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Perturbation Monte Carlo methods for tissue structure alterations

机译:扰动蒙特卡洛方法用于组织结构改变

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摘要

This paper describes an extension of the perturbation Monte Carlo method to model light transport when the phase function is arbitrarily perturbed. Current perturbation Monte Carlo methods allow perturbation of both the scattering and absorption coefficients, however, the phase function can not be varied. The more complex method we develop and test here is not limited in this way. We derive a rigorous perturbation Monte Carlo extension that can be applied to a large family of important biomedical light transport problems and demonstrate its greater computational efficiency compared with using conventional Monte Carlo simulations to produce forward transport problem solutions. The gains of the perturbation method occur because only a single baseline Monte Carlo simulation is needed to obtain forward solutions to other closely related problems whose input is described by perturbing one or more parameters from the input of the baseline problem. The new perturbation Monte Carlo methods are tested using tissue light scattering parameters relevant to epithelia where many tumors originate. The tissue model has parameters for the number density and average size of three classes of scatterers; whole nuclei, organelles such as lysosomes and mitochondria, and small particles such as ribosomes or large protein complexes. When these parameters or the wavelength is varied the scattering coefficient and the phase function vary. Perturbation calculations give accurate results over variations of ∼15–25% of the scattering parameters.
机译:本文描述了当相位函数被任意扰动时,扰动蒙特卡洛方法的扩展,以模拟光传输。当前的扰动蒙特卡罗方法允许散射系数和吸收系数都扰动,但是相位函数不能改变。我们在这里开发和测试的更复杂的方法并不仅限于此。我们得出了一个严格的摄动蒙特卡洛扩展,可以将其应用于一大批重要的生物医学光传输问题,并证明与使用传统的蒙特卡洛模拟来产生正向传输问题解决方案相比,它具有更高的计算效率。之所以会产生微扰方法,是因为只需要一个基线蒙特卡洛模拟就可以获得其他紧密相关问题的正解,而这些正相关问题的输入是通过从基线问题的输入中扰乱一个或多个参数来描述的。使用与许多肿瘤起源的上皮细胞相关的组织光散射参数测试了新的摄动蒙特卡洛方法。组织模型具有用于三类散射体的数量密度和平均尺寸的参数。整个细胞核,细胞器(如溶酶体和线粒体)和小颗粒(如核糖体或大蛋白复合物)。当这些参数或波长变化时,散射系数和相位函数也会变化。扰动计算可在散射参数的约15–25%的变化范围内给出准确的结果。

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