190 A medical knowledge based system for the selection of beam orientations in intensity-modulated radiation therapy (IMRT)

Liu, Xing; Pugachev, Andrei; Li, J.; Le, Q.; Donaldson, S.; Goffinet, Don R.; Hancock, Steven; Boyer, Arthur L.

doi:10.1016/s0360-3016(99)90208-0

Cited by 7 publications

(4 citation statements)

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“…The algorithm is being generalized to a realistic 3D system and used for building a knowledge based system for the selection of beam orientations. 23…”

Section: Discussionmentioning

confidence: 99%

Beam orientation optimization in intensity‐modulated radiation treatment planning

2000

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Beam direction optimization is an important problem in radiation therapy. In intensity modulated radiation therapy (IMRT), the difficulty for computer optimization of the beam directions arises from the fact that they are coupled with the intensity profiles of the incident beams. In order to obtain the optimal incident beam directions using iterative or stochastic methods, the beam profiles ought to be optimized after every change of beam configuration. In this paper we report an effective algorithm to optimize gantry angles for IMRT. In our calculation the gantry angles and the beam profiles (beamlet weights) were treated as two separate groups of variables. The gantry angles were sampled according to a simulated annealing algorithm. For each sampled beam configuration, beam profile calculation was done using a fast filtered backprojection (FBP) method. Simulated annealing was also used for beam profile optimization to examine the performance of the FBP for beam orientation optimization. Relative importance factors were incorporated into the objective function to control the relative importance of the target and the sensitive structures. Minimization of the objective function resulted in the best possible beam orientations and beam profiles judged by the given objective function. The algorithm was applied to several model problems and the results showed that the approach has potential for IMRT applications.

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“…The algorithm is being generalized to a realistic 3D system and used for building a knowledge based system for the selection of beam orientations. 23…”

Section: Discussionmentioning

confidence: 99%

Beam orientation optimization in intensity‐modulated radiation treatment planning

2000

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“…Some studies have shown that a specially designed initialization helps the algorithm explore the search space more efficiently (Hu et al 2004). It is expected that the performance of PSO for beam angle optimization could be improved by a problem-dependent initialization with the guidance of some prior knowledge such as the computer-calculated beam scores (Gokhale et al 1994, Pugachev andXing 2002) and expert knowledge (Xing et al 1999, Li et al 2004.…”

Section: Discussionmentioning

confidence: 99%

A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning

Li¹,

Yao²,

Yao³

et al. 2005

Phys. Med. Biol.

101

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Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.

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“…Several investigators have studied beam orientation optimization for IMRT (Bortfeld and Schlegel 1993, Xing et al 1999, Pugachev and Xing 2002, Stein et al 1997, Rowbottom et al 2001, Djajaputra et al 2003, Das et al 2003, Wang et al 2004, D'Souza et al 2004, Hou et al 2003, Li et al 2004, Lee et al 2006. To make this problem more tractable, some approximation or compromising strategies for identifying the most preferred beam orientations were employed.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming

et al. 2006

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The purpose of this study is to extend an algorithm proposed for beam orientation optimization in classical conformal radiotherapy to intensity-modulated radiation therapy (IMRT) and to evaluate the algorithm's performance in IMRT scenarios. In addition, the effect of the candidate pool of beam orientations, in terms of beam orientation resolution and starting orientation, on the optimized beam configuration, plan quality and optimization time is also explored. The algorithm is based on the technique of mixed integer linear programming in which binary and positive float variables are employed to represent candidates for beam orientation and beamlet weights in beam intensity maps. Both beam orientations and beam intensity maps are simultaneously optimized in the algorithm with a deterministic method. Several different clinical cases were used to test the algorithm and the results show that both target coverage and critical structures sparing were significantly improved for the plans with optimized beam orientations compared to those with equi-spaced beam orientations. The calculation time was less than an hour for the cases with 36 binary variables on a PC with a Pentium IV 2.66 GHz processor. It is also found that decreasing beam orientation resolution to 10 degrees greatly reduced the size of the candidate pool of beam orientations without significant influence on the optimized beam configuration and plan quality, while selecting different starting orientations had large influence. Our study demonstrates that the algorithm can be applied to IMRT scenarios, and better beam orientation configurations can be obtained using this algorithm. Furthermore, the optimization efficiency can be greatly increased through proper selection of beam orientation resolution and starting beam orientation while guaranteeing the optimized beam configurations and plan quality.

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190 A medical knowledge based system for the selection of beam orientations in intensity-modulated radiation therapy (IMRT)

Cited by 7 publications

References 0 publications

Beam orientation optimization in intensity‐modulated radiation treatment planning

Beam orientation optimization in intensity‐modulated radiation treatment planning

A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning

Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming

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