UT transform based tumor respiratory motion estimation and prediction for radiosurgery robot

Stereotactic radiotherapy such as Cyberknife is one of the main methods of treatment for lung cancer, but tumor location change caused by human respiration has brought great difficulties to accurate radiation therapy. The main method to reduce the effect of respiratory motion in the process of radiotherapy is respiratory motion real-time tracking technology. The basis of real-time tracking is establishing correlation model between motions of the tumor and the markers on patients' skin. Afterwards, prediction algorithm is used on the markers' data of the movement to predict tumor motion data, thus achieving the purpose of real-time tumor-tracking. The traditional modeling methods are mainly based on the least squares method to establish the linear model or polynomial model. However, the traditional method has not considered sensor noises and modeling error, so it is with low precision. The sensor noise and model error are taken into consideration, and a correlation model is established based on UT transform in this paper. As the following step, Extended Kalman Filter algorithm is adopted to predict the movement of the tumor. Meanwhile, comparative analysis with traditional modeling method is conducted to verify the effectiveness of this method by comparing the prediction error of the two methods.