In extracranial robotic radiotherapy, tumour motion due to respiration is compensated based external markers. Two models are typically used to enable a real-time adaptation. A prediction model, which compensates time latencies of the treatment systems due to e.g. kinematic limitations, and a correlation model, which estimates the internal tumour position based on external markers. We present a novel approach based on multi-task Gaussian Processes (MTGP) which enables an efficient combination of both models by simultaneously learning the correlation and temporal delays between markers. The approach is evaluated using datasets acquired from porcine and human studies. We conclude that the prediction accuracy of MTGP is superior to that of existing methods and can be further increased by using multivariate input data. We investigate the dependency of the number of internal training points and the potential for using the marginal likelihood for model selection.
|Title of host publication||2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Number of pages||6|
|Publication status||Published - 01.09.2014|
|Event||2014 24th IEEE International Workshop on Machine Learning for Signal Processing - Reims, France|
Duration: 21.09.2014 → 24.09.2014