Respiratory motion prediction for adaptive radiotherapy

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

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 language	English
Publication status	Published - 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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HaasnancycombFinal published version, 313 KB

http://www.acd-2006.cran.uhp-nancy.fr/

MAESTRO: Methods and Advanced Equipment for Simulation and Treatment in Radiation Oncology
haas, O., Burnham, K., Skworcow, P. & Sahih, A.
1/05/04 → 30/04/09
Project: Research

Cite this

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title = "Respiratory motion prediction for adaptive radiotherapy",

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)",

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T1 - Respiratory motion prediction for adaptive radiotherapy

AU - Sahih, Abdelhamid

AU - Haas, Olivier C.L.

AU - Goodband, John H.

AU - Putra, Devi

AU - Mills, John A.

AU - Burnham, Keith J.

N1 - 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

PY - 2006/11

Y1 - 2006/11

N2 - 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)

AB - 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)

KW - : Adaptive radiotherapy

KW - Bilinear filter

KW - Respiratory modelling

KW - Prediction

KW - methods

KW - Kalman filter

KW - Neural networks.

M3 - Paper

ER -

Respiratory motion prediction for adaptive radiotherapy

Abstract

Bibliographical note

Keywords

Access to Document

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Projects

MAESTRO: Methods and Advanced Equipment for Simulation and Treatment in Radiation Oncology

Cite this