Method for predicting human body thoracico-abdominal surface area respiratory movement in radiotherapy

The method provides a method for predicting the human body thoracico-abdominal surface area respiratory movement in radiotherapy, belonging to the field of tumor treatment and respiratory motion prediction. The prediction method with the respiratory motion prediction function adopts a dual-camera to collect respiration motion signal data, the collected data are preprocessed, and the obtained datais used as the input of a Gaussian process regression prediction algorithm. Then, a Gaussian process model is provided for predicting the regression problem. A proper regression model is constructedby utilizing the data, training and prediction are carried out, a kernel function is selected in the training process, and moreover, the hyper-parameter can be obtained and the feasibility of the algorithm is verified through an off-line simulation experiment. Finally, a quasi-periodic kernel function is selected, and hyper-parameters are solved by utilizing a conjugation gradient method, and themodel prediction on the respiratory movement data through a Gaussian process regression method. According to the method for predicting the human body thoracico-abdominal surface area respiratory movement in radiotherapy, the influence of the respiratory movement on the radiotherapy precision can be reduced.