基于非参数回归的呼吸运动预测方法在实时跟踪放疗中的应用

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

目的在实时跟踪放疗中,需要通过预测来补偿系统延迟的影响。但由于呼吸运动的不规则性,利用传统的方法很难满足要求,本研究提出应用非参数回归模型进行预测。方法采集11名受试者的呼吸运动数据,然后运用非参数回归数学模型进行预测,并与自回归模型和BP神经网络的预测结果进行比较。进一步地,针对预测过程中出现的"异常状态"提出了一种改进的非参数回归方法。最后将预测算法与测量系统相结合,验证算法实时预测的有效性。结果经测试表明,在不同的预测长度下,非参数回归法在预测精度和实时性两方面均有很好的表现,改进的方法能大幅减小"异常状态"的预测误差,且与测量系统结合后,算法同样能实时准确的进行预测。结论非参数回归法在呼吸运动预测中准确度高、实时性好,能满足实时跟踪放疗的要求。

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