Research on fault identification and diagnosis of multileaf collimator system of radiotherapy accelerator based on probabilistic neural network
Objective With the increasing number of radiotherapy for cancer patients in hospitals,the requirements for sustained and stable treatment of radiotherapy equipment are getting higher and higher.Radiotherapy linear accelerator is the main equipment for radiotherapy,and multileaf collimator(MLC)is one of the most frequent systems for intensity modulated radiotherapy.However,its failure rate is high.Once a failure occurs,it will not only affect the treatment effect of patients,but also bring economic losses to hospitals.Therefore,it is of great significance to identify and eliminate faults quickly and accurately to ensure the normal operation of MLC system.In this paper,a fault identification and diagnosis method of MLC system based on probabitistic neural network(PNN)is proposed,which provides maintenance basis for different fault phenomena and types of MLC system.Methods Combined with the maintenance experience and daily fault records of Medical University Radiotherapy Accelerator in the Cancer Hospital Affiliated to Fudan University,140 cases of MLC system structure and common fault phenomena were sorted and analyzed,and the parameter data of equipment state under common faults were statistically studied.The information that can characterize the fault characteristics was selected as input vector and fault classification output vector,and the combination of different characteristic input vectors represented different fault types.After the data were normalized and disordered,PNN neural network model was established and trained.Finally,the actual fault classification and prediction classification results were compared and analyzed.Results Through the comparison of classification results and confusion matrix,we found that there were 98 samples in the training set,and the accuracy of prediction comparison was 100%.There were 42 samples in the test set,the accuracy of prediction and comparison was 97.619%,and the total training time was 4.626 s.Conclusions The fault identification and diagnosis model of MLC system based on PNN probabilistic neural network has the advantages of fast training speed,good fault tolerance,and high accuracy in identification and diagnosis.
万方数据
维普
