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Reliable recovery of the optical properties of multi-layer turbid media by iteratively using a layered diffusion model at multiple source-detector separations

机译:通过在多个源-检测器分离处迭代使用分层扩散模型来可靠地恢复多层混浊介质的光学特性

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摘要

Accurately determining the optical properties of multi-layer turbid media using a layered diffusion model is often a difficult task and could be an ill-posed problem. In this study, an iterative algorithm was proposed for solving such problems. This algorithm employed a layered diffusion model to calculate the optical properties of a layered sample at several source-detector separations (SDSs). The optical properties determined at various SDSs were mutually referenced to complete one round of iteration and the optical properties were gradually revised in further iterations until a set of stable optical properties was obtained. We evaluated the performance of the proposed method using frequency domain Monte Carlo simulations and found that the method could robustly recover the layered sample properties with various layer thickness and optical property settings. It is expected that this algorithm can work with photon transport models in frequency and time domain for various applications, such as determination of subcutaneous fat or muscle optical properties and monitoring the hemodynamics of muscle.
机译:使用分层扩散模型准确确定多层混浊介质的光学特性通常是一项艰巨的任务,并且可能是一个不适定的问题。在这项研究中,提出了一种迭代算法来解决此类问题。该算法采用分层扩散模型来计算在几个源检测器分离(SDS)下分层样品的光学特性。相互参考在各种SDS处确定的光学特性,以完成一轮迭代,并在进一步的迭代中逐渐修改光学特性,直到获得一组稳定的光学特性为止。我们使用频域蒙特卡洛模拟评估了该方法的性能,发现该方法可以通过各种层厚度和光学特性设置稳健地恢复分层的样品特性。期望该算法可以在频域和时域中与光子传输模型配合使用,以用于各种应用,例如确定皮下脂肪或肌肉的光学特性并监测肌肉的血液动力学。

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