Multiobjective inverse planning for intensity modulated radiotherapy with constraint-free gradient-based optimization algorithms

We consider the behaviour of the limited memory L-BFGS algorithm as a representative constraint-free gradient-based algorithm which is used for multiobjective (MO) dose optimization for intensity modulated radiotherapy (IMRT). Using a parameter transformation, the positivity constraint problem of ne...

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Bibliographic Details
Published in:Physics in medicine & biology Vol. 48; no. 17; p. 2843
Main Authors: Lahanas, Michael, Schreibmann, Eduard, Baltas, Dimos
Format: Journal Article
Language:English
Published: England 07.09.2003
Subjects:
Algorithms
Brain Neoplasms - physiopathology
Brain Neoplasms - radiotherapy
Humans
Linear Energy Transfer
Male
Neoplasms - physiopathology
Neoplasms - radiotherapy
Prostatic Neoplasms - physiopathology
Prostatic Neoplasms - radiotherapy
Quality Control
Radiation Protection - methods
Radiometry - methods
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted - methods
Radiotherapy, Conformal - methods
Reproducibility of Results
Sensitivity and Specificity
ISSN:0031-9155
Online Access:Get more information
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Summary:We consider the behaviour of the limited memory L-BFGS algorithm as a representative constraint-free gradient-based algorithm which is used for multiobjective (MO) dose optimization for intensity modulated radiotherapy (IMRT). Using a parameter transformation, the positivity constraint problem of negative beam fluences is entirely eliminated: a feature which to date has not been fully understood by all investigators. We analyse the global convergence properties of L-BFGS by searching for the existence and the influence of possible local minima. With a fast simulated annealing (FSA) algorithm we examine whether the L-BFGS solutions are globally Pareto optimal. The three examples used in our analysis are a brain tumour, a prostate tumour and a test case with a C-shaped PTV. In 1% of the optimizations global convergence is violated. A simple mechanism practically eliminates the influence of this failure and the obtained solutions are globally optimal. A single-objective dose optimization requires less than 4 s for 5400 parameters and 40000 sampling points. The elimination of the problem of negative beam fluences and the high computational speed permit constraint-free gradient-based optimization algorithms to be used for MO dose optimization. In this situation, a representative spectrum of possible solutions is obtained which contains information such as the trade-off between the objectives and range of dose values. Using simple decision making tools the best of all the possible solutions can be chosen. We perform an MO dose optimization for the three examples and compare the spectra of solutions, firstly using recommended critical dose values for the organs at risk and secondly, setting these dose values to zero.
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ISSN:0031-9155
DOI:10.1088/0031-9155/48/17/308