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. © 2013 Springer-Verlag.
CITATION STYLE
Dürichen, R., Wissel, T., Ernst, F., & Schweikard, A. (2013). Respiratory motion compensation with relevance vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8150 LNCS, pp. 108–115). https://doi.org/10.1007/978-3-642-40763-5_14
Mendeley helps you to discover research relevant for your work.