[1]段欢欢,李书舟,曹瑛,等.基于深度学习方法预测IMRT计划射野的γ通过率[J].中国医学物理学杂志,2021,38(6):677-681.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.004]
　. School of Nuclear Science and Technology,University of South China,Hengyang 00,et al.Predicting gamma passing rates for intensity-modulated radiotherapy fields based on deep learning method[J].Chinese Journal of Medical Physics,2021,38(6):677-681.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.004]
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基于深度学习方法预测IMRT计划射野的γ通过率()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第6期
页码:
677-681
栏目:
医学放射物理
出版日期:
2021-06-29

文章信息/Info

Title:
Predicting gamma passing rates for intensity-modulated radiotherapy fields based on deep learning method
文章编号:
1005-202X（2021）06-0677-05
作者:
段欢欢1; 李书舟2; 曹瑛2; 唐杜2; 雷明军2; 杨振2; 邱小平1
1.南华大学核科学技术学院， 湖南 衡阳 421001； 2.中南大学湘雅医院肿瘤科， 湖南 长沙 410008
Author(s):
1. School of Nuclear Science and Technology;  University of South China;  Hengyang 421001;  China 2. Department of Oncology;  Xiangya Hospital;  Central South University;  Changsha 410008;  China
1. School of Nuclear Science and Technology, University of South China, Hengyang 421001, China 2. Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
关键词:
脑胶质瘤; 深度学习; γ通过率; IMRT; 质量保证
Keywords:
Keywords: gliomas deep learning gamma passing rate intensity-modulated radiotherapy quality assurance
分类号:
R318；R815.6
DOI:
DOI:10.3969/j.issn.1005-202X.2021.06.004
文献标志码:
A
摘要:
目的：建立卷积神经网络（CNN）模型预测IMRT计划射野的γ通过率（GPR）。方法：从Eclipse治疗计划系统中选取48例脑胶质瘤患者的IMRT计划，共计260个照射野，制作每个计划基于电子射野影像系统测量的验证计划，并在Varian 23EX直线加速器上执行。利用portal dosimetry剂量测定软件包对计划剂量的计算值和电子射野影像系统实测值进行γ分析，得到射野在2%（global）/2 mm标准下的GPR。选取portal dosimetry系统计算的剂量分布图作为输入数据，并将数据集划分为训练集208个射野，验证集和测试集各26个射野。基于tensorflow框架建立CNN模型去学习射野的剂量分布图与GPR之间的相关性，并使用平均绝对误差对模型的预测效果进行评估。结果：在验证集和测试集上，96%样本的GPR预测误差都小于±3%，最大误差分别为3.09%和3.54%，平均绝对误差分别为0.99%和1.17%，模型预测和实际测量的GPR之间的皮尔逊相关性系数r分别为0.96和0.90。结论：深度学习CNN模型可以准确地预测脑胶质瘤患者IMRT计划射野的GPR，有助于物理师提前识别可能不能通过QA测量的计划，有效地促进临床放疗的QA工作。
Abstract:
Abstract: Objective To develop a convolution neural network (CNN) model for predicting the gamma passing rates (GPR) for intensity-modulated radiotherapy (IMRT) fields. Methods The IMRT plans of 48 gliomas patients were extracted from Eclipse treatment planning system, with a total of 260 radiation fields. The corresponding verification plan of each IMRT plan was designed based on the measurements of electronic portal imaging device, and it was delivered on Varian 23 EX Linac. Then, portal dosimetry system was used for Gamma analysis on calculated dose and actual dose measured by electronic portal imaging device, thereby obtaining GPR under 2% (global)/2 mm criterion. The dose distribution map obtained by portal dosimetry system was taken as input, and the data set was divided into training set (208 fields), validation set (26 fields) and test set (26 fields). A CNN model which was developed based on tensorflow framework was used to learn the correlation between the dose distribution map and GPR and mean absolute error was used to evaluate the prediction effect of the model. Results In validation set and test set, the GPR prediction errors of 96% of samples were less than ±3%, with a maximum prediction error of 3.09% and 3.54% and a mean absolute error of 0.99% and 1.17%. The Pearson correlation coefficients between predicted and measured GPR in validation set and test set were 0.96 and 0.90, respectively. Conclusion The CNN model developed based on deep learning can accurately predict the GPR for gliomas IMRT fields, which can inform the physicists which plans may not meet the requirement of quality assurance in advance, thereby effectively promoting the quality assurance of clinical radiotherapy.

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备注/Memo

备注/Memo:
【收稿日期】2021-01-20 【基金项目】国家自然科学基金青年科学基金（61906215） 【作者简介】段欢欢，硕士研究生，研究方向：医学物理，E-mail: 985856963@qq.com 【通信作者】邱小平，教授，研究方向：核技术应用，E-mail: nh6551@163.com；杨振，副教授，研究方向：肿瘤放射物理学，E-mail: zhenyang@csu.edu.cn
更新日期/Last Update: 2021-06-29