A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.

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Fluorescence spectroscopy has been demonstrated to non-invasively detect changes related to precursors of epithelial cancers, which include decreased fluorescence emission from collagen crosslinks in the connective tissue and increased fluorescence emission from reduced nicotinamide adenine dinucleotide (NADH) in the epithelial tissue. We implemented two-layer forward Monte Carlo models to predict diffuse reflectance and fluorescence intensities at the surface of cervical mucosa given tissue absorption, scattering, and fluorescence properties. The absorption and scattering coefficients of the upper epithelial layer and underlying connective tissue, as well as the epithelial thickness, were estimated from diffuse reflectance spectra using iterative curve fitting. The estimated parameters were used by the fluorescence forward model to obtain quantities needed to relate the intrinsic fluorescence of tissue fluorophores to measured fluorescence intensity. The emission spectra of tissue fluorophores were modeled by skew normal functions, and together with the efficiency of the fluorophores were extracted by fitting the modeled fluorescence spectra to measured spectra using the genetic algorithm. Compared to conventional one-layer forward models, the proposed two-layer models showed significantly smaller errors both in tissue properties estimated from simulated spectra, and in spectral errors of fitting to in-vivo data. Results of a preliminary in-vivo study showed that in seven of eight subjects with histopathologically confirmed dysplasia, the NADH-to-collagen intrinsic fluorescence ratio estimated from the biopsied site was at least two times greater than that estimated from the normal site on the same subject. The ability to more accurately estimate layer-specific intrinsic fluorescence from cervical mucosa could aid the detection of precancers in the cervix as well as other sites including oral and esophageal mucosae.