[Prediction of respiratory motion based on nonparametric regression for real-time tumor-tracking radiotherapy]

It is necessary to compensate the system latencies in real-time tumor-tracking radiotherapy by prediction. However, due to the irregularities of respiratory motions, the results obtained with traditional methods were not acceptable. The purpose of this study is to evaluate the value of nonparametric regression model in respiratory motion prediction.

The data of respiratory trajectory of 11 volunteers were obtained and predicted based on nonparametric regression method. The results were compared with those of autoregressive model and back propagation neural network. An improved method was proposed to deal with the abnormal state in respiration. We combined the prediction method with the tracking system to test its performance in practical application.

The results indicated that the proposed method could predict the motion accurately in real-time for different latencies. This method decreased the error of the abnormal state substantially and also allowed effective prediction of respiration motion when combined with the tracking system.

The nonparametric regression model can predict the respiratory motion accurately in real-time and therefore meets the requirement of real-time tumor-tracking radiotherapy.