@inproceedings{ESS07a,
Author = {F. Ernst and A. Schlaefer and A. Schweikard},
Title = {Prediction of respiratory motion with wavelet-based multiscale autoregression.},
Year = {(2007).},
Volume = {<strong>10</strong>.},
Number = {(Pt 2),},
Pages = {668-675},
Address = {Brisbane, Australia},
Isbn = {978-3-540-75759-7},
Booktitle = {<em>Med Image Comput Comput Assist Interv</em>},
Organization = {10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007)},
PMID = {18044626},
Doi = {10.1007/s11548-007-0083-7},
Institution = {Institute of Robotics and Cognitive Systems, University of Lübeck, DE. ernst@rob.uni-luebeck.de},
Keywords = {Computer Simulation; Humans; Models, Biological; Movement, physiology; Radiosurgery, methods; Regression Analysis; Reproducibility of Results; Respiratory Mechanics, physiology; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Surgery, Computer-Assisted, methods},
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\%.}
}

@COMMENT{Bibtex file generated on 2026-5-11 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2012-2000 }