Efficient SVR model update approaches for respiratory motion prediction

In order to successfully ablate moving tumours in robotic radiosurgery, respiratory motion prediction is needed to compensate time delays. In this context, recent studies revealed a high potential of support vector regression (SVR). However, high computational cost is one major drawback, particularly caused by batch mode training. We evaluate two approaches to reduce the update rate as well as computation time, while keeping a low prediction error. The update rules are either based on information about the respiratory phase or based on the current prediction error. An evaluation on patient data sets revealed that the second approach on average decreases computation time by 88.53% compared to a batch mode implementation. The prediction error increased by 0.3%, hence indicating enhanced efficiency.