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This study introduces a novel analytical source model for fast Monte Carlo (MC) dose calculations on graphics processing units (GPUs). The new model improves efficiency and accuracy in radiation therapy dose simulations.

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Area of Science:

  • Medical Physics
  • Computational Physics
  • Radiotherapy Physics

Background:

  • Fast Monte Carlo (MC) dose calculation methods are crucial for graphics processing unit (GPU) platforms in radiation therapy.
  • An analytical linear accelerator (linac) source model is preferred over phase-space file-based models for GPU-based MC dose engines to avoid data loading and CPU-GPU transfers.

Purpose of the Study:

  • To develop and validate a novel analytical, field-independent source model for GPU-based MC dose calculations.
  • To introduce a GPU-friendly sampling scheme and the phase-space-ring (PSR) concept for efficient particle simulation.

Main Methods:

  • Developed an analytical source model parameterizing particle location, direction, and energy using Gaussian distributions for primary photons, scattered photons, and electrons.
  • Introduced a phase-space-ring (PSR) concept for grouping particles of similar type and energy.
  • Implemented a GPU-friendly sampling strategy to minimize thread divergences during MC simulations.
  • Analyzed reference phase-space files to derive model parameters.

Main Results:

  • Dose distributions in a water phantom showed excellent agreement with reference phase-space files (average DTA within 1 mm in high dose gradient regions).
  • Root-mean-square dose differences were within 1.1% in low dose gradient regions, with output factor differences within 0.5%.
  • Achieved over 98.5% passing rate in 3D gamma-index tests (2%/2 mm) for IMRT prostate and head-and-neck cases.
  • Demonstrated a 1.3–2.2 times efficiency improvement in dose calculation compared to a previous phase-space-based model.

Conclusions:

  • The proposed analytical source model accurately represents reference phase-space files for GPU-based MC dose calculations.
  • The model significantly enhances the efficiency of MC dose calculations on GPUs, crucial for clinical applications.
  • The GPU-friendly sampling strategy and PSR concept contribute to improved performance and accuracy in radiotherapy simulations.