Effect of Scalp Hair Follicles on NIRS Quantification by Monte Carlo Simulation and Visible Chinese Human Dataset

Abstract

Near-infrared spectroscopy (NIRS) has increasingly been used to noninvasively determine hemodynamic concentration change noninvasively by detecting light intensity changes. The effect of scalp hair follicle (SHF) on NIRS quantification is highlighted since its dark pigmentations is a strong absorption source to contaminate the NIRS signal. Here we have incorporated the Monte Carlo modeling for light transport in voxelized media, and visible Chinese human with high precision in depicting three-dimensional human anatomical structures, to study the effect of SHF density on NIRS quantification. The results quantified the strong impact of SHF on NIRS measurements and revealed that the detected light intensity signal decreased by 15%-80% when SHF density varied from 1% to 11.1% at Asian human range. More surprisingly, the hemodynamics-interpreted brain activation could be miscalculated by 11.7%-292.24% linearly with SHF density varied in 1%-11.1%. It is the first time that the effect of SHF on NIRS measurements has been quantitatively evaluated and the dramatic influence of SHF is outlined to be seriously concerned. The finding of the linear correlation between NIRS signal underestimation and the density of scalp hair follicles also indicate a potential calibration method to eliminate the SHF effect on NIRS measurement.