Efficient SVR model update approaches for respiratory motion prediction

Type of publication:  Artikel in einem Konferenzbericht
Buchtitel: 11. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirugie (CURAC)
Jahr: 2012
Monat: November 15-16
Organisation: CURAC
Ort: Düsseldorf, Germany
Notiz: Motion Compensation in Radiosurgery
Querverweis: CURAC12
Abriss: In order to successfully ablate moving tumours in robotic radiosurgery, respiratory motion prediction is needed to compensate time delays. In this context, recent studies revealed a high potential of support vector regression (SVR). However, high computational cost is one major drawback, particularly caused by batch mode training. We evaluate two approaches to reduce the update rate as well as computation time, while keeping a low prediction error. The update rules are either based on information about the respiratory phase or based on the current prediction error. An evaluation on patient data sets revealed that the second approach on average decreases computation time by 88.53% compared to a batch mode implementation. The prediction error increased by 0.3%, hence indicating enhanced efficiency.
Autoren: Dürichen, Robert
Wissel, Tobias
Schweikard, Achim
  • dws_12.pdf