The Study on Predicting Respiratory Motion with Support Vector Regression

DOI

10.2991/cmsa-18.2018.47How to use a DOI?

Keywords

radiotherapy; support vector regression; respiratory motion prediction; kernel function

Abstract

Objective: 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. Method: This paper proposed a prediction method based on support vector regression, it dynamically updates the training set and achieves the accurate online support vector regression. Result: The experiment selected seven respiratory motion data, using online model trained and predicted. The mean absolute error was 0.30mm. Conclusion: The online accurate support vector regression described respiratory motion accurately, and the results with high precision can be satisfied in practical application.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lei Tong
AU  - Chaomin Chen
AU  - Kailian Kang
AU  - Zihai Xu
PY  - 2018/04
DA  - 2018/04
TI  - The Study on Predicting Respiratory Motion with Support Vector Regression
BT  - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
PB  - Atlantis Press
SP  - 204
EP  - 207
SN  - 1951-6851
UR  - https://doi.org/10.2991/cmsa-18.2018.47
DO  - 10.2991/cmsa-18.2018.47
ID  - Tong2018/04
ER  -