Respiratory motion prediction for adaptive radiotherapy

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

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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

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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.

Cite this

Sahih, A., Haas, O. C. L., Goodband, J. H., Putra, D., Mills, J. A., & Burnham, K. J. (2006). Respiratory motion prediction for adaptive radiotherapy.