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Physical parameter optimization scheme for radiobiological studies of charged particle therapy

机译:带电粒子疗法放射生物学研究的物理参数优化方案

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摘要

We have developed an easy-to-implement method to optimize the spatial distribution of a desired physical quantity for charged particle therapy. The basic methodology requires finding the optimal solutions for the weights of the constituent particle beams that together form the desired spatial distribution of the specified physical quantity, e.g., dose or dose-averaged linear energy transfer (LETd), within the target region. We selected proton, 4He ion, and 12C ion beams to demonstrate the feasibility and flexibility of our method. The pristine dose Bragg curves in water for all ion beams and the LETd for proton beams were generated from Geant4 Monte Carlo simulations. The optimization algorithms were implemented using the Python programming language. High-accuracy optimization results of the spatial distribution of the desired physical quantity were then obtained for different cases. The relative difference between the real value and the expected value of a given quantity was approximately within ± 1.0% in the whole target region. The optimization examples include a flat dose spread-out Bragg peak (SOBP) for the three selected ions, an upslope dose SOBP for protons, and a downslope dose SOBP for protons. The relative difference was approximately within ± 2.0% for the case with a flat LETd (target value = 4 keV/μm) distribution for protons. These one-dimensional optimization algorithms can be extended to two or three dimensions if the corresponding physical data are available. In addition, this physical quantity optimization strategy can be conveniently extended to encompass biological dose optimization if appropriate biophysical models are invoked.
机译:我们已经开发了一种易于实现的方法,可以优化带电粒子疗法所需物理量的空间分布。基本方法学要求找到组成粒子束权重的最佳解决方案,这些粒子束共同形成目标区域内指定物理量(例如剂量或剂量平均线性能量转移(LETd))的所需空间分布。我们选择了质子, 4 He离子和 12 C离子束,以证明该方法的可行性和灵活性。所有水离子束的原始布拉格剂量曲线和质子束的LETd是通过Geant4 Monte Carlo模拟生成的。优化算法是使用Python编程语言实现的。然后针对不同情况获得了所需物理量的空间分布的高精度优化结果。在整个目标区域中,给定数量的实际值和期望值之间的相对差大约在±1.0%之内。优化示例包括三个选定离子的平坦剂量分布布拉格峰(SOBP),质子的上坡剂量SOBP和质子的下坡剂量SOBP。对于质子具有平坦LETd(目标值= 4 keV /μm)分布的情况,相对差异约为±2.0%以内。如果相应的物理数据可用,则这些一维优化算法可以扩展到二维或三维。此外,如果调用适当的生物物理模型,则可以方便地将此物理量优化策略扩展为涵盖生物剂量优化。

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