Study on predicting respiratory motion with support vector regression
KANG Kailian
TONG Lei
WAN Weiquan
SUN Haitao
CHEN Chaomin
Abstract：The target is usually tracked in real time at thoracic and abdominal radiotherapy due to the effect of respiratory motion,the prediction is necessary to compensate the system latency.A prediction method based on support vector regression (SVR) was pro-posed, a part of historical data for training was selected,and then the output was calculated according to the training model when there was a new sequence.Furthermore, the training set would be dynamically updated and the accurate online support vector regression model was achieved.The experiment selected seven respiratory motion data ;the model was trained by on-line and off-line method, then prediction was carried out.The mean absolute error was 0.42 mm, 0.30 mm, respectively.The respiratory motion is accurately described by the online accurate support vector regression,and the results with high precision can satisfy practical application.
Keywords：RadiotherapyRespiratory motionSupport vector regressionPrediction algorithmKernel function
Publication Date：2018-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 132-137 )