Modeling and uncertainty quantification of motion of lung tumors for image guided radiation therapy

Ravi Kumar, Tarunraj Singh, Puneet Singla

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

Abstract

Target localization is a key issue in the image guided radiation therapy procedures for treating tumors in thorax and abdomen. Breathing induced tumor motion necessitates larger margins during radiation therapy planning which may be harmful for healthy tissue surrounding the tumor. Large sampling time in data acquisition and latencies involved in real time imaging systems and tracking system pose a significant challenge to target localization. A framework based on pulmonary mechanics is developed to predict and precisely track the breathing induced motion of lung tumor to direct the tracking system to an estimated position instead of an observed one. A hybrid approach based on the correlation of real-time imagery data of internal markers and easy to measure external respiratory signals like flow readings etc, is proposed to support dynamic radiation therapy procedures. Issues related to reliability of proposed model predictions in the presence of parametric uncertainty are explored using Polynomial Chaos Expansion.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
PublisherIEEE Computer Society
Pages1254-1259
Number of pages6
ISBN (Print)9781424474264
DOIs
StatePublished - 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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