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Parallelized Monte-Carlo dosimetry using graphics processing units to model cylindrical diffusers used in

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Graphics processing units (GPUs) accelerate Monte-Carlo simulations for photodynamic therapy (PDT) dosimetry. This GPU-enhanced method accurately models light delivery for interstitial PDT, enabling faster treatment planning.

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Area of Science:

  • Medical Physics
  • Computational Physics
  • Biomedical Engineering

Background:

  • Monte Carlo (MC) simulations are standard for radiation dosimetry but are computationally intensive.
  • Graphics Processing Units (GPUs) offer a cost-effective solution for accelerating MC simulations through parallelization.

Purpose of the Study:

  • To describe a parallelized MC algorithm using GPU technology for photodynamic therapy (PDT) dosimetry.
  • To optimize the algorithm for light emitted from optical fibers with cylindrical diffusers used in interstitial PDT.

Main Methods:

  • Developed and implemented a parallel MC algorithm on GPUs for PDT dosimetry.
  • Optimized the algorithm for light transport from cylindrical diffusers.
  • Validated the simulation results using experimental measurements with an optical phantom mimicking brain tissue.
  • Compared results with ex-vivo measurements from intraoperative PDT devices.

Main Results:

  • The GPU-accelerated MC method accurately computes light dosimetry for interstitial PDT.
  • Experimental validation using an optical phantom and ex-vivo measurements confirmed the accuracy of the GPU MC results.
  • The acceleration achieved is consistent with literature values and suitable for clinical integration.

Conclusions:

  • GPU-based MC simulations provide a significant speed-up for PDT dosimetry.
  • This accelerated method is accurate and fast enough for integration into treatment planning systems for routine interstitial PDT.
  • The validated algorithm supports improved treatment planning for brain tumor PDT.