Respiratory Motion Compensation with Relevance Vector Machines

Robert Dürichen, Tobias Wissel, Floris Ernst, Achim Schweikard


In modern robotic radiation therapy, tumor movements due to respiration can be compensated. The accuracy of these methods can be increased by time series prediction of external optical surrogates. An algorithm based on relevance vector machines (RVM) is introduced. We evaluate RVM with linear and nonlinear basis functions on a real patient data set containing 304 motion traces and compare it with a wavelet based least mean square algorithm (wLMS), the best algorithm for this data set so far. Linear RVM outperforms wLMS significantly and increases the prediction accuracy for 80.3 % of the data. We show that real time prediction is possible in case of linear RVM and discuss how the predicted variance can be used to construct promising hybrid algorithms, which further reduce the prediction error.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2013
EditorsKensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
Number of pages8
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Publication date01.09.2013
ISBN (Print)978-3-642-40762-8
ISBN (Electronic)978-3-642-40763-5
Publication statusPublished - 01.09.2013
EventWorkshop on Breast Image Analysis - In conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013)
- Toyoda Auditrium, Nagoya University, Nagoya, Japan
Duration: 22.09.201326.09.2013


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