A stochastic approach to full inverse treatment planning for charged-particle therapy
type: Item
instance of: scholarly article
title: A stochastic approach to full inverse treatment planning for charged-particle therapy
zbMATH Open document ID: 1453.90210
author: Marc C. Robini
author: Feng Yang
author: Yuemin Zhu
published in: Journal of Global Optimization
publication date: 2020-08-07T00:00:00Z
review text: The paper deals with an optimization problem called full inverse treatment planning (full ITP). It deals with finding a radiotherapy treatment plan to tumors that accurately matches the dose prescribed to the tumor while sparing nearby healthy structures. The full ITP is a quite complex optimization problem, full of local minima. For this reason, in the past, research was focused on its simplified version, in which only a reduced set of variables is optimized, compared to the full ITP. The present article tries to fill this gap in the literature by dealing directly with the full ITP. This is achieved by proposing a variation of simulation annealing, which takes into account specific features of full ITP, without altering global convergence properties of the algorithm. Numerical experiments show the effectiveness of the methodology proposed in the article.
reviewed by: Giorgio Gnecco
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7229502
zbMATH Keywords: simulated annealing
zbMATH Keywords: hadrontherapy
zbMATH Keywords: Markov chain Monte Carlo (MCMC)
zbMATH Keywords: inverse treatment planning
zbMATH Keywords: charged-particle therapy
describes a project that uses: GEANT4
MaRDI profile type: Publication
full work available at URL: s10898-020-00902-2
OpenAlex ID: W3011080380
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DOI: 10.1007/S10898-020-00902-2
description: scientific article; zbMATH DE number 7229502