Respiratory Motion Compensation with Relevance Vector Machines

Robert Dürichen^{1, 2}, Tobias Wissel^{1, 2}, Floris Ernst¹, and Achim Schweikard¹

¹Institute of Robotics and Cognitive Systems, University of Lübeck, Germany
duerichen@rob.uni-luebeck.de
wissel@rob.uni-luebeck.de
ernst@rob.uni-luebeck.de
schweikard@rob.uni-luebeck.de

²Graduate School for Computing in Medicine and Life Sciences, Lübeck, Germany

Abstract. In modern robotic radiation therapy, tumor movements due to respiration can be compensated. The accuracy of these methods can be increased by time series prediction of external optical surrogates. An algorithm based on relevance vector machines (RVM) is introduced. We evaluate RVM with linear and nonlinear basis functions on a real patient data set containing 304 motion traces and compare it with a wavelet based least mean square algorithm (wLMS), the best algorithm for this data set so far. Linear RVM outperforms wLMS significantly and increases the prediction accuracy for 80.3 % of the data. We show that real time prediction is possible in case of linear RVM and discuss how the predicted variance can be used to construct promising hybrid algorithms, which further reduce the prediction error.

Keywords: respiratory motion compensation, relevance vector machine, radiotherapy, bayesian learning

LNCS 8150, p. 108 ff.

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