This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose...This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.展开更多
目的:探讨C B C T和E P I D在体部肿瘤放疗摆位的对比情况.方法:选取我院2016年3月至2018年3月收治的64例肺癌患者为研究对象,所有患者均采用真空垫体位固定摆位模型,将患者随机分为两组,每组各32例患者.研究组患者采用CBCT进行摆位,在C...目的:探讨C B C T和E P I D在体部肿瘤放疗摆位的对比情况.方法:选取我院2016年3月至2018年3月收治的64例肺癌患者为研究对象,所有患者均采用真空垫体位固定摆位模型,将患者随机分为两组,每组各32例患者.研究组患者采用CBCT进行摆位,在CBCT影像模式指导下进行放射治疗;常规组患者采用EPID进行摆位,在EPID影像模式指导下进行放射治疗.对两组患者的放疗摆位情况进行对比.结果:所有患者均全部顺利完成放疗,真空漏气事件未发生.与常规组相比,研究组患者的左右(X值)、上下(Y值)及前后(Z值)偏差值明显偏低,差异统计学意义(P<0.05).结论:CBCT和EPID配准方法均可以满足临床应用,但是CBCT图像引导放射治疗能更全面准确的分析摆位误差,效果优于EPID.展开更多
基金supported by National Key R&D Program of China(No.2022YFC2404604)Chongqing Research Institution Performance Incentive Guidance Special Project(No.CSTB2023JXJL-YFX0080)Chongqing Medical Scientific Research Project(Joint project of Chongqing Health Commission and Science and Technology Bureau)(No.2022DBXM005)。
文摘This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.
文摘目的:探讨C B C T和E P I D在体部肿瘤放疗摆位的对比情况.方法:选取我院2016年3月至2018年3月收治的64例肺癌患者为研究对象,所有患者均采用真空垫体位固定摆位模型,将患者随机分为两组,每组各32例患者.研究组患者采用CBCT进行摆位,在CBCT影像模式指导下进行放射治疗;常规组患者采用EPID进行摆位,在EPID影像模式指导下进行放射治疗.对两组患者的放疗摆位情况进行对比.结果:所有患者均全部顺利完成放疗,真空漏气事件未发生.与常规组相比,研究组患者的左右(X值)、上下(Y值)及前后(Z值)偏差值明显偏低,差异统计学意义(P<0.05).结论:CBCT和EPID配准方法均可以满足临床应用,但是CBCT图像引导放射治疗能更全面准确的分析摆位误差,效果优于EPID.
