A time variant seasonal ARIMA model for lung tumor motion prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
Pages485-488
Number of pages4
Publication statusPublished - 2010 Dec 1
Event15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
Duration: 2010 Feb 42010 Feb 6

Other

Other15th International Symposium on Artificial Life and Robotics, AROB '10
CountryJapan
CityBeppu, Oita
Period10/2/410/2/6

Keywords

  • Lung tumor motion
  • Real time following radiation therapy
  • Seasonal ARIMA
  • Time series prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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  • Cite this

    Ichiji, K., Sakai, M., Homma, N., Takai, Y., & Yoshizawa, M. (2010). A time variant seasonal ARIMA model for lung tumor motion prediction. In Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10 (pp. 485-488)