扩散光学成像技术在生物医学领域有着广泛的应用。相较于磁共振成像（MRI）、计算机X射线断层扫描（CT）、正电子发射断层扫描（PET）和超声成像等成像方式，扩散光学成像利用经组织吸收和散射的扩散光进行成像，可无创、无标记、宽场、定量测量氧合血红蛋白、脱氧血红蛋白、血氧、水分、脂质、黑色素等成分浓度和组织功能信息，在安全性、特异性和系统成本等方面有明显优势。本文介绍了扩散光学成像的基本原理，包括光与组织的相互作用和光传播模型，并总结扩散光学成像的相关方法和应用，包括脉搏血氧术、漫射光谱、扩散光学层析成像、荧光分子层析成像和空间频域成像，并对它们的未来发展进行了展望。

Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects. Despite developments of optical imaging technologies, still the di±culties about deep skin vascular imaging have been continued. On the other hand, since hemoglobin concentration of human blood has key role in the veins imaging by optical manner, the used wavelength in vascular imaging, must be chosen considering absorption of hemoglobin. In this research, we constructed a near infrared (NIR) light source because of lower absorption of hemoglobin in this optical region. To obtain vascular image, reflectance geometry was used. Next, from recorded images, vascular network analysis, such as calculation of width of vascular of interest and complexity of selected region were implemented. By comparing with other modalities, we observed that proposed imaging system has great advantages including nonionized radiation, moderate penetration depth of 0.5–3mm and diameter of 1mm, cost-effective and algorithmic simplicity for analysis.

We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44%–72%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.