Prediction of Tumour Motion using Interacting Multiple Model Filter

Devi Putra, LOlivier C.L. Haas, John A. Mills, Keith J. Burnham

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

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    Abstract

    Accurate prediction of tumour motion - over a prescribed time - is essential for enabling adaptive radiotherapy. The prediction time horizon is determined by measurement processing time, predictor algorithm processing time and the time-to-adapt radiation delivery. A trade off between the predictor algorithm complexity and the required prediction time horizon, therefore, has to be made. This paper proposes an interacting multiple model (IMM) filter and two Kalman filters to predict 0.2 s ahead respiratory tumour motions. The performance of the filters is evaluated using 333 traces of 4 minutes respiratory motions for 24 adult patients. The average RMSE of the IMM filter and the best Kalman filter with 5Hz measurements rate are 0.98 mm and 1.1 mm, which are improvements of 38% and 30% compared to use of measurements only.
    Original languageEnglish
    Title of host publicationAdvances in Medical, Signal and Information Processing, 2006. MEDSIP 2006. IET 3rd International Conference On
    PublisherIET
    Pages1-4
    Number of pages4
    ISBN (Print)978-0-86341-658-3
    Publication statusPublished - Jul 2006
    Event3rd IET International Conference on Advances in Medical, Signal and Information Processing - Glasgow, United Kingdom
    Duration: 17 Jul 200619 Jul 2006

    Conference

    Conference3rd IET International Conference on Advances in Medical, Signal and Information Processing
    Country/TerritoryUnited Kingdom
    CityGlasgow
    Period17/07/0619/07/06

    Bibliographical note

    e Framework 6 European integrated project Methods and Advanced Equipment for Simulation and Treatment in Radiation Oncology (MAESTRO) CE LSHC CT 2004 503564.

    Keywords

    • tumour motion prediction methods
    • Kalman
    • filter
    • interacting multiple model filter
    • medical systems

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