Optimized order estimation for autoregressive models to predict respiratory motion

Type of publication:  Artikel in einem Konferenzbericht
Buchtitel: Proceedings of the 27th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS'13)
Serie: International Journal of Computer Assisted Radiology and Surgery
Band: 8
Nummer: S1
Jahr: 2013
Monat: Juni
Seiten: accepted for publication
Ort: Heidelberg, Germany
Notiz: Motion Compensation in Radiosurgery
Querverweis: CARS13
Abriss: 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 correlat-ing the external movement with the tumor position as it is implemented in CyberKnife Synchrony. 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 for a more e cient approach instead, this study evaluates the Akaike information criterion (AIC), the corrected AIC (AICc) and the Bayesian information criterion (BIC) on the first minute of the respiratory signal. Exemplarily, we evaluated the approach for an 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 algorithm. Extending the investiga-tions to 304 real motion traces, the prediction accuracy of wLMS using AIC was found to decrease for 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.
Autoren: Dürichen, Robert
Wissel, Tobias
Schweikard, Achim
  • 2013 - CARS - Dürichen - Optim...