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Optimization of inverse algorithm for estimating the optical properties of biological materials using spatially-resolved diffuse reflectance

机译:利用空间分辨漫反射率估计生物材料光学特性的逆算法的优化

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摘要

Determination of the optical properties from intact biological materials based on diffusion approximation theory is a complicated inverse problem, and it requires proper implementation of inverse algorithm, instrumentation and experiment. This article was aimed at optimizing the procedure of estimating the absorption and reduced scattering coefficients of turbid homogeneous media from spatially-resolved diffuse reflectance data. A diffusion model and the inverse algorithm were first validated by Monte Carlo simulations. Sensitivity analysis was performed to gain an insight into the relationship between the estimated parameters and the dependent variables in the inverse algorithm for improving the parameter estimation procedure. Three data transformation and the relative weighting methods were compared in the nonlinear least squares regression. It is found that the logarithm and integral data transformation and relative weighting methods greatly improve estimation accuracy with the relative errors of 10.4%, 10.7% and 11.4% for the absorption coefficient, and 6.6%, 7.0% and 7.1% for the reduced scattering coefficient, respectively. Further statistical analysis shows that the logarithm transformation and relative weighting methods give more reliable estimations of the optical parameters. To accurately estimate the optical parameters, it is important to study and quantify the characteristics and properties of the mathematical model and its inverse algorithm.
机译:基于扩散近似理论从完整的生物材料中确定光学性质是一个复杂的逆问题,它需要逆算法,仪器和实验的适当实现。本文旨在优化从空间分辨漫反射数据估算浑浊均质介质的吸收系数和降低的散射系数的程序。首先通过蒙特卡洛模拟验证了扩散模型和逆算法。进行了敏感性分析,以了解逆算法中估计参数与因变量之间的关系,以改善参数估计程序。在非线性最小二乘回归中比较了三种数据转换和相对加权方法。发现对数和积分数据转换以及相对加权方法极大地提高了估计准确性,吸收系数的相对误差分别为10.4%,10.7%和11.4%,降低的散射系数的相对误差为6.6%,7.0%和7.1% , 分别。进一步的统计分析表明,对数变换和相对加权方法可以更可靠地估计光学参数。为了准确地估计光学参数,研究和量化数学模型及其逆算法的特征和特性非常重要。

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  • 来源
    《Inverse Problems in Science and Engineering》 |2010年第6期|p.853-872|共20页
  • 作者单位

    Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA;

    US Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean Research Unit, 224 Farrall Hall, Michigan State University, East Lansing, MI 48824, USA;

    Department of Food Science and Human Nutrition, Michigan State University, 135 Trout Food Science Building, East Lansing, MI 48824, USA;

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