Neural network modelling of dose distribution and dose uniformity in the Tunisian Gamma Irradiator

In this paper an approach to model dose distributions, isodose curves and dose uniformity in the Tunisian Gamma Irradiation Facility using artificial neural networks (ANNs) are described. For this purpose, measurements were carried out at different points in the irradiation cell using polymethyl methacrylate dosemeters. The calculated and experimental results are compared and good agreement is observed showing that ANNs can be used as an efficient tool for modelling dose distribution in the gamma irradiation facility. Monte Carlo (MC) photon-transport simulation techniques have been used to evaluate the spatial dose distribution for extensive benchmarking. ANN approach appears to be a significant advance over the time-consuming MC or the less accurate regression methods for dose mapping. As a second application, a detailed dose mapping using two different product densities was carried out. The minimum and maximum dose locations and dose uniformity as a function of the irradiated volume for each product density were determined. Good agreement between ANN modelling and experimental results was achieved.