A unified approach for respiratory motion prediction and correlation with multi-task Gaussian Processes

R. Durichen, T. Wissel, F. Ernst, M. A. F. Pimentel, D. A. Clifton, A. Schweikard

Abstract

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.
Original languageEnglish
Title of host publication2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
PublisherIEEE
Publication date01.09.2014
Pages1-6
ISBN (Print)978-147993694-6
DOIs
Publication statusPublished - 01.09.2014
Event2014 24th IEEE International Workshop on Machine Learning for Signal Processing - Reims, France
Duration: 21.09.201424.09.2014

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