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Forecasting respiratory motion with accurate online support vector regression (SVRpred)

  • Floris Ernst
  • Achim Schweikard
Original Article

Abstract

Object

To accurately deliver radiation in image-guided robotic radiosurgery, highly precise prediction algorithms are required. A new prediction method is presented and evaluated.

Materials and methods

SVRpred, a new prediction method based on support vector regression (SVR), has been developed and tested. Computer-generated data mimicking human respiratory motion with a prediction horizon of 150 ms was used for lab tests. The algorithm was subsequently evaluated on a respiratory motion signal recorded during actual radiosurgical treatment using the CyberKnife®. The algorithm’s performance was compared to the MULIN prediction methods and Wavelet-based multi scale autoregression (wLMS).

Results

The SVRpred algorithm clearly outperformed both the MULIN and the wLMS algorithms on both real (by 15 and 16 percentage points, respectively) and noise-corrupted simulated data (by 13 and 48 percentage points, respectively). Only on noise-free artificial data, the SVRpred algorithm did perform as well as the MULIN algorithms but not as well as the wLMS algorithm.

Conclusion

This new algorithm is a feasible tool for the prediction of human respiratory motion signals significantly outperforming previous algorithms. The only drawback is the high computational complexity and the resulting slow prediction speed. High performance computers will be needed to use the algorithm in live prediction of signals sampled at a high resolution.

Keywords

Respiration Forecasting Prediction Algorithms Radiosurgery Support vector regression 

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Copyright information

© CARS 2009

Authors and Affiliations

  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany

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