Controlling motion prediction errors in radiotherapy with relevance vector machines

Purpose Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms. Methods First, the duty cycle and the precision were correlated to the variance by interrupting the treatment if the variance exceeds a threshold. Second, two hybrid algorithms based on the variance were developed, one consisting of multiple RVMs ( HYB RVM ) and the other of a combination between a wavelet-based least mean square algorithm (wLMS) and a RVM ( HYB wLMS - RVM ). The variance for different motion traces was analyzed to reveal a characteristic variance pattern which gives insight in what kind of prediction errors can be controlled by the variance. Results Limiting the variance by a threshold resulted in an increased precision with a decreased duty cycle. All hybrid algorithms showed an increased prediction accuracy compared to using only their individual algorithms. The best hybrid algorithm, HYB RVM , can decrease the mean RMSE over all 304 motion traces from 0.18 mm for a linear RVM to 0.17 mm. Conclusions The predicted variance was shown to be an efficient metric to control prediction errors, resulting in a more robust radiotherapy treatment. The hybrid algorithm HYB RVM could be translated to clinical practice. It does not require further parameters, can be completely parallelised and easily further extended.