A Survey of Algorithms for Respiratory Motion Prediction in Robotic Radiosurgery

Abstract

In robotic radiosurgery, a standard six-jointed industrial robot carries a linear accelerator. The accelerator can be moved such as to compensate for respiratory motion. Unfortunately, this motion cannot be compensated perfectly since the motion of the robot lags behind the motion of the target organ by-in systems currently employed clinically-approximately 150 ms. This delay is compensated by prediction algorithms, i.e., the time series stemming from human respiration is forecast. We have compared the performance of seven algorithms implemented in a common prediction tool kit. They are: multi-frequency tracking with Extended Kalman Filtering (EKF), normalised and regular Least Mean Squares filters (LMS and nLMS), wavelet-based multiscale autoregression (wLMS), a recursive least squares filter (RLS), multi-step linear methods (MULIN) and prediction based on support vector regression (SVRpred). All algorithms were tested on two signals: a simulated signal, corrupted by Gaussian noise, and a real breathing motion signal from a treatment session with the CyberKnife R at Georgetown University Hospital. The results are clear: the SVRpred algorithm outperforms the best other algorithm (wLMS for the real signal and MULIN for the simulated signal) by 15 and 9 percentage points, respectively.

OriginalspracheEnglisch
Seiten1035-1043
Seitenumfang9
PublikationsstatusVeröffentlicht - 01.12.2009
Veranstaltung39th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Im Focus das Leben, INFORMATIK 2009
- Audimax der Universität zu Lübeck, Lübeck, Deutschland
Dauer: 28.09.200902.10.2009
Konferenznummer: 95691

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress39th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Im Focus das Leben, INFORMATIK 2009
Land/GebietDeutschland
OrtLübeck
Zeitraum28.09.0902.10.09

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