RESUMO

This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin required by the code of practice 398 of the International Atomic Energy Agency (IAEA). Our method uses deep-learning Artificial Neural Networks as an alternative to the Monte Carlo methods usually used nowadays. It reproduces the radiation dose profiles for X-rays of 6 and 15 MV data reported in the British Journal of Radiology (Aird, 1996). Even more, our method reproduces data from other sources with acceptable errors. These simulations pave the way to enhance radiotherapy techniques in planning patient doses and calibrating ionizing radiation measurement instruments used in the fight against cancer.

Assuntos

Redes Neurais de Computação , Planejamento da Radioterapia Assistida por Computador , Humanos , Raios X , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Radiografia , Método de Monte Carlo , Imagens de Fantasmas , Radiometria/métodos

RESUMO

In this paper, we present a model of the gamma irradiation room at the National Institute of Nuclear Research (ININ is its acronym in Spanish) in Mexico to improve the use of physics in dosimetry for human protection. We deal with air-filled ionization chambers and scientific computing made in house and framed in both the GEANT4 scheme and our analytical approach to characterize the irradiation room. This room is the only secondary dosimetry facility in Mexico. Our aim is to optimize its experimental designs, facilities, and industrial applications of physical radiation. The computational results provided by our model are supported by all the known experimental data regarding the performance of the ININ gamma irradiation room and allow us to predict the values of the main variables related to this fully enclosed space to within an acceptable margin of error.