A Unified Approach For Respiratory Motion Prediction and Correlation with Multi-Task Gaussian Processes

Type of publication:  Artikel in einem Konferenzbericht
Buchtitel: IEEE International Workshop on Machine Learning for Signal Processing
Jahr: 2014
Monat: September
Seiten: 1--6
Ort: Reims, France
Notiz: Motion Compensation in Radiosurgery
DOI: 10.1109/MLSP.2014.6958895
Abriss: 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.
Autoren: Dürichen, Robert
Wissel, Tobias
Ernst, Floris
Pimentel, Marco A. F.
Clifton, David A.
Schweikard, Achim
  • dwe_14.pdf