Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy

Lee, Eva K.; Fox, Tim; Crocker, Ian

doi:10.1016/j.ijrobp.2005.08.023

Cited by 89 publications

(52 citation statements)

References 42 publications

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“…Aleman et al [2] propose a response surface approach and include non-coplanar angles in beam orientation optimization. Lee et al [12] suggests a mixed integer programming (MIP) approach for simultaneously determining an optimal intensity map and optimal beam angles for IMRT delivery. Schreibmann et al [23] propose a hybrid multiobjective evolutionary optimization algorithm for IMRT inverse planning and apply it to the optimization of the number of incident beams, their orientations and intensity profiles.…”

Section: Beam Angle Optimization In Imrt Treatment Planningmentioning

confidence: 99%

IMRT Beam Angle Optimization Using Electromagnetism-Like Algorithm

Rocha

Dias

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. The selection of appropriate beam irradiation directions in radiotherapy -beam angle optimization (BAO) problem -is very important for the quality of the treatment, both for improving tumor irradiation and for better organs sparing. However, the BAO problem is still not solved satisfactorily and, most of the time, beam directions continue to be manually selected in clinical practice which requires many trial and error iterations between selecting beam angles and computing fluence patterns until a suitable treatment is achieved. The objective of this paper is to introduce a new approach for the resolution of the BAO problem, using an hybrid electromagnetism-like algorithm with descent search to tackle this highly non-convex optimization problem. Electromagnetismlike algorithms are derivative-free optimization methods with the ability to avoid local entrapment. Moreover, the hybrid electromagnetism-like algorithm with descent search has a high ability of producing descent directions. A set of retrospective treated cases of head-and-neck tumors at the Portuguese Institute of Oncology of Coimbra is used to discuss the benefits of the proposed algorithm for the optimization of the BAO problem.

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Section: Beam Angle Optimization In Imrt Treatment Planningmentioning

confidence: 99%

IMRT Beam Angle Optimization Using Electromagnetism-Like Algorithm

Rocha

Dias

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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“…The importance weights are determined by the planner based on his/her experience or, for example, by navigating through the ideal Pareto surface and choosing the desired point (e.g., see Craft et al 19 ). For our numerical experiments we have used the values reported in Table 1 which were found by evaluating the dose distribution of the ideal 9 plan. Note that we have considered a zero weight for cord because the dose to cord has been limited to 45…”

Section: Mixed Integer Programming Treatment Planning Modelmentioning

confidence: 99%

“…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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“…Inverse planning applied to radiotherapy is a fruitful ground of research with several important unresolved issues, but until now most of the efforts have been devoted at solving the FMO, and comparatively fewer research effort has been directed to the BAO problem. The BAO problem has been tackled using several different methodologies like response surface approaches [1], derivative-free approaches [2], mixed integer programming approaches [3], simulated annealing [4], particle swarm optimization [5] or genetic algorithms [6]. In this paper we apply DDS algorithm to the BAO problem.…”

Section: Introductionmentioning

confidence: 99%

IMRT Beam Angle Optimization Using Dynamically Dimensioned Search

et al. 2014

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Abstract-In Intensity Modulated Radiation Therapy (IMRT), the selection of appropriate radiation incidence directions is one of the determinants of proper tumor coverage and sparing of healthy tissues. Nevertheless, most of the times, the incidence directions used in clinical practice are equidistant or determined by a trial and error procedure that is very time consuming and does not guarantee the best possible treatment plan. This paper presents some preliminary results considering the application of DDS (Dynamically Dimensioned Search) algorithm to the problem of Beam Angle Optimization (BAO) for IMRT treatment planning. BAO is a problem known by having many local minima. DDS is a derivative-free optimization algorithm, and presents the capability of not getting trapped in these local minima as happens with, for instance, gradient descent based algorithms. In this paper we will briefly describe the problem, the algorithm, and present computational results for clinical cases of head and neck tumors.

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Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy

Cited by 89 publications

References 42 publications

IMRT Beam Angle Optimization Using Electromagnetism-Like Algorithm

IMRT Beam Angle Optimization Using Electromagnetism-Like Algorithm

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

IMRT Beam Angle Optimization Using Dynamically Dimensioned Search

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