Liu Zihao, Wang Yuxiang, Han Yiqun, Hu Panpan, Zheng Cheng, Yan Bing, Yang Yidong
Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.
Ion Medical Research Institute, University of Science and Technology of China, Hefei, Anhui, China.
Med Phys. 2025 Jul;52(7):e17899. doi: 10.1002/mp.17899. Epub 2025 May 29.
Accurate dose computation is crucial in intensity-modulated radiation therapy. Owing to its high accuracy, Monte Carlo method is considered the gold standard for radiation dose computation. Its efficiency, however, demands continuous improvement.
This study aims to develop a GPU-accelerated Monte Carlo radiation dose engine (GARDEN) for fast and accurate dose computation in external beam radiotherapy.
In GARDEN simulation, photon and electron transport were modeled using Woodcock tracking and Class II condensed history technique, respectively. To enhance GPU computational efficiency, warp convergence optimization and coalesced access methods were employed. A novel linear accelerator (Linac) head model was established by incorporating a virtual source and a digital collimator system. The physics was verified against GEANT4 in both homogeneous and heterogeneous phantoms. The Linac head model was commissioned using data measured in a water tank and validated by comparing simulation with film doses for two alternating open and closed MLC patterns. Finally, computational efficiency and accuracy were further evaluated in clinical IMRT and VMAT treatment plans.
GARDEN was more than 2500 times faster than GEANT4, with dose differences less than 1% in both homogeneous water and heterogeneous water-lung-bone phantoms. Compared to commission data, the average differences in percentage depth dose curves were less than 1%, and the penumbra differences in lateral dose profiles were less than 1 mm for various radiation field sizes. For two MLC patterns, the gamma pass rates between GARDEN simulations and films were 98.78% and 97.30% at 2%/2 mm criteria, respectively. Both IMRT and VMAT treatment plans achieved gamma pass rates exceeding 99.23% at 3%/3 mm criteria compared to GEANT4 results, with GARDEN completing the dose calculations within 3 s at ∼1% uncertainty on an i9-13900K CPU and NVIDIA 4080 GPU.
The accuracy and efficiency of GARDEN has been benchmarked against GEANT4 and validated in both phantoms and clinical treatment plans. With its capability for fast and accurate dose computation, GARDEN shows strong potential for applications in treatment planning and quality assurance.
精确的剂量计算在调强放射治疗中至关重要。由于其高精度，蒙特卡罗方法被认为是放射剂量计算的金标准。然而，其效率仍需不断提高。
本研究旨在开发一种用于外照射放疗中快速准确剂量计算的GPU加速蒙特卡罗放射剂量引擎（GARDEN）。
在GARDEN模拟中，分别使用伍德科克追踪法和II类凝聚历史技术对光子和电子输运进行建模。为提高GPU计算效率，采用了 warp收敛优化和合并访问方法。通过结合虚拟源和数字准直器系统建立了一种新型直线加速器（Linac）头部模型。在均匀和非均匀体模中与GEANT4进行了物理验证。使用水箱中测量的数据对Linac头部模型进行了调试，并通过比较两种交替的开放和闭合MLC模式的模拟剂量与胶片剂量进行了验证。最后，在临床IMRT和VMAT治疗计划中进一步评估了计算效率和准确性。
GARDEN比GEANT4快2500倍以上，在均匀水模和非均匀水-肺-骨体模中的剂量差异均小于1%。与调试数据相比，不同辐射野大小下，百分深度剂量曲线的平均差异小于1%，侧向剂量分布的半值层差异小于1毫米。对于两种MLC模式，在2%/2毫米标准下，GARDEN模拟与胶片之间的伽马通过率分别为98.78%和97.30%。与GEANT4结果相比，IMRT和VMAT治疗计划在3%/3毫米标准下的伽马通过率均超过99.23%，在i9-13900K CPU和NVIDIA 4080 GPU上，GARDEN在约1%的不确定性下3秒内完成剂量计算。
GARDEN的准确性和效率已与GEANT4进行了基准比较，并在体模和临床治疗计划中得到验证。凭借其快速准确的剂量计算能力，GARDEN在治疗计划和质量保证应用中显示出强大的潜力。
