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

Carlotta Trigila1, Guneet Mummaneni2, Brahim Mehadji3

  • 1Department of Biomedical Engineering, University of California, Davis, CA, United States of America.

Physics in Medicine and Biology
|May 28, 2025
PubMed
Summary

OptiGAN, a novel generative adversarial network, significantly accelerates optical simulations for radiation detectors. This AI tool maintains high fidelity, improving computational efficiency by up to 100 times for nuclear imaging.

Keywords:
accurate optical photon simulationgenerative adversarial networkmultidimensional distributionsnuclear imaging systemradiation detector

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Area of Science:

  • Physics
  • Computer Science
  • Engineering

Background:

  • Optical Monte Carlo (MC) simulations are crucial for modeling light transport in radiation detectors for nuclear imaging and high-energy physics.
  • Full-system MC simulations are computationally intensive, limiting their application in large detector arrays.
  • Accelerating these simulations is vital for advancing detector technology and analysis.

Purpose of the Study:

  • To develop and validate optiGAN, a conditional Wasserstein generative adversarial network (GAN), for accelerating optical simulations in radiation detectors.
  • To maintain high fidelity in simulations while significantly reducing computational cost.
  • To integrate the developed tool into the GATE simulation toolkit for broader accessibility.

Main Methods:

  • Trained optiGAN using a combination of conditional GAN and Wasserstein GAN with gradient penalty (WGAN-GP) on optical photon distributions generated by GATE 10.
  • Utilized datasets with multidimensional features (spatial, energy, time) and time-only distributions from 511 keV interactions in bismuth germanate crystals.
  • Evaluated model performance using Jensen-Shannon distance and validated system-level detector performance by generating silicon photomultiplier signals.

Main Results:

  • Achieved over 90% similarity scores for most photon properties, with enhanced accuracy for timing distributions.
  • Demonstrated that optiGAN-generated signals closely matched full MC simulations in energy and timing resolutions.
  • Reported computational efficiency improvements of up to two orders of magnitude compared to traditional MC methods.

Conclusions:

  • OptiGAN effectively accelerates detailed optical simulations for radiation detectors while preserving essential characteristics.
  • The tool enables rapid evaluation of new detector technologies and is integrated into the latest GATE version.
  • OptiGAN shows significant promise for large-scale detector simulations and system-level nuclear imaging applications.