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A Macro-Monte Carlo method for the simulation of diffuse light transport in tissue

机译:宏观蒙特卡罗方法模拟组织中的漫射光传输

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摘要

The Monte Carlo (MC) method of calculating light distributions in turbid media such as tissue has become the gold standard, especially in complex geometries and heterogeneous tissue. The utility of the MC method, however, is limited by is computational intensity. In an effort to reduce the time needed for MC calculations, we have adapted a macro-Monte Carlo (MMC) method (Neuenschwander, et al. 1995, Phys. Med. Biol. >40, 543-574) to the solution of tissue optics problems. Traditional MC routines trace individual photons step-by-step through the tissue. Instead, the MMC approach relies on a data set consisting of spheres in which the light absorbed in each voxel is pre-calculated using a traditional MC routine. At each MMC step, the pre-calculated absorbed light dose in the appropriate sphere, aligned to the current position and direction of the sphere, is recorded in the dose matrix. The position and direction of the photon exiting the sphere are chosen from the exit distribution of the pre-calculated sphere, and the process is repeated. By choosing the size of the pre-calculated sphere appropriately, arbitrarily complex boundary geometries can be simulated. We compare the accuracy and calculation time of the MMC method with a traditional MC algorithm for a variety of tissue optical properties and geometries. We find that the MMC algorithm can increase the speed of calculation by as much as two orders of magnitude, depending on the optical properties being simulated, without a significant loss in accuracy.
机译:计算诸如组织之类的混浊介质中光分布的蒙特卡洛(MC)方法已成为金标准,尤其是在复杂几何形状和异质组织中。但是,MC方法的实用性受到计算强度的限制。为了减少MC计算所需的时间,我们采用了宏观蒙特卡罗(MMC)方法(Neuenschwander等,1995,Phys。Med。Biol。> 40 ,543-574 )解决组织光学问题。传统的MC例程会逐步跟踪组织中的单个光子。取而代之的是,MMC方法依赖于由球体组成的数据集,其中使用传统的MC例程预先计算每个体素中吸收的光。在每个MMC步骤中,将与球体的当前位置和方向对齐的适当球体中预先计算的吸收光剂量记录在剂量矩阵中。从预先计算的球体的出口分布中选择离开球体的光子的位置和方向,然后重复该过程。通过适当选择预先计算的球体的大小,可以模拟任意复杂的边界几何形状。我们将MMC方法的准确性和计算时间与传统的MC算法进行比较,以用于各种组织的光学特性和几何形状。我们发现,MMC算法可以将计算速度提高多达两个数量级,这取决于要模拟的光学特性,而不会显着降低精度。

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