Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China.
Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac7194.
To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields.A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code's dose calculation accuracy and efficiency were assessed through both phantoms and patient cases.gSMC accurately calculated the dose in various phantoms for both = 0 T and = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186-304 relative to EGSnrc with 10 CPU threads.A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code's high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC.
为了在磁场中进行高效、准确的剂量计算，开发并验证了一种基于图形处理单元（GPU）的叠加蒙特卡罗（SMC）代码。在水模体中，使用 EGSnrc 模拟了一系列从 25keV 到 7.7MeV 的单能光子，以生成粒子轨迹数据库。SMC 物理被扩展到带电荷粒子在磁场中的输运，并随后在 GPU 上作为 gSMC 进行编程。通过结合方差减少技术设计了优化的模拟方案，以缓解一般 GPU-MC 代码中的线程发散问题，并提高计算效率。通过体模和患者病例评估了 gSMC 代码的剂量计算准确性和效率。gSMC 准确计算了在磁场中各种体模的剂量，在 0T 和 1.5T 下，与 EGSnrc 吻合良好，整个深度剂量区域的均方根误差小于 1.0%。患者病例验证也表明，与 EGSnrc 相比，所有测试肿瘤部位的三维伽马通过率（2%/2mm）均大于 97%，剂量吻合度很高。通过光子分裂和粒子轨迹重复技术，gSMC 解决了线程发散问题，与使用 10 个 CPU 线程的 EGSnrc 相比，效率提高了 186-304 倍。开发了一种名为 gSMC 的 GPU 叠加蒙特卡罗代码，用于磁场中的剂量计算。该开发代码具有高计算准确性和效率，使其适用于带有 MR-LINAC 的在线自适应放疗中的剂量计算任务。
