A hierarchical modeling algorithm for respiration induced tumor motion modeling

Cheng Jin, Puneet Singla, Tarunraj Singh

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


This paper presents a hierarchical approach to model tumor motion dynamics for image guided radiation therapy. Respiration induced tumor motion poses a significant challenge for using radiation therapy for tumors in the thorax and abdomen areas of the patients. The continuous motion of the tumor during radiation therapy can degrade the accuracy of radiation delivery and cause adverse effect on the surrounding healthy tissues. The proposed approach uses a two-stage modeling architecture-global modeling followed by local modeling to capture the dynamics of the tumor motion. The key idea of our proposed approach is based on an averaging method, which is able to merge arbitrary local models to a unbiased globally smooth model. Furthermore, the unscented Kalman filter is used to predict the tumor motion based on the identified nonlinear model and making use of respiratory motion observations in real-time. The proposed approach is tested by using numerical and experimental data. Our results show the proposed approach has a potential to achieve long-term tumor motion prediction with a sub-millimeter accuracy.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
Number of pages6
StatePublished - Nov 26 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2012 American Control Conference, ACC 2012
CityMontreal, QC

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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