naa a22 4500 510807127 CHVBK 20180411083423.0 cr unu---uuuuu 180411e20131101xx s 000 0 eng 10.1007/s11548-013-0900-0 doi (NATIONALLICENCE)springer-10.1007/s11548-013-0900-0 Optimized order estimation for autoregressive models to predict respiratory motion [Elektronische Daten] [Robert Dürichen, Tobias Wissel, Achim Schweikard] Purpose: To successfully ablate moving tumors in robotic radio-surgery, it is necessary to compensate for motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in the CyberKnife $$^\circledR $$ Synchrony system. Tracking errors, originating from system immanent time delays, are typically reduced by time series prediction. Many prediction algorithms exploit autoregressive (AR) properties of the signal. Estimating the optimal model order $$p$$ for these algorithms constitutes a challenge often solved via grid search or prior knowledge about the signal. Methods: Aiming at a more efficient approach instead, this study evaluates the Akaike information criterion (AIC), the corrected AIC, and the Bayesian information criterion (BIC) on the first minute of the respiratory signal. Exemplarily, we evaluated the approach for a least mean square (LMS) and a wavelet-based LMS (wLMS) predictor. Results: Analyzing 12 motion traces, orders estimated by AIC had the highest prediction accuracy for both prediction algorithms. Extending the investigations to 304 real motion traces, the prediction error of wLMS using AIC was found to decrease significantly by 85.1% of the data compared to the original implementation Conclusions: The overall results suggest that using AIC to estimate the model order $$p$$ for prediction algorithms based on AR properties is a valid method which avoids intensive grid search and leads to high prediction accuracy. CARS, 2013 Computer assisted radiation therapy nationallicence Respiratory motion compensation nationallicence Autoregressive models nationallicence Information criteria nationallicence Dürichen Robert Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut Wissel Tobias Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut Schweikard Achim Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/6(2013-11-01), 1037-1042 1861-6410 8:6<1037 2013 8 11548 https://doi.org/10.1007/s11548-013-0900-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-013-0900-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Dürichen Robert Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut NATIONALLICENCE 700 1- Wissel Tobias Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut NATIONALLICENCE 700 1- Schweikard Achim Institute for Robotics and Cognitive Systems, University of Luebeck, Ratzeburger Allee 160, Lübeck, Germany aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/6(2013-11-01), 1037-1042 1861-6410 8:6<1037 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer