Iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation therapy planning

Lim, Gino J.; Choi, Jeong‐Seok; Mohan, Radhe

doi:10.1007/s00291-007-0096-1

Cited by 59 publications

(52 citation statements)

References 33 publications

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“…RAD is designed to study beam selection and fluence optimization and is well positioned to address the current research pursuit of simultaneously optimizing both fluence and beams [2,3,20]. Rather than asking a user to identify beams, users are instead asked to select collections of couch angles and isocenters, which is clinically easier.…”

Section: Problem Managementmentioning

confidence: 99%

Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System

Acosta

Brick

Hanna

et al. 2009

Operations Research and Cyber-Infrastructure

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Optimally designing radiotherapy and radiosurgery treatments to increase the likelihood of a successful recovery from cancer is an important application of operations research. Researchers have been hindered by the lack of academic software that supports head-to-head comparisons of different techniques, and this article addresses the inherent difficulties of designing and implementing an academic treatment planning system. In particular, this article details the algorithms and the software design of Radiotherapy optimAl Design (RAD).

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Section: Problem Managementmentioning

confidence: 99%

Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System

Acosta

Brick

Hanna

et al. 2009

Operations Research and Cyber-Infrastructure

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“…Lim et al (2008) developed an LP-based iterative beam angle elimination algorithm to obtain promising beam angles with optimized fluence maps. Lim et al (2014) examined the strengths and weaknesses of six optimization methods for selecting beam angles: branch and bound, simulated annealing (SA), genetic algorithms (GA), nested partitions (NP), branch and prune (BP), and local neighborhood search (LNS).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several researchers have proposed heuristics aimed at eliminating unpromising choices. Lim et al (2008), for example, developed an iterative scheme that removes angles based on scores derived from the LP solution of the original problem. Here, we discuss a heuristic that iteratively eliminates unpromising angles in the LP solution until the remaining problem is manageable as an IP.…”

Section: Lp Rounding Heuristicmentioning

confidence: 99%

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Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles

Lin

Lim

Bard

2016

European Journal of Operational Research

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“…The BAO has sometimes been modeled as a mixed integer program (MIP) (e.g., [9][10][11] ). Since the resultant MIP is large-scale, heuristics have often been used to obtain good solutions in a reasonable amount of time.…”

Section: Introductionmentioning

confidence: 99%

MO‐A‐137‐03: Effective Heuristic Cuts for Beam Angle Optimization in Radiation Therapy

Yarmand

Craft

2013

Medical Physics

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2 AbstractPurpose: To improve the solution efficiency (i.e., reducing computation time while obtaining highquality treatment plans) for the beam angle optimization problem (BAO) in radiation therapy treatment planning. Methods:We formulate BAO as a mixed integer programming problem (MIP) whose solution gives the optimal beam orientation as well as optimal beam intensity map. We propose and investigate two novel heuristic approaches to reduce the computation time of the resultant MIP. One is a family of heuristic cuts based on the observation that the number of candidate beams is usually much larger than the number of beams used in the treatment plan, and therefore, it is less likely that "adjacent" beams are simultaneously used in the optimal treatment plan. The proposed cuts, referred to as "neighbor cuts", force the optimization system to choose one or a few beams from any set of adjacent beams. As a result, the search space and the computation time are reduced considerably. The second heuristic is a beam elimination scheme to eliminate beams with an insignificant contribution to deliver the dose to the tumor in the ideal plan in which all potential beams can be used simultaneously. Both heuristics can be added to any MIP formulation for BAO including the cases of coplanar/noncoplanar beams for intensity modulated radiation/proton therapy (IMRT and IMPT) and stereotactic body radiation therapy (SBRT). For the numerical experiments a clinical liver case (IMRT and SBRT) with 34 coplanar beams were considered. Results:We first solved the corresponding MIP without the heuristics and recorded the optimal solution and the computation time. Then we incorporated the heuristics into the MIP and resolved it. Our results show that both heuristics reduce the computation time considerably while obtaining high-quality treatment plans. Conclusion:This research incorporates two observations to improve the efficiency of the solution technique for BAO drastically: optimal beam configurations are typically a sparse set of well-spaced beams and optimal beams have a relatively large contribution to deliver the dose to the tumor in the ideal plan.3

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Iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation therapy planning

Cited by 59 publications

References 33 publications

Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System

Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System

Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles

MO‐A‐137‐03: Effective Heuristic Cuts for Beam Angle Optimization in Radiation Therapy

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