  In recent years, the use of Monte Carlo (MC) simulations in the domain of
Medical Physics has become a state-of-the-art technology that consumes lots of
computational resources for the accurate prediction of particle interactions.
The use of generative adversarial network (GAN) has been recently proposed as
an alternative to improve the efficiency and extending the applications of
computational tools in both medical imaging and therapeutic applications. This
study introduces a new approach to simulate positron paths originating from
Fluorine 18 (18 F) isotopes through the utilization of GANs. The proposed
methodology developed a pure conditional transformer least squares (LS)-GAN
model, designed to generate positron paths, and to track their interaction
within the surrounding material. Conditioning factors include the
pre-determined number of interactions, and the initial momentum of the emitted
positrons, as derived from the emission spectrum of 18 F. By leveraging these
conditions, the model aims to quickly and accurately simulate electromagnetic
interactions of positron paths. Results were compared to the outcome produced
with Geant4 Application for Tomography Emission (GATE) MC simulations toolkit.
Less than 10 % of difference was observed in the calculation of the mean and
maximum length of the path and the 1-D point spread function (PSF) for three
different materials (Water, Bone, Lung).
