Medical Physics Program, Department of Physics and Applied Physics, University of Massachusetts Lowell, Lowell, MA, United States of America. Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Phys Med Biol. 2020 Dec 2;65(23):235042. doi: 10.1088/1361-6560/abaeba.
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 的计算复杂性高和执行时间长限制了其广泛应用。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，并展示了在兆伏（MV）锥形束计算机断层扫描（CBCT）模拟中的应用。我们专门为 MV-CBCT 采集设计了一种从单次模拟运行生成一系列 MV 投影的新框架。基于 Geant4 的光子模拟 GPU 代码被纳入框架中，用于模拟光子通过体模的传输。FastEPID 方法用于加速 MV 图像的模拟，被修改并集成到框架中。在 Catphan 604 体模和具有 2.5 MV、6 MV 和 6 MV FFF 束能的人体骨盆体模中对提出的基于 GPU 的模拟策略进行了准确性和效率测试。在所有情况下，与测量值和基于 CPU 的模拟相比，基于 GPU 的模拟在重建图像质量方面表现出了很好的模拟准确性和极好的一致性。使用 NVIDIA Tesla V100 GPU 卡，与 2.5 GHz AMD OpteronTM Processor 6380 相比，MV-CBCT 模拟的加速因子约为 900-2300。
