Siebers Jeffrey V
Virginia Commonwealth University, Richmond VA, 23298-0058 USA
J Phys Conf Ser. 2008 Apr 4;102:12020. doi: 10.1088/1742-6596/102/1/012020.
Monte Carlo (MC) is rarely used for IMRT plan optimization outside of research centres due to the extensive computational resources or long computation times required to complete the process. Time can be reduced by degrading the statistical precision of the MC dose calculation used within the optimization loop. However, this eventually introduces optimization convergence errors (OCEs). This study determines the statistical noise levels tolerated during MC-IMRT optimization under the condition that the optimized plan has OCEs <100 cGy (1.5% of the prescription dose) for MC-optimized IMRT treatment plans.Seven-field prostate IMRT treatment plans for 10 prostate patients are used in this study. Pre-optimization is performed for deliverable beams with a pencil-beam (PB) dose algorithm. Further deliverable-based optimization proceeds using: (1) MC-based optimization, where dose is recomputed with MC after each intensity update or (2) a once-corrected (OC) MC-hybrid optimization, where a MC dose computation defines beam-by-beam dose correction matrices that are used during a PB-based optimization. Optimizations are performed with nominal per beam MC statistical precisions of 2, 5, 8, 10, 15, and 20%. Following optimizer convergence, beams are re-computed with MC using 2% per beam nominal statistical precision and the 2 PTV and 10 OAR dose indices used in the optimization objective function are tallied. For both the MC-optimization and OC-optimization methods, statistical equivalence tests found that OCEs are less than 1.5% of the prescription dose for plans optimized with nominal statistical uncertainties of up to 10% per beam. The achieved statistical uncertainty in the patient for the 10% per beam simulations from the combination of the 7 beams is ~3% with respect to maximum dose for voxels with D>0.5D(max). The MC dose computation time for the OC-optimization is only 6.2 minutes on a single 3 Ghz processor with results clinically equivalent to high precision MC computations.
由于完成蒙特卡罗(MC)过程需要大量计算资源或较长计算时间,因此在研究中心之外,MC很少用于调强放疗(IMRT)计划优化。通过降低优化循环中使用的MC剂量计算的统计精度,可以减少计算时间。然而,这最终会引入优化收敛误差(OCE)。本研究确定了在MC优化的IMRT治疗计划中,优化后的计划OCE<100 cGy(处方剂量的1.5%)的条件下,MC-IMRT优化过程中可容忍的统计噪声水平。本研究使用了10例前列腺癌患者的七野前列腺IMRT治疗计划。使用笔形束(PB)剂量算法对可交付射束进行预优化。进一步基于可交付射束的优化过程如下:(1)基于MC的优化,每次强度更新后用MC重新计算剂量;(2)一次校正(OC)MC混合优化,其中MC剂量计算定义逐束剂量校正矩阵,在基于PB的优化过程中使用。以每束名义MC统计精度2%、5%、8%、10%、15%和20%进行优化。优化器收敛后,使用每束2%的名义统计精度用MC重新计算射束,并计算优化目标函数中使用的2个计划靶体积(PTV)和10个危及器官(OAR)剂量指标。对于MC优化和OC优化方法,统计等效性测试发现,对于每束名义统计不确定度高达10%的优化计划,OCE小于处方剂量的1.5%。对于10%每束模拟的患者,由7束射束组合产生的最大剂量下,体素的统计不确定度约为3%(D>0.5D(max))。在单个3 Ghz处理器上,OC优化的MC剂量计算时间仅为6.2分钟,结果在临床上与高精度MC计算等效。