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A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife

Akartunali, Kerem, Mak-Hau, Vicky and Tran, Thu 2015, A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife, Computers & operations research, vol. 56, pp. 134-150, doi: 10.1016/j.cor.2014.11.009.

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Title A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife
Author(s) Akartunali, Kerem
Mak-Hau, Vicky
Tran, Thu
Journal name Computers & operations research
Volume number 56
Start page 134
End page 150
Total pages 17
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-04
ISSN 0305-0548
Keyword(s) Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Operations Research & Management Science
Computer Science
Engineering
OR in medicine
Integer programming
Heuristics
Radiotherapy treatment planning
Metaheuristics
Lagrangian relaxation
MODULATED ARC THERAPY
TREATMENT PLAN OPTIMIZATION
RADIATION-THERAPY
PROSTATE-CANCER
ALGORITHM
IMPLEMENTATION
SEARCH
IMRT
Summary All rights reserved. In this paper, we propose and study a unified mixed-integer programming model that simultaneously optimizes fluence weights and multi-leaf collimator (MLC) apertures in the treatment planning optimization of VMAT, Tomotherapy, and CyberKnife. The contribution of our model is threefold: (i) Our model optimizes the fluence and MLC apertures simultaneously for a given set of control points. (ii) Our model can incorporate all volume limits or dose upper bounds for organs at risk (OAR) and dose lower bound limits for planning target volumes (PTV) as hard constraints, but it can also relax either of these constraint sets in a Lagrangian fashion and keep the other set as hard constraints. (iii) For faster solutions, we propose several heuristic methods based on the MIP model, as well as a meta-heuristic approach. The meta-heuristic is very efficient in practice, being able to generate dose- and machinery-feasible solutions for problem instances of clinical scale, e.g., obtaining feasible treatment plans to cases with 180 control points, 6750 sample voxels and 18,000 beamlets in 470 seconds, or cases with 72 control points, 8000 sample voxels and 28,800 beamlets in 352 seconds. With discretization and down-sampling of voxels, our method is capable of tackling a treatment field of 8000-64,000cm3, depending on the ratio of critical structure versus unspecified tissues.
Language eng
DOI 10.1016/j.cor.2014.11.009
Field of Research 080702 Health Informatics
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082229

Document type: Journal Article
Collection: School of Information Technology
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