Multi-modal respiratory motion prediction using sequential forward selection method

In robotic radiotherapy, systematic latencies have to be compensated by prediction of external optical surrogates. We investigate possibilities to increase the prediction accuracy using multi-modal sensors. The measurement setup includes position, acceleration, strain and flow sensors. To select the most relevant and least redundant information from the sensors and to limit the size of the feature set, a sequential forward selection (SFS) method is proposed. The method is evaluated with three prediction algorithms – the least means square (LMS) algorithm, a wavelet-based LMS algorithm (wLMS) and an algorithm using relevance vector machines (RVM). We show that multi-modal inputs can easily be integrated into general algorithms. The relative root mean square error (RMSrel) of the best predictor, RVM, could be decreased from 60.5 % to 48.4 Furthermore, the results indicate that more complex algorithms can efficiently use different modalities like acceleration which are less correlated with the optical sensor to be predicted.