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用于重离子放射治疗的放射生物学研究的物理参数优化方案。

Physical parameter optimization scheme for radiobiological studies of charged particle therapy.

机构信息

Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.

Orbital Debris Program Office, NASA Johnson Space Center, Houston, TX 77058, USA.

出版信息

Phys Med. 2018 Jul;51:13-21. doi: 10.1016/j.ejmp.2018.06.001. Epub 2018 Jun 14.

Abstract

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 (LET), within the target region. We selected proton, He ion, and C ion beams to demonstrate the feasibility and flexibility of our method. The pristine dose Bragg curves in water for all ion beams and the LET 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 LET (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.

摘要

我们开发了一种易于实施的方法,用于优化带电粒子治疗中所需物理量的空间分布。该基本方法需要找到构成粒子束的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传递 (LET))的所需空间分布。我们选择质子、氦离子和碳离子束来证明我们方法的可行性和灵活性。所有离子束在水中的原始剂量布拉格曲线和质子束的 LET 是通过 Geant4 蒙特卡罗模拟生成的。优化算法是使用 Python 编程语言实现的。然后,针对不同情况,获得了所需物理量空间分布的高精度优化结果。在整个目标区域内,给定数量的实际值与期望值之间的相对差异约为 ±1.0%。优化示例包括三种选定离子的平展剂量布喇格峰 (SOBP)、质子的上坡剂量 SOBP 和质子的下坡剂量 SOBP。对于质子的平坦 LET(目标值=4keV/µm)分布情况,相对差异约为 ±2.0%。如果有相应的物理数据,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,此物理量优化策略可以方便地扩展到包含生物剂量优化。

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