Respiratory motion prediction for tumor following radiotherapy by using time-variant seasonal autoregressive techniques

Kei Ichiji, Noriyasu Homma, Masao Sakai, Yoshihiro Takai, Yuichiro Narita, Mokoto Abe, Norihiro Sugita, Makoto Yoshizawa

研究成果: Conference contribution

抄録

We develop a new prediction method of respiratory motion for accurate dynamic radiotherapy, called tumor following radiotherapy. The method is based on a time-variant seasonal autoregressive (TVSAR) model and extended to further capture time-variant and complex nature of various respiratory patterns. The extended TVSAR can represent not only the conventional quasi-periodical nature, but also the residual components, which cannot be expressed by the quasi-periodical model. Then, the residuals are adaptively predicted by using another autoregressive model. The proposed method was tested on 105 clinical data sets of tumor motion. The average errors were 1.28 ± 0.87 mm and 1.75 ± 1.13 mm for 0.5 s and 1.0 s ahead prediction, respectively. The results demonstrate that the proposed method can outperform the state-of-the-art prediction methods.

本文言語English
ホスト出版物のタイトル2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
ページ6028-6031
ページ数4
DOI
出版ステータスPublished - 2012 12 14
イベント34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
継続期間: 2012 8 282012 9 1

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period12/8/2812/9/1

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

フィンガープリント 「Respiratory motion prediction for tumor following radiotherapy by using time-variant seasonal autoregressive techniques」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル