Bergman Alanah M, Bush Karl, Milette Marie-Pierre, Popescu I Antoniu, Otto Karl, Duzenli Cheryl
Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.
Med Phys. 2006 Oct;33(10):3666-79. doi: 10.1118/1.2336509.
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 mm2 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 approximately 33% compared to fluence-based optimization methods.
这项工作将基于EGSnrc的蒙特卡罗（MC）子野剂量分布矩阵引入到用于调强放疗逆向计划的直接孔径优化（DAO）算法中。该技术被称为蒙特卡罗直接孔径优化（MC-DAO）。目的是评估精确的蒙特卡罗组织不均匀性建模与DAO逆向计划相结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，调强放疗治疗野中存在小射野和/或不均匀物质会导致无法准确模拟电子不平衡的算法出现剂量计算误差。这个问题也可能影响调强放疗的优化过程，因为剂量计算算法可能无法正确模拟困难的几何形状，例如靠近低密度区域（肺、空气等）的靶区。使用BEAMnrc（加拿大国家研究委员会）对临床直线加速器机头进行模拟。一种新的内部算法将所得相空间细分为2.5×5.0平方毫米的子野。每个子野都投影到患者特异性体模上。使用DOSXYZnrc计算感兴趣结构中每个体素的子野剂量贡献。多叶准直器（MLC）叶片位置与子野剂量分布的位置相关联。使用直接孔径优化（DAO）对MLC形状进行优化。使用包含MLC建模的最终蒙特卡罗计算来计算最终剂量分布。蒙特卡罗模拟可以为传统上难以计算的几何形状生成准确的子野剂量分布，特别是对于穿过组织不均匀区域的小射野。DAO算法的引入通过提高治疗实施效率带来了额外的改进。与基于通量的优化方法相比，本文给出的示例中，所需监测单位总数减少了约33%。
