@inproceedings{43d3b4358e7843648c65e33f27b40333,
title = "A time variant seasonal ARIMA model for lung tumor motion prediction",
abstract = "We propose a prediction method of lung tumor motion for real-time tumor following radiation therapy. An essential core of the method is a model building of time variant nature of the lung tumor motion. The method is based on a seasonal ARIMA model with an estimator of the time variant nature. The estimator provides the time variant period of the lung tumor motion by using a correlation analysis. The time variant SARIMA model can then predict complex lung motion by using the estimated period. The proposed method achieved highly accurate prediction of the average error 0.820±0.669[mm] at 0.5[sec] ahead prediction. This result is superior to other conventional methods at short- or mid-term prediction.",
keywords = "Lung tumor motion, Real time following radiation therapy, Seasonal ARIMA, Time series prediction",
author = "K. Ichiji and M. Sakai and N. Homma and Y. Takai and M. Yoshizawa",
year = "2010",
language = "English",
isbn = "9784990288044",
series = "Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10",
pages = "485--488",
booktitle = "Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10",
note = "15th International Symposium on Artificial Life and Robotics, AROB '10 ; Conference date: 04-02-2010 Through 06-02-2010",
}