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Med Phys. 2015 Mar;42(3):1367-77. doi: 10.1118/1.4908224.

Optimization approaches to volumetric modulated arc therapy planning.

Author information

1
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
2
Department of Medical Physics and Department of Radiation Oncology, Aarhus University Hospital, Aarhus C DK-8000, Denmark.
3
Department of Medical Physics in Radiation Oncology, German Cancer Research Center, Heidelberg D-69120, Germany.
4
RaySearch Laboratories, Stockholm SE-111 34, Sweden.
5
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556.
6
Department of Radiation Oncology, Stanford University, Stanford, California 94305.
7
Department of Research, Elekta, Maryland Heights, Missouri 63043.
8
Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom.
9
Department of Mathematics, North Carolina State University, Raleigh, North Carolina 27695.
10
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332.
11
Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, Kansas 67260.

Abstract

Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.

PMID:
25735291
PMCID:
PMC5148175
DOI:
10.1118/1.4908224
[Indexed for MEDLINE]
Free PMC Article

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