当前用于光传播建模的开源蒙特卡洛(MC)方法实现多次繁琐的构建,并且需要第三方许可的软件,这通常会阻止生物医学光学界的潜在研究人员充分利用光传播工具。此外,同样的缺点也限制了由各种MC代码估计的物理量的严格交叉验证。
关于光传播建模的开源工具和易于访问的数据集的建议,以鼓励研究人员之间进行富有成效的交流,并为MC方法的可用实现之间的更一致的比较铺平道路。
基于Python编程语言和PyOpenCL扩展的多层和体素组织的MC方法的PyXOpto实现可在众多启用OpenCL的设备上进行大规模并行计算。所提出的实现用于计算大的反射率数据集,透光率,能量沉积,和各种来源的采样量,检测器,和组织结构。
提出的PyXOpto与最初的MC实现非常吻合。然而,进一步的验证揭示了原始MC实现中使用的随机数生成器引入的明显偏差。
建立共同的数据集对确认混浊介质中光传播的MC代码的存在和发展是非常重要的。
Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes.
Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method.
The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations.
The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation.
Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.