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The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction—based optimized treatment plans

机译:统计噪声对IMRT计划质量和基于MC和基于MC校正的优化治疗计划的收敛性的影响

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摘要

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>0.5Dmax. 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.
机译:由于完成过程所需的大量计算资源或较长的计算时间,蒙特卡洛(MC)在研究中心之外很少用于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,可以减少时间。但是,这最终会引入优化收敛误差(OCE)。这项研究确定了MC-IMRT治疗方案中优化方案的OCEs <100 cGy(处方剂量的1.5%)的情况下,MC-IMRT优化过程中容许的统计噪声水平。七场前列腺IMRT治疗方案为10在这项研究中使用前列腺患者。使用笔形光束(PB)剂量算法对可交付光束进行预优化。进一步的基于可交付项的优化使用以下方法进行:(1)基于MC的优化,其中在每次强度更新后用MC重新计算剂量,或(2)一次校正(OC)MC混合优化,其中MC剂量计算定义了射束-基于PB的优化过程中使用的电子束剂量校正矩阵。以2%,5%,8%,10%,15%和20%的名义每束MC统计精度执行优化。在优化器收敛之后,使用每束标称统计精度2%的MC重新计算光束,并计算出优化目标函数中使用的2个PTV和10个OAR剂量指数。对于MC优化方法和OC优化方法,统计等效性测试都发现,对于优化后的计划,其名义统计不确定性高达每束10%,OCE小于处方剂量的1.5%。相对于D> 0.5Dmax的体素的最大剂量,从7束光束组合得出的每束光束10%的模拟中,患者获得的统计不确定度约为〜3%。在单个3 Ghz处理器上,用于OC优化的MC剂量计算时间仅为6.2分钟,其结果在临床上等同于高精度MC计算。

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  • 作者

    Jeffrey V. Siebers;

  • 作者单位
  • 年(卷),期 -1(102),-1
  • 年度 -1
  • 页码 012020
  • 总页数 12
  • 原文格式 PDF
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