Earticle

초록

영어

Motion of thoracic and abdominal tumors induced by respiratory motion often exceeds more than one centimeter which can compromise dose conformality significantly. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to a tumor with minimal normal tissue exposure, by compensating for the tumor motion in real time. This requires prediction of respiratory motion to estimate the respiratory movement that has occurred during the system latency due to measurement and control. One of the most successful models for predicting respiratory motion is the local circular motion (LCM) model. It characterizes the local respiratory behavior with a circular motion in an augmented plane and captures the natural evolution of respiratory motion. In this paper, we utilize the first and second-order extended Kalman filters based on LCM model for predicting respiratory motion. We also optimize the parameters of the extended Kalman filters for each trace in an attempt to improve prediction accuracy. Numerical experiments are performed to evaluate and compare prediction accuracy of four different prediction schemes.

목차

Abstract
 1. Introduction
 2. LCM Model and Prediction of Respiratory Motion
 3. Numerical Experiments
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Bo-Hwan Jung [ Department of Information and Communication, Yongin Songdam College, Yongin, Republic of Korea, School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea ]
• Byoung-Hee Kim [ Department of Information and Communication, Yongin Songdam College, Yongin, Republic of Korea, School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea ]
• Sun-Mog Hong [ Department of Information and Communication, Yongin Songdam College, Yongin, Republic of Korea, School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605