Towards large nuclear imaging system optical simulations with optiGAN, a generative adversarial network.

Abstract

Optical Monte Carlo (MC) simulations are essential for modeling light transport in radiation detectors used in nuclear imaging and high-energy physics. However, full-system simulations remain computationally prohibitive due to the need to track optical photons across large detector arrays. To address this challenge, we developed optiGAN, a conditional Wasserstein Generative Adversarial Network (GAN) designed to accelerate detailed optical simulations while maintaining high fidelity. Our approach trains optiGAN on high-dimensional optical photon distributions generated using GATE 10, the new Python-based version of the well-established MC simulation toolkit. Two datasets were constructed from 511 keV interactions in bismuth germanate crystals: one included multidimensional features (spatial coordinates, kinetic energy, and time), and another focused solely on time distributions. OptiGAN employs a combination of conditional GAN and Wasserstein GAN with gradient penalty (WGAN-GP) to enhance training stability and accuracy. Model performance was evaluated using the Jensen- Shannon distance, achieving similarity scores exceeding 90% for most photon properties, with further improvements when focusing exclusively on timing distributions. To validate optiGAN ability to reproduce system-level detector performance, we used its output to generate silicon photomultiplier signals using a validated SiPM simulation toolkit. The resulting energy and timing resolutions closely matched those obtained from full MC simulations, demonstrating that optiGAN preserves key detector characteristics while improving computational efficiency by up to two orders of magnitude. These findings establish optiGAN as a promising tool for large-scale detector simulations, enabling rapid evaluation of new detector technologies, also because it has been integrated in the new version of GATE. Future work will focus on further optimizing model performance and extending its applicability to system-level nuclear imaging simulations. .