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首页> 外文期刊>Journal of synchrotron radiation >An efficient numerical tool for dose deposition prediction applied to synchrotron medical imaging and radiation therapy
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An efficient numerical tool for dose deposition prediction applied to synchrotron medical imaging and radiation therapy

机译：一种有效的用于剂量沉积预测的数值工具，应用于同步加速器医学成像和放射治疗

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摘要

Medical imaging and radiation therapy are widely used synchrotron-based techniques which have one thing in common: a significant dose delivery to typically biological samples. Among the ways to provide the experimenters with image guidance techniques indicating optimization strategies, Monte Carlo simulation has become the gold standard for accurately predicting radiation dose levels under specific irradiation conditions. A highly important hampering factor of this method is, however, its slow statistical convergence. A track length estimator (TLE) module has been coded and implemented for the first time in the open-source Monte Carlo code GATE/Geant4. Results obtained with the module and the procedures used to validate them are presented. A database of energy-absorption coefficients was also generated, which is used by the TLE calculations and is now also included in GATE/Geant4. The validation was carried out by comparing the TLE-simulated doses with experimental data in a synchrotron radiation computed tomography experiment. The TLE technique shows good agreement versus both experimental measurements and the results of a classical Monte Carlo simulation. Compared with the latter, it is possible to reach a pre-defined statistical uncertainty in about two to three orders of magnitude less time for complex geometries without loss of accuracy.
机译：医学成像和放射疗法是广泛使用的基于同步加速器的技术，这些技术有一个共同点：向典型的生物样品提供大量剂量。在为实验者提供指示优化策略的图像指导技术的方法中，蒙特卡洛模拟已成为准确预测特定辐射条件下辐射剂量水平的金标准。但是，此方法的一个非常重要的阻碍因素是其缓慢的统计收敛。轨道长度估算器（TLE）模块已在开源的蒙特卡洛代码GATE / Geant4中首次进行编码和实现。介绍了通过模块获得的结果以及用于验证结果的过程。还生成了能量吸收系数的数据库，供TLE计算使用，现在也包含在GATE / Geant4中。通过将TLE模拟的剂量与同步辐射计算机断层摄影实验中的实验数据进行比较来进行验证。 TLE技术相对于实验测量和经典蒙特卡洛模拟的结果均显示出良好的一致性。与后者相比，对于复杂的几何图形，可以在约两到三个数量级的时间范围内达到预定的统计不确定性，而不会降低精度。

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