Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

Automated summary

A method using generative adversarial networks to model particle distribution in emission tomography imaging devices during Monte Carlo simulation is proposed, allowing for efficient particle path generation and reduced computation time, beneficial for imaging system design.