Binary level-set shape optimization model and algorithm for volumetric modulated arc therapy in radiotherapy treatment

Li Tien Cheng, Bin Dong, Chunhua Men, Xun Jia, Steve Jiang

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We introduce, in this paper, a variational framework for the construction of improved treatment plans in volumetric modulated arc therapy for cancer treatment. This framework consists of a shape optimization component, from the molding of beam shapes, as well as strong constraints, from the equipment involved, on both beam shapes and intensities. We apply the binary level-set method, to handle complex shape topologies, and the fast sweeping method, to handle beam intensity constraints. The result is a fast, flow-based algorithm, in a simplified setting, that guarantees energy decrease. We include numerical simulations of clinical cases to demonstrate the efficacy of the approach.

Original languageEnglish (US)
JournalSIAM Journal on Scientific Computing
Volume35
Issue number6
DOIs
StatePublished - 2013

Fingerprint

Radiotherapy
Oncology
Shape Optimization
Shape optimization
Optimization Model
Level Set
Molding
Therapy
Optimization Algorithm
Arc of a curve
Topology
Binary
Computer simulation
Sweeping
Level Set Method
Efficacy
Cancer
Numerical Simulation
Decrease
Energy

Keywords

  • Cancer radiotherapy
  • Fast sweeping method
  • Level-set method
  • Shape optimization
  • Variational method
  • Volumetric modulated arc therapy

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics

Cite this

Binary level-set shape optimization model and algorithm for volumetric modulated arc therapy in radiotherapy treatment. / Cheng, Li Tien; Dong, Bin; Men, Chunhua; Jia, Xun; Jiang, Steve.

In: SIAM Journal on Scientific Computing, Vol. 35, No. 6, 2013.

Research output: Contribution to journalArticle

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AB - We introduce, in this paper, a variational framework for the construction of improved treatment plans in volumetric modulated arc therapy for cancer treatment. This framework consists of a shape optimization component, from the molding of beam shapes, as well as strong constraints, from the equipment involved, on both beam shapes and intensities. We apply the binary level-set method, to handle complex shape topologies, and the fast sweeping method, to handle beam intensity constraints. The result is a fast, flow-based algorithm, in a simplified setting, that guarantees energy decrease. We include numerical simulations of clinical cases to demonstrate the efficacy of the approach.

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