Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer–Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.