Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900–2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.

蒙特卡罗模拟（MCS）是放射治疗中剂量计算和图像形成最准确的计算方法之一。然而，MCS 的高计算复杂度和长执行时间限制了它的广泛使用。在本文中，我们提出了一种使用图形处理单元 (GPU) 加速 MCS 的新策略，并展示了在兆电压 (MV) 锥形束计算机断层扫描 (CBCT) 仿真中的应用。从单次模拟运行生成一系列 MV 投影的新框架专为 MV-CBCT 采集而设计。用于光子模拟的基于 Geant4 的 GPU 代码被合并到框架中，用于模拟通过幻象体积的光子传输。对加速 MV 图像模拟的 FastEPID 方法进行了修改并集成到框架中。所提出的基于 GPU 的模拟策略在 Catphan 604 体模和拟人骨盆体模中的准确性和效率进行了测试，光束能量分别为 2.5 MV、6 MV 和 6 MV FFF。在所有情况下，所提出的基于 GPU 的仿真都表现出很高的仿真精度，并且在重建图像质量方面与测量和基于 CPU 的仿真非常一致。使用 NVIDIA Tesla V100 GPU 卡与 2.5 GHz AMD Opteron™ 处理器 6380 相比，MV-CBCT 模拟加速了大约 900–2300 倍。所提出的基于 GPU 的模拟在重建图像质量方面表现出很高的模拟精度，并且与测量和基于 CPU 的模拟非常吻合。使用 NVIDIA Tesla V100 GPU 卡与 2.5 GHz AMD Opteron™ 处理器 6380 相比，MV-CBCT 模拟加速了大约 900–2300 倍。所提出的基于 GPU 的模拟在重建图像质量方面表现出很高的模拟精度，并且与测量和基于 CPU 的模拟非常吻合。使用 NVIDIA Tesla V100 GPU 卡与 2.5 GHz AMD Opteron™ 处理器 6380 相比，MV-CBCT 模拟加速了大约 900–2300 倍。