Selection and determination of beam weights based on genetic algorithms for conformal radiotherapy treatment planning

Abstract

A genetic algorithm has been used to optimize the selection of beam weights for external beam three-dimensional conformal radiotherapy treatment planning. A fitness function is defined, which includes a difference function to achieve a least-square fit to doses at preselected points in a planning target volume, and a penalty item to constrain the maximum allowable doses delivered to critical organs. Adjustment between the dose uniformity within the target volume and the dose constraint to the critical structures can be achieved by varying the beam weight variables in the fitness function. A floating-point encoding schema and several operators, like uniform crossover, arithmetical crossover, geometrical crossover, Gaussian mutation and uniform mutation, have been used to evolve the population. Three different cases were used to verify the correctness of the algorithm and quality assessment based on dose-volume histograms and three-dimensional dose distributions were given. The results indicate that the genetic algorithm presented here has considerable potential.

Mixed integer programming with dose‐volume constraints in intensity‐modulated proton therapy

“…Falkinger et al [4] proposed a prioritized optimization algorithm for IMPT planning. Gradient-based methods [3] and a genetic algorithm [5] have also been used to generate treatment plans. Xing et al [6] reported a method for estimating the parameters for the nonlinear objective function and the corresponding algorithm used to determine those parameters.…”

An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT

“…One of the common nonlinear approaches minimizes the total weighted nonlinear function of the dose deviation violation [7,8]. Local search methods have been reported to solve these models, including gradient-based methods [8] and metaheuristics such as simulated annealing [9] and genetic algorithm [10]. …”

Comparison of beam angle selection strategies for intracranial IMRT

“…9 Hence, the BAS problem is usually formulated as a combinatorial optimization (CO) problem, i.e., the selection of an ensemble with η beams from a discrete set of |B| candidate beams. This combinatorial BAS problem may be solved efficiently with metaheuristics for CO-like simulated annealing, 3,5,10 genetic algorithms, 6,11,12 swarm algorithms, 13,14 and mixed integer programming 15,16 or with iterative strategies. 7,15,17,18 Independent BAS strategies, in contrast, exploit prior knowledge that may be based on geometric 8,19,20 or dosimetric 2,21,22 considerations to select a beam ensemble before the FO.…”

“…5 Both approaches have demonstrated clear benefits in comparison to standard beam configurations applying equispaced coplanar beams. 2,3,[5][6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22] In the literature, however, there is neither a comprehensive comparison of different BAS strategies nor a detailed analysis of the underlying BAS parameters and their clinical implications.…”