Direct aperture optimization for IMRT using Monte Carlo generated beamlets
Direct aperture optimization for IMRT using Monte Carlo generated beamlets
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DOI：
10.1118/1.2336509
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发表时间：
2006-10-01
期刊：
影响因子：
3.8
通讯作者：
Duzenli, Cheryl
中科院分区：
文献类型：
--
作者：
Bergman, Alanah M.;Bush, Karl;Duzenli, Cheryl
本文将基于EGSnrc的蒙特卡罗（MC）子束分布矩阵引入到直接孔径优化（DAO）算法中，用于IMRT逆规划。该技术被称为蒙特卡洛直接孔径优化（MC-DAO）。目标是评估准确的蒙特卡罗组织不均匀性建模和DAO逆计划的组合是否会提高治疗计划的剂量准确性和治疗效率。几位作者已经表明，IMRT治疗野中小野和/或不均匀材料的存在会导致无法准确模拟电子不平衡的算法的剂量计算错误。该问题还可能影响IMRT优化过程，因为剂量计算算法可能无法正确建模困难的几何形状，例如靠近低密度区域（肺部、空气等）的目标。使用BEAMnrc（NRC，加拿大）模拟临床直线加速器头。一种新的内部算法将得到的相空间细分为2.5 X 5.0 mm（2）细光束。每个细光束被投射到患者特定的体模上。使用DOSXYZnrc计算对感兴趣结构中每个体素的子束剂量贡献。多叶准直器（MLC）的叶片位置链接到小束剂量分布的位置。MLC形状使用直接孔径优化（DAO）进行优化。使用具有MLC建模的最终Monte Carlo计算来计算最终剂量分布。蒙特卡罗模拟可以生成精确的小射束剂量分布，传统上难以计算的几何形状，特别是对于小的领域交叉区域的组织不均匀性。DAO的引入通过提高治疗递送效率而导致额外的改进。对于本文中提出的示例，与基于注量的优化方法相比，要提供的监视器单元的总数减少了33%。2006年美国医学物理学家协会。
This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment efficiency for treatment planning. Several authors have shown that the presence of small fields and/or inhomogeneous materials in IMRT treatment fields can cause dose calculation errors for algorithms that are unable to accurately model electronic disequilibrium. This issue may also affect the IMRT optimization process because the dose calculation algorithm may not properly model difficult geometries such as targets close to low-density regions (lung, air etc.). A clinical linear accelerator head is simulated using BEAMnrc (NRC, Canada). A novel in-house algorithm subdivides the resulting phase space into 2.5 X 5.0 mm(2) beamlets. Each beamlet is projected onto a patient-specific phantom. The beamlet dose contribution to each voxel in a structure-of-interest is calculated using DOSXYZnrc. The multileaf collimator (MLC) leaf positions are linked to the location of the beamlet does distributions. The MLC shapes are optimized using direct aperture optimization (DAO). A final Monte Carlo calculation with MLC modeling is used to compute the final dose distribution. Monte Carlo simulation can generate accurate beamlet dose distributions for traditionally difficult-to-calculate geometries, particularly for small fields crossing regions of tissue inhomogeneity. The introduction of DAO results in an additional improvement by increasing the treatment delivery efficiency. For the examples presented in this paper the reduction in the total number of monitor units to deliver is similar to 33% compared to fluence-based optimization methods. 2006 American Association of Physicists in Medicine.