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We propose a method to isolate absorption trends confined to the lower layer of a two-layer turbid medium, as is desired in near-infrared spectroscopy (NIRS) of cerebral hemodynamics. Several two-layer Monte Carlo simulations of NIRS time series were generated using a physiologically relevant range of optical properties and varying the absorption coefficients due to bottom-layer, top-layer, and/or global fluctuations. Initial results showed that by measuring absorption trends at two source-detector separations and performing a least-squares fit of one to the other, processed signals strongly resemble the simulated bottom-layer absorption properties. Through this approach, it was demonstrated that fitting coefficients can be estimated within less than +/- 2% of the ideal value without any a priori knowledge of the optical properties present in the model. An analytical approximation for the least-squares coefficient provides physical insight into the nature of errors and suggests ways to reduce them.
Saager et al. (Thu,) studied this question.