Large scale optimization of beam weights under dose-volume restrictions

Langer, Mark; Brown, Richard; Urie, Marsha; Leong, J.; Stracher, Michael A.; Shapiro, Jeremy F.

doi:10.1016/0360-3016(90)90413-e

Cited by 111 publications

(70 citation statements)

References 19 publications

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“…Later works involving LP include Hodes (1974), Morrill et al (1990), Langer et al (1990) and Rosen et al (1991). The linear models vary in their definition of both objectives and constraints.…”

Section: The Vectors X Lmentioning

confidence: 99%

Efficient schemes for robust IMRT treatment planning

Ólafsson

Wright

2006

Phys. Med. Biol.

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We use robust optimization techniques to formulate an IMRT treatment planning problem in which the dose matrices are uncertain, due to both dose calculation errors and interfraction positional uncertainty of tumour and organs. When the uncertainty is taken into account, the original linear programming formulation becomes a second-order cone program. We describe a novel and efficient approach for solving this problem, and present results to compare the performance of our scheme with more conventional formulations that assume perfect knowledge of the dose matrix.

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Section: The Vectors X Lmentioning

confidence: 99%

Efficient schemes for robust IMRT treatment planning

Ólafsson

Wright

2006

Phys. Med. Biol.

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“…These values can be set to approximate dose volume histogram constraints (see Langer et al, 1990), or other piecewise linearizations can be used to implement so-called CVAR constraints (Romeijn et al, 2006), or standard infinity or L 1 norm constraints. Further details on these choices are available in Lim et al (2006).…”

Section: Computational Modelmentioning

confidence: 99%

Sampling issues for optimization in radiotherapy

Ferris¹,

Einarsson²,

Jiang

et al. 2006

Ann Oper Res

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“…In addition to these data reduction techniques, rather than using the complete sets of organ structures, we follow an interactive approach that tries to determine promising beam angles based on an α% sampling of the sensitive structure as outlined in [18]. The sampling scheme is also noted elsewhere [16].…”

Section: Data Samplingmentioning

confidence: 99%

Optimization Tools for Radiation Treatment Planning in Matlab

Ferris

Lim

Shepard

2005

International Series in Operations Research &Amp; Management Science

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This paper describes a suite of optimization tools for radiation treatment planning within the Matlab programming environment. The data included with these tools was computed for real patient cases using a Monte Carlo dose engine. The formulation of a series of optimization models is described that utilizes this data within a modeling system. Furthermore, visualization techniques are provided that assist in validating the quality of each solution. The versatility and utility of the tools are shown using a sequence of optimization techniques designed to generate a practical solution. These tools and the associated data are available for download from www.cs.wisc.edu/∼ferris/3dcrt.

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Large scale optimization of beam weights under dose-volume restrictions

Cited by 111 publications

References 19 publications

Efficient schemes for robust IMRT treatment planning

Efficient schemes for robust IMRT treatment planning

Sampling issues for optimization in radiotherapy

Optimization Tools for Radiation Treatment Planning in Matlab

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