Respiratory Motion Compensation with Relevance Vector Machines

Type of publication:  Artikel in einem Konferenzbericht
Buchtitel: MICCAI 2013, Part II
Serie: Lecture Notes in Computer Science
Band: 8150
Jahr: 2013
Monat: September
Seiten: 108-115
Verlag: Springer
Ort: Nagoya, Japan
Organisation: MICCAI
Notiz: Motion Compensation in Radiosurgery
Querverweis: MICCAI13
DOI: 10.1007/978-3-642-40763-5_14
Abriss: 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.
Autoren: Dürichen, Robert
Wissel, Tobias
Ernst, Floris
Schweikard, Achim
  • dwes_13a.pdf