基于漫反射光谱法下层组织信息的最佳探测位置的研究

由于人体组织结构的复杂性和个体差异性，采用光谱分析技术实现组织内部信息的检测方法中，仍存在着检测位置不明确的问题。采用多位置空间分辨漫反射光谱法对脂肪肌肉组织中内部信息的最佳探测位置进行了研究。由于目标组织为肌肉组织，为了消除由于不同人体皮下脂肪层厚度的差异以及目标层中多次后向散射光对测量结果引起的较大误差，并根据光在复杂生物组织中的传输模型，通过增加约束条件——两条理想的“香蕉型”路径—来定义有效光子比SNR，并以SNR作为评价最佳探测位置SDS_best的标准，对优化后的Monte Carlo仿真结果进行统计分析，分别研究了脂肪层厚度h_f、脂肪层吸收系数μ_af和肌肉层吸收系数μ_am与光源探测器间距SDS之间的关系，并以h_f为自变量与SDS_best建立线性回归模型。研究表明，(1)当0<h_f<0.6 cm时，μ_af和μ_am对SDS_best几乎没有影响，而h_f与SDS_best的建模相关系数R2为0.991 8;(2)任取脂肪层厚度值0.12与0.22 cm作预测，得到预测误差分别为0.030 14和0.020 16，预测误差均能控制在5%以内。该方法能更方便、快速地为测量组织内部信息确定最佳检测位置，并有效削弱浅表组织以及目标层中多次背向散射光的干扰。

Study on Internal Information of the Two-Layered Tissue by Optimizing the Detection Position

As to most methods of detecting the inner information of inhomogenous tissue,a significant issue is that the detection position is ambiguous because of the complexity of human tissue structure and discrepancies among individuals.This paper studies the best source-detector distance (SDSbest )to detect internal information of a fat-muscle tissue with spatially resolved diffuse reflectance spectra.In order to weaken the measurement error caused by the discrepancies among individuals and multiple backscattered photons,and according to the transmission model of light in complex biological tissue,then we added the constraint condition———two ideal“banana shape”paths———to define the effective photon ratio(SNR),which was used to select the best source-detector separations (SDSbest ),and the results from Monte Carlo simulation modified by adding constraint con-dition were statistically analyzed,and we regard the SNR as a basis and analyze the relationship between the fat thickness (hf ),the absorption coefficient of a fat layer (μaf ),the absorption coefficient of a muscle layer (μam ) and the source-detector distance (SDS),and hf is used as the independent variable to develop a linear regres-sion model to predict SDSbest .The result showed thatμaf andμam have no effect on SDSbest when 0