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Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

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A GPU-accelerated Monte Carlo dose engine for external beam radiotherapy.

Zihao Liu¹, Yuxiang Wang^1,2, Yiqun Han¹

¹Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.

Medical Physics

|May 29, 2025

Summary

This summary is machine-generated.

This study introduces GARDEN, a GPU-accelerated Monte Carlo tool for rapid and precise radiation dose calculations in external beam radiotherapy. GARDEN significantly enhances computational speed while maintaining high accuracy for treatment planning.

Keywords:

Dose computation GPU IMRT Monte Carlo

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Area of Science:

Medical Physics
Computational Biology
Radiotherapy

Background:

Accurate dose computation is essential for intensity-modulated radiation therapy.
Monte Carlo methods offer high accuracy but require efficiency improvements for clinical use.

Purpose of the Study:

To develop a GPU-accelerated Monte Carlo radiation dose engine (GARDEN).
To achieve fast and accurate dose computation for external beam radiotherapy.

Main Methods:

Utilized Woodcock tracking and Class II condensed history for photon/electron transport.
Implemented GPU optimizations (warp convergence, coalesced access) for efficiency.
Developed a novel linear accelerator head model and validated physics against GEANT4.

Main Results:

GARDEN demonstrated over 2500x speed improvement compared to GEANT4 with <1% dose differences.
Achieved <1% differences in depth dose curves and <1mm in penumbra.
Clinical IMRT/VMAT plans showed >99.23% gamma pass rates, with calculations completed in under 3 seconds.

Conclusions:

GARDEN's accuracy and efficiency are validated against GEANT4 and clinical data.
The tool shows significant potential for radiotherapy treatment planning and quality assurance.