Feasibility of deep learning-accelerated Monte Carlo simulation of EPID transit dose images
10.3969/j.issn.1005-202X.2025.11.001
- VernacularTitle:基于深度学习加速EPID透射剂量图像蒙特卡罗模拟的可行性
- Author:
Ning GAO
1
;
Jieping ZHOU
;
Yankui CHANG
;
Qiang REN
;
Xi PEI
;
Aidong WU
;
Xie XU
Author Information
1. 中国科学技术大学核科学技术学院,安徽 合肥 230026
- Publication Type:Journal Article
- Keywords:
electronic portal imaging device;
transit dose image;
Monte Carlo;
deep learning;
in vivodose verification
- From:
Chinese Journal of Medical Physics
2025;42(11):1401-1407
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.
