Respiratory Motion Compensation with Relevance Vector Machines
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.
Keywordsrespiratory motion compensation relevance vector machine radiotherapy bayesian learning
- 1.Giraud, P., De Rycke, Y., Helfre, S., Voican, D., Guo, L., Rosenwald, J.-C., Keraudy, K., Housset, M., Touboul, E., Cosset, J.-M.: Conformal radiotherapy (CRT) planning for lung cancer: analysis of intrathoracic organ motion during extreme phases of breathing. International Journal of Radiation Oncology 51(4), 1081–1092 (2001)CrossRefGoogle Scholar