Prediction of respiratory motion with wavelet-based multiscale autoregression.
In robotic radiosurgery, a photon beam source, moved by a robot arm, is used to ablate tumors. The accuracy of the treatment can be improved by predicting respiratory motion to compensate for system delay. We consider a wavelet-based multiscale autoregressive prediction method. The algorithm is extended by introducing a new exponential averaging parameter and the use of the Moore-Penrose pseudo inverse to cope with long-term signal dependencies and system matrix irregularity, respectively. In test cases, this new algorithm outperforms normalized LMS predictors by as much as 50%. With real patient data, we achieve an improvement of around 5 to 10%.
Institute of Robotics and Cognitive Systems, University of Lübeck, DE. ernst@rob.uni-luebeck.de
Floris
F
Ernst
Alexander
A
Schlaefer
Achim
A
Schweikard
D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D016634:Radiosurgery / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D015656:Respiratory Mechanics / Q000502:physiology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D025321:Surgery, Computer-Assisted / Q000379:methods*