Abstract ID: 133 Fast and accurate 3D dose distribution computations using artificial neural networks

In radiation therapy, the trade-off between accuracy and speed is the key of the algorithms used in Treatment Planning Systems (TPS). For photon beams, commercial solutions generally relies on analytic algorithms, biased Monte Carlo, or heavily parallelized Monte Carlo on Graphics Processing Units (GPU). Alternatively, we propose an algorithm using Artificial Neural Network (ANN) to compute the dose distributions resulting from ionizing radiations inside a phantom [1] , [2] . We present an evolution of this platform taking into account modulated field sizes and shapes, and various orientations of the beam to the phantom. Firstly, tomodensitometry-based phantoms are created to validate the d…