Research on multi-leaf collimator fault prediction model of Varian Novalis Tx medical linear accelerator based on BP Neural Network realized by R language
10.3760/cma.j.issn.1004-4221.2018.05.012
- VernacularTitle:基于R语言BP神经网络瓦里安NovalisTx直线加速器MLC系统故障预测模型研究
- Author:
Yongjin DENG
1
;
Zhenhua XIAO
;
Bin OUYANG
;
Zhenyu WANG
;
Botian HUANG
;
Jingxian HUANG
;
Yong BAO
Author Information
1. 中山大学附属第一医院放射治疗科
- Keywords:
Neural network;
Multi-leaf collimator;
Fault prediction;
R language
- From:
Chinese Journal of Radiation Oncology
2018;27(5):495-499
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and investigate the multi-leaf collimator (MLC) fault prediction model of Varian NovalisTx medical linear accelerator based on BP neural network.Methods The MLC fault data applied in clinical trial for 18 months were collected and analyzed.The total use time of accelerator,the quantity of patients per month,average daily working hours of accelerator,volume of RapidArc plans and time interval between accelerator maintenance were used as the input factors and the prediction of MLC fault frequency was considered as the output result.The BP neural network model of MLC fault prediction was realized by AMORE package of R language and the simulation results were validated.Results The model contained 3 layers of network to realize the input-output switch.There were 5 nodes in the input layer,13 nodes in the hide layer and 1 node in the output layer,respectively.The transfer function from the input layer to the hide layer selected the tansig function and purelin function was used from the hide layer to the output layer.The maximum time of training was pre-set as 150 in the designed model.Actually,111 times of training were performed.The pre-set error was 3％ and the actual error was 2.7％,which indicated good convergence.The simulation results of MLC fault applied in clinical trial for 18 months were similar to the actual data.Conclusions The BP neural network model realized by R language of MLC fault prediction can describe the mapping relationship between fault factors and fault frequency,which provides references for the understanding of accelerator fault and management of spare parts inventory.
