the patients at a specific moment, Monte Carlo simulation software GATE was used to calculate the internal irradiation dose rate, and obtain the dose rate distribution map of each patient. The PET and CT image patches were used as inputs for the training of a deep neural network constructed based on U-Net network, while internal irradiation dose rate map calculated by Monte Carlo simulation software GATE was given as ground truth. The trained deep learning model could predict the dose rate map according to the PET/CT images. Meanwhile, the radiosensitive organs and tissues in the CT images were automatically segmented using DeepViewer. The absorbed dose rates of the corresponding organs and tissues were calculated based on organ segmentation results and the predicted dose rate distribution. The PET/CT images of 50 patients were used in the study. Ten of which were used as testing set, and the others were used for 4-fold cross-validation training, with 30 for training and 10 for validation in each fold. The predicted results were compared with the results obtained by GATE and ARCHER-NM (a GPU-accelerated Monte Carlo dose calculation module). Results For most of the 24 organs segmented by DeepViewer, the relative differences between predicted dose rate and GATE simulation results were within ±10%. Specifically, the average relative differences of brain, heart, liver, left and right lungs were 3.3%, 1.1%, 1.0%, -1.1% and 0.0%, respectively, indicating a good consistency between dose rate prediction and GATE simulation. For each patient, the deep learning-based prediction costs 15.1 s on average for the estimation of internal irradiation dose rate, while the GATE simulation costs 8.91 h. The calculation speed was increased by a factor of 2 120. The comparison between deep learning-based prediction and ARCHER-NM showed that the deep learning-based prediction had an advantage of execution time, while its interpretability needed further improvement. Conclusion The combination of deep learning-based dose prediction and auto-segmentation technique is expected to be a method for the rapid estimation of internal irradiation dose rate in PET/CT imaging, and provide a solution to calculate the real-time internal absorbed dose rapidly for the practices of clinical nuclear medicine.
Last Update: 2023-03-03