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Prediction of respiratory motion with wavelet-based multiscale autoregression.

Author information

1
Institute of Robotics and Cognitive Systems, University of L├╝beck, DE. ernst@rob.uni-luebeck.de

Abstract

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%.

PMID:
18044626
[Indexed for MEDLINE]

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