Advanced Monte Carlo Methods for Medical Physics – MoCaMed

Monte Carlo (MC) simulation in medical physics is extensively used during research and development of nuclear imaging systems and treatment planning systems in radiotherapy. All imaging system rely on MC simulation to design, control, optimize, but also to perform research in image reconstruction. All treatment planning systems use MC to characterize photon/particle beams, to compute Dose Point Kernels for analytical dose engines, or directly absorbed dose in patients. However, the increasing need for detailed and accurate simulations, especially considering recent imaging detectors and new radiotherapy protocols, still leads to long simulation times. Variance Reduction Techniques (VRT) have been developed for years but are still restricted to specific applications. Fast GPU particle tracking methods have also been investigated but turned out to be difficult to maintain and generalize. Hence, there are still important needs to improve MC efficiency both in imaging and radiotherapy simulation.

Recent developments on asymptotic VRT such as Quasi-Monte Carlo (QMC) and Artificial Intelligence (AI) open new ways to improve MC. QMC approaches, mostly developed in the field of Computer Graphics, improve MC efficiency by considering samples that are as uniformly distributed as possible. Regarding AI methods in MC, recent approaches showed the feasibility to learn particles response function of SPECT head detector and dose distribution in particle therapy. However, these methods are still limited to some specific applications and do not revolutionize the basis of MC simulation for medical physics.

The ambition of the MoCaMed project is to design, develop and evaluate advanced Monte Carlo sampling and Deep Learning methods to improve MC simulations in medical physics. To achieve such advances, three main foundations of MC simulations will be revisited through three main objectives, from theoretical problems to realistic example applications (imaging and dosimetry) while building the necessary software infrastructure for hosting future developments. The first objective will study the adaptation of QMC and Optimal Transport techniques that have been studied in Computer Graphics to medical physics applications. The foreseen outcome will be new ways to represent and sample distribution probabilities. The second objective is to develop alternative methods to conventional particle tracking within phantom and detector head in order to speed up the MC simulations, based on GAN and neural networks. The third objective will propose fast and accurate predictions of dose distributions by combining MC and AI, and in particular, Deep Learning approaches. Two models will be proposed, one for photons and one for electrons. Finally, all developments will be integrated into the open-source GATE software, one of the main open-source platforms for advanced medical physics simulation in nuclear medicine and particle therapy, together with complete realistic demonstrators (SPECT simulation of molecular therapy treatment, prostate low-dose-rate brachytherapy, and hypo-fractioned external beam radiotherapy).

This consortium gathers researchers from LaTIM, CREATIS and LIRIS laboratories (INSERM, CNRS), experienced in Computer Graphics, AI and simulations in medical physics. All developed methods and results will be openly available for researchers and industrials and will serve as a basis of future developments. Sharing accessible and efficient new MC methods will also foster future common projects with research groups and companies (such as Philips, GE, Siemens, Elekta, IBA with which we are regularly collaborating). Finally, while we focus here on medical physics applications, improved Monte Carlo algorithms may also be useful in other domains such as computer vision or finance.