The ability to accurately measure layered biological tissue optical properties (OPs) may improve understanding of spectroscopic device performance and facilitate early cancer detection. Towards these goals, we have performed theoretical and experimental evaluations of an approach for broadband measurement of absorption and reduced scattering coefficients at ultraviolet-visible wavelengths. Our technique is based on neural network (NN) inverse models trained with diffuse reflectance data from condensed Monte Carlo simulations. Experimental measurements were performed from 350 to 600 nm with a fiber-optic-based reflectance spectroscopy system. Two-layer phantoms incorporating OPs relevant to normal and dysplastic mucosal tissue and superficial layer thicknesses of 0.22 and 0.44 mm were used to assess prediction accuracy. Results showed mean OP estimation errors of 19% from the theoretical analysis and 27% from experiments. Two-step NN modeling and nonlinear spectral fitting approaches helped improve prediction accuracy. While limitations and challenges remain, the results of this study indicate that our technique can provide moderately accurate estimates of OPs in layered turbid media.

Light-tissue interactions that influence vascular contrast enhancement in narrow band imaging (NBI) have not been the subject of extensive theoretical study. In order to elucidate relevant mechanisms in a systematic and quantitative manner we have developed and validated a Monte Carlo model of NBI and used it to study the effect of device and tissue parameters, specifically, imaging wavelength (415 versus 540 nm) and vessel diameter and depth. Simulations provided quantitative predictions of contrast-including up to 125% improvement in small, superficial vessel contrast for 415 over 540 nm. Our findings indicated that absorption rather than scattering-the mechanism often cited in prior studies-was the dominant factor behind spectral variations in vessel depth-selectivity. Narrow-band images of a tissue-simulating phantom showed good agreement in terms of trends and quantitative values. Numerical modeling represents a powerful tool for elucidating the factors that affect the performance of spectral imaging approaches such as NBI.

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