RESUMEN

The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol. 45 913-22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth-dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journees Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map.

Asunto(s)

Red Nerviosa , Redes Neurales de la Computación , Radiometría/métodos , Planificación de la Radioterapia Asistida por Computador/métodos , Algoritmos , Humanos , Modelos Teóricos , Método de Montecarlo , Neuronas/metabolismo , Fantasmas de Imagen , Programas Informáticos

RESUMEN

We consider networks of a large number of neurons (or units, processors, ...), whose dynamics are fully asynchronous with overlapping updating. We suppose that the neurons take a finite number of states (discrete states), and that the updating scheme is discrete in time. We make no hypotheses on the activation function of the neurons; the networks may have multiple cycles and basins. We derive conditions on the initialization of the networks, which ensures convergence to fixed points only. Application to a fully asynchronous Hopfield neural network allows us to validate our study.