Feasibility-seeking and superiorization algorithms applied to inverse treatment planning in radiation therapy

Ran Davidi, Yair Censor, Reinhard W. Schulte, Sarah Geneser, Lei Xing

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

We apply the recently proposed superiorization methodology (SM) to the inverse planning problem in radiation therapy. The inverse planning problem is represented here as a constrained minimization problem of the total variation (TV) of the intensity vector over a large system of linear twosided inequalities. The SM can be viewed conceptually as lying between feasibility-seeking for the constraints and full-fledged constrained minimization of the objective function subject to these constraints. It is based on the discovery that many feasibility-seeking algorithms (of the projection methods variety) are perturbation-resilient, and can be proactively steered toward a feasible solution of the constraints with a reduced, thus superiorized, but not necessarily minimal, objective function value.

Original languageEnglish
Title of host publicationContemporary Mathematics
PublisherAmerican Mathematical Society
Pages83-92
Number of pages10
DOIs
StatePublished - 2015

Publication series

NameContemporary Mathematics
Volume636
ISSN (Print)0271-4132
ISSN (Electronic)1098-3627

Bibliographical note

Publisher Copyright:
© 2015 R. Davidi, Y. Censor, R. W. Schulte, S. Geneser, L. Xing.

ASJC Scopus subject areas

  • General Mathematics

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