On improving physical selectivity in the treatment of cancer: A systems modelling and optimisation approach

Olivier C.L. Haas; Keith J. Burnham; John A. Mills

doi:10.1016/S0967-0661(97)10029-6

On improving physical selectivity in the treatment of cancer: A systems modelling and optimisation approach

Olivier C.L. Haas, Keith J. Burnham, John A. Mills

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Abstract

This paper presents the results arising from a practical implementation of a novel hybrid optimisation scheme, used to solve the inverse problem in radiotherapy treatment planning (RTP). A matrix-based beam model which has been developed making use of a control systems modelling approach is used, together with a hybrid optimisation scheme. Patient-specific compensator profiles are deduced from the intensity modulated beam profiles obtained from the hybrid scheme, with use being made of an exponential attenuation factor coupled with a point spread convolution function to account for the scatter in the compensator. A good agreement between the predicted and actual conformational distributions is achieved.

Original language	English
Pages (from-to)	1739–1745
Journal	Control Engineering Practice
Volume	5
Issue number	12
DOIs	https://doi.org/10.1016/S0967-0661(97)10029-6
Publication status	Published - Dec 1997

Bibliographical note

© IFAC 1997. This work is posted here by permission of IFAC for your personal use. Not for
distribution. The original version was published in ifac-papersonline.net, DOI
10.1016/S0967-0661(97)10029-6

Keywords

Genetic algorithms
geometric approaches
inverse problem
iterative methods
least squares methods
multiobjective optimisation
physics
systems methodology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/S0967-0661(97)10029-6

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title = "On improving physical selectivity in the treatment of cancer: A systems modelling and optimisation approach",

abstract = "This paper presents the results arising from a practical implementation of a novel hybrid optimisation scheme, used to solve the inverse problem in radiotherapy treatment planning (RTP). A matrix-based beam model which has been developed making use of a control systems modelling approach is used, together with a hybrid optimisation scheme. Patient-specific compensator profiles are deduced from the intensity modulated beam profiles obtained from the hybrid scheme, with use being made of an exponential attenuation factor coupled with a point spread convolution function to account for the scatter in the compensator. A good agreement between the predicted and actual conformational distributions is achieved. ",

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N2 - This paper presents the results arising from a practical implementation of a novel hybrid optimisation scheme, used to solve the inverse problem in radiotherapy treatment planning (RTP). A matrix-based beam model which has been developed making use of a control systems modelling approach is used, together with a hybrid optimisation scheme. Patient-specific compensator profiles are deduced from the intensity modulated beam profiles obtained from the hybrid scheme, with use being made of an exponential attenuation factor coupled with a point spread convolution function to account for the scatter in the compensator. A good agreement between the predicted and actual conformational distributions is achieved.

AB - This paper presents the results arising from a practical implementation of a novel hybrid optimisation scheme, used to solve the inverse problem in radiotherapy treatment planning (RTP). A matrix-based beam model which has been developed making use of a control systems modelling approach is used, together with a hybrid optimisation scheme. Patient-specific compensator profiles are deduced from the intensity modulated beam profiles obtained from the hybrid scheme, with use being made of an exponential attenuation factor coupled with a point spread convolution function to account for the scatter in the compensator. A good agreement between the predicted and actual conformational distributions is achieved.

KW - Genetic algorithms

KW - geometric approaches

KW - inverse problem

KW - iterative methods

KW - least squares methods

KW - multiobjective optimisation

KW - physics

KW - systems methodology

U2 - 10.1016/S0967-0661(97)10029-6

DO - 10.1016/S0967-0661(97)10029-6

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JO - Control Engineering Practice

JF - Control Engineering Practice

IS - 12

ER -

On improving physical selectivity in the treatment of cancer: A systems modelling and optimisation approach

Abstract

Bibliographical note

Keywords

UN SDGs

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