Abstract

Near-infrared spectroscopy is a noninvasive optical method used primarily to monitor tissue oxygenation due to the absorption properties of hemoglobin. Accurate estimation of hemoglobin concentrations and other light absorbers requires techniques that can separate the effect of absorption from the much greater effect of light scattering. One of the most advanced methods is time-resolved near-infrared spectroscopy (TR-NIRS), which measures the absorption and scattering coefficients of a turbid medium by modeling the recorded distribution time of flight of photons. A challenge with TR-NIRS is that it requires accurate characterization of the dispersion caused by the system. In this study, we present a method for circumventing this problem by applying statistical moment analysis to two time-of-flight distributions measured at separated source–detector distances. Simulations based on analytical models and Monte Carlo code, and tissue-mimicking phantoms, were used to demonstrate its accuracy for source–detector distances typically used in neuroimaging applications. The simplicity of the approach is well suited to real-time applications requiring accurate quantification of the optical properties of a turbid medium.

© 2016 Optical Society of America

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