Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.
Department of Radiation Oncology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Med Phys. 2023 Aug;50(8):5238-5247. doi: 10.1002/mp.16409. Epub 2023 Apr 12.
Accurate dose computation is critical in precision small animal radiotherapy. The Monte Carlo simulation method is the gold standard for radiation dose computation but has not been widely implemented in practice due to its low computation efficiency.
This study aims to develop a GPU-accelerated radiation dose engine (GARDEN) based on the Monte Carlo simulation method for fast and accurate dose computation.
In the GARDEN simulation, Compton scattering, Rayleigh scattering, and photoelectric effect were considered. The Woodcock tracking algorithm and GPU-specific acceleration techniques were used to obtain a high computational efficiency. Benchmark studies against both Geant4 simulations and experimental measurements were performed for various phantoms and beams. Finally, a conformal arc treatment plan was designed for a lung tumor to further evaluate the accuracy and efficiency in small animal radiotherapy.
The engine attained a speed-up of 1232 times in a homogeneous water phantom and 935 times in a water-bone-lung heterogeneous phantom when compared with Geant4. Both the depth-dose curves and cross-sectional dose profiles for various radiation field sizes showed a great match between measurements and the GARDEN calculations. For in vivo dose validation, the differences between calculations and measurements in the mouse thorax and abdomen were 2.50% ± 1.50% and 1.56% ± 1.40%, respectively. The computation time for an arc treatment plan delivered from 36 angles was 2 s at a <1% uncertainty level using an NVIDIA GeForce RTX 2060 SUPER GPU. When compared with Geant4, the 3D gamma comparison passing rate was 98.7% at 2%/0.3 mm criteria.
GARDEN can perform fast and accurate dose computations in heterogeneous tissue environments and is expected to play a vital role in image-guided precision small animal radiotherapy.
在精准小动物放射治疗中，准确的剂量计算至关重要。蒙特卡罗模拟方法是辐射剂量计算的金标准，但由于计算效率低，尚未在实践中广泛应用。
本研究旨在开发一种基于蒙特卡罗模拟方法的 GPU 加速辐射剂量引擎（GARDEN），以实现快速准确的剂量计算。
在 GARDEN 模拟中，考虑了康普顿散射、瑞利散射和光电效应。采用 Woodcock 跟踪算法和 GPU 特定的加速技术，获得了较高的计算效率。针对各种体模和射束，对 GARDEN 与 Geant4 模拟和实验测量进行了基准研究。最后，为肺肿瘤设计了一个适形弧治疗计划，以进一步评估在小动物放射治疗中的准确性和效率。
与 Geant4 相比，在均匀水模体中，该引擎的速度提高了 1232 倍，在水-骨-肺非均匀体模中，速度提高了 935 倍。对于各种射束大小，深度剂量曲线和横截面剂量分布都显示出与 GARDEN 计算的很好吻合。对于体内剂量验证，在小鼠胸部和腹部，计算值与测量值的差异分别为 2.50%±1.50%和 1.56%±1.40%。使用 NVIDIA GeForce RTX 2060 SUPER GPU，在不确定性水平为<1%时，36 个角度的弧形治疗计划的计算时间为 2 秒。与 Geant4 相比，在 2%/0.3mm 标准下，3D 伽马比较通过率为 98.7%。
GARDEN 可以在异质组织环境中进行快速准确的剂量计算，有望在图像引导的精准小动物放射治疗中发挥重要作用。
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