A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization

作者：Dias Joana^*; Rocha Humberto; Ferreira Brigida; Lopes Maria do Carmo

来源：Central European Journal of Operations Research, 2014, 22(3): 431-455.

DOI：10.1007/s10100-013-0289-4

• Genetic algorithms
• Radiotherapy
• IMRT
• Surrogate model
• Neural networks

Intensity Modulated Radiotherapy Treatment (IMRT) is a technique used in the treatment of cancer, where the radiation beams are modulated by a multileaf collimator allowing the irradiation of the patient using non-uniform radiation fields from selected angles. Beam angle optimization consists in trying to find the best set of angles that should be used in IMRT planning. The choice of this set of angles is patient and pathology dependent and, in clinical practice, most of the times it is made using a trial and error procedure or simply using equidistantly distributed angles. In this paper we propose a genetic algorithm that aims at calculating good sets of angles in an automated way, given a predetermined number of angles. We consider the discretization of all possible angles in the interval [0, 360], and each individual is represented by a chromosome with 360 binary genes. As the calculation of a given individual%26apos;s fitness is very expensive in terms of computational time, the genetic algorithm uses a neural network as a surrogate model to calculate the fitness of most of the individuals in the population. To explicitly consider the estimation error that can result from the use of this surrogate model, the fitness of each individual is represented by an interval of values and not by a single crisp value. The genetic algorithm is capable of finding improved solutions, when compared to the usual equidistant solution applied in clinical practice. The genetic algorithm will be described and computational results will be shown.

• 出版日期2014-9