Respiratory motion prediction for adaptive radiotherapy

Abdelhamid Sahih, Olivier C.L. Haas, John H. Goodband, Devi Putra, John A. Mills, Keith J. Burnham

    Research output: Contribution to conferencePaper

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    Abstract

    Adaptive and image guided radiation therapy aims to adapt radiotherapy treatment delivery to tumour and patient motion. To achieve this it is necessary to predict trajectory evolution for a time horizon long enough to facilitate the required changes in radiation delivery. This paper presents a new comparative study between different approaches, namely interactive multiple models (IMM), Kalman filter (KF) assuming constant velocity (CV) and constant acceleration (CA) and adaptive bilinear filter (ABF) models and two structures of neural network (NN)
    Original languageEnglish
    Publication statusPublished - Nov 2006

    Bibliographical note

    This paper is free to view on the Université Henri Poincaré de Nancy IAR
    website at: http://www.acd-2006.cran.uhp-nancy.fr/Files/IAR/p14.pdf

    Keywords

    • : Adaptive radiotherapy
    • Bilinear filter
    • Respiratory modelling
    • Prediction
    • methods
    • Kalman filter
    • Neural networks.

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