Artificial Intelligence for Monte Carlo Simulation in Medical Physics - Sarrut, David; Etxebeste, Ane; Muñoz, Enrique; Krah, Nils; Létang, Jean Michel (Frontiers in Physics, 2021)
Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as patient dose distribution estimation, or the development of imaging detectors, Monte Carlo simulation is a key tool both in academic research labs and industrial research and development services. Because of the very nature of Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. We review here the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.