PDF(Pubmed)
Abstract：
OBJECTIVE: To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS).
METHODS: GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively.
RESULTS: For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min).
CONCLUSIONS: Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.
METHODS: GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively.
RESULTS: For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min).
CONCLUSIONS: Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.
摘要：
目的：对带有虹膜准直器的CyberknifeM6进行独立的GPU加速蒙特卡罗（MC）剂量验证，并评估Raytracing（TPS-RT）算法和蒙特卡罗（TPS-MC）算法在精确治疗计划系统（TPS）中的剂量计算精度。
方法：将GPU加速的MC算法（ArcherQA-CK）集成到商业剂量验证系统中，ArcherQA,在CyberknifeM6系统中实施针对患者的质量保证。临床30例（头部10例，胸部有10例，本研究收集了10例腹部病例）。对于每种情况，三种不同的剂量计算方法(TPS-MC，TPS-RT和ArcherQA-CK）基于相同的治疗方案实施并相互比较。为了评估，对靶体积和危险器官(OAR)的三维全局伽马分析和剂量参数进行了对比分析。
结果：对于2％/2毫米标准的伽马通过率，TPS-MC与TPS-MC的结果超过98.0%TPS-RT,TPS-MCvs.ArcherQA-CK和TPS-RTvs.头部病例中的ArcherQA-CK,TPS-MC的84.9%与TPS-RT,TPS-MC的98.0%与ArcherQA-CK和TPS-RT的83.3%与ArcherQA-CK在胸部病例中，TPS-MC的98.2%与TPS-RT,TPS-MC的99.4%与ArcherQA-CK和TPS-RT的94.5%与腹部病例中的ArcherQA-CK。对于胸部病例的计划目标体积（PTV）的剂量参数，TPS-RT与TPS-MC和ArcherQA-CKvs.TPS-MC有显著性差异(P<0.01),以及TPS-RT与TPS-MC和TPS-RTvs.ArcherQA-CK相似(P>0.05)。与TPS-MC相比，ArcherQA-CK的计算时间更短(1.66minvs.65.11分钟)。
结论：我们提出的MC剂量引擎（ArcherQA-CK）与PrecisionTPS-MC算法具有高度的一致性，能快速识别TPS-RT算法对某些胸部病例的计算误差。ArcherQA-CK可以在临床实践中提供准确的患者特异性质量保证。
方法：将GPU加速的MC算法（ArcherQA-CK）集成到商业剂量验证系统中，ArcherQA,在CyberknifeM6系统中实施针对患者的质量保证。临床30例（头部10例，胸部有10例，本研究收集了10例腹部病例）。对于每种情况，三种不同的剂量计算方法(TPS-MC，TPS-RT和ArcherQA-CK）基于相同的治疗方案实施并相互比较。为了评估，对靶体积和危险器官(OAR)的三维全局伽马分析和剂量参数进行了对比分析。
结果：对于2％/2毫米标准的伽马通过率，TPS-MC与TPS-MC的结果超过98.0%TPS-RT,TPS-MCvs.ArcherQA-CK和TPS-RTvs.头部病例中的ArcherQA-CK,TPS-MC的84.9%与TPS-RT,TPS-MC的98.0%与ArcherQA-CK和TPS-RT的83.3%与ArcherQA-CK在胸部病例中，TPS-MC的98.2%与TPS-RT,TPS-MC的99.4%与ArcherQA-CK和TPS-RT的94.5%与腹部病例中的ArcherQA-CK。对于胸部病例的计划目标体积（PTV）的剂量参数，TPS-RT与TPS-MC和ArcherQA-CKvs.TPS-MC有显著性差异(P<0.01),以及TPS-RT与TPS-MC和TPS-RTvs.ArcherQA-CK相似(P>0.05)。与TPS-MC相比，ArcherQA-CK的计算时间更短(1.66minvs.65.11分钟)。
结论：我们提出的MC剂量引擎（ArcherQA-CK）与PrecisionTPS-MC算法具有高度的一致性，能快速识别TPS-RT算法对某些胸部病例的计算误差。ArcherQA-CK可以在临床实践中提供准确的患者特异性质量保证。
