Particle filtering-based tumor motion estimation and prediction system and method of radiotherapy robot

Abstract

The invention discloses a particle filtering-based tumor motion estimation and prediction system and method of a radiotherapy robot. The method comprises the following steps of (S1) collecting three-dimensional motion data of a skin-marker and an in vivo tumor by using a breathing tracking unit and an image positioning unit separately; (S2) building a motion relationship model between the tumor at a current moment and the tumor at a historic moment according to the three-dimensional motion data, and building a motion relationship model between the skin-marker and the in vivo tumor within a period of time as an observation equation of particle filtering by taking the model as a state transfer equation of particle filtering; and (S3) estimating the motion position of the in vivo tumor by using a particle filtering algorithm according to motion data of the skin-marker at the current moment on the basis of the state transfer equation and the observation equation. The particle filtering-based tumor motion estimation and prediction system and method can be applied to the state space model in any form, has stronger modeling capability on nonlinear characteristics of variable parameters and is relatively high in prediction accuracy.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a system and method for estimating and predicting tumor motion of a radiotherapy robot based on particle filtering. Background technique [0002] At present, the main method for the treatment of lung cancer is stereotactic radiotherapy, but the respiratory movement of the human body seriously affects the accuracy of radiotherapy. In order to reduce the influence of respiratory motion, the most effective method is the real-time tracking technology of respiratory motion. This technology establishes an association model between the tumor in the body and the marker points on the body surface, and uses a prediction algorithm to obtain the future movement of the tumor according to the movement of the marker points. Information, so as to adjust the radiation beam in real time to ensure the relative stillness of the radiation and the tumor, so as to achieve real-time tracking of tumor r...

AI Technical Summary

Problems solved by technology

[0004] The learning efficiency of artificial neural network is low, the convergence speed is slow, and it takes a long time for network training, which is not suitable for real-time prediction;

[0005] The Kalman filter requires the system to be a Gaussian white noise system, and it is only suitable for linear systems, but the actual respiratory movement process is a complex nonlinear process, so the Kalman filter is not suitable for the prediction of tumor movement;

[0006] The fuzzy control prediction effect is better, but this method has limited adaptive ability and high complexity

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