Controlling motion prediction errors in radiotherapy with relevance vector machines.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
May 16, 2014
Abstract
PURPOSE: Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms.