基于ICA的漫射光相关谱血流分离算法研究

血流是人体的一个重要生理参数，实时测量脑部、骨骼肌及乳腺等组织的血流对疾病诊断治疗及手术、重症监护有重要意义。近红外漫射光相关谱（DCS）是新兴的组织血流测量技术，利用DCS技术进行血流测量时，每个距离的光源-探测器（S-D）均含有不同程度的表层组织和深层组织的混合信号，其中表层信号对提取深层组织的血流有较大影响。本文结合N阶线性算法（NL算法）和独立成分分析算法（ICA）对DCS技术获取的近距离和远距离光学信号进行分离处理。计算机仿真表明，本文提出的算法可以较好地分离出表层和深层组织的血流信号，对今后DCS技术在临床的血流测量应用有重要潜力。 

Research on Blood Flow Separation Algorithm of Diffuse Light Correlation Spectrum Based on ICA

Blood flow is an important physiological parameter of the human body. Real-time measurement of blood flow in the brain, skeletal muscle, and breast tissue is of great significance for disease diagnosis, treatment, surgery, and intensive care. Near-Infrared Diffuse Correlation Spectroscopy (DCS) is a new-type tissue blood flow measurement technology. When using DCS technology for blood flow measurement, the light source-detector (S-D) at each distance contains different degrees of mixed signals of superficial and deep tissues, among which the superficial signals show greater impact on the extraction of blood flow in deep tissues. This paper combines the Nth order linear algorithm (NL algorithm) with the independent component analysis algorithm (Independent Component Analysis, ICA) to separate and process the short-range and long-range optical signals obtained by DCS technology. The computer simulation shows that the algorithm proposed in this paper can better separate the blood flow signals of the superficial and deep tissues, and demonstrates important potential for the application of DCS technology in clinical blood flow measurement in the future.