Abstract
In general, diffuse reflectance spectroscopy (DRS) systems work with photon diffusion models to determine the absorption coefficient μa and reduced scattering coefficient μs' of turbid samples. However, in some DRS measurement scenarios, such as using short source-detector separations to investigate superficial tissues with comparable μa and μs', photon diffusion models might be invalid or might not have analytical solutions. In this study, a systematic workflow of constructing a rapid, accurate photon transport model that is valid at short source-detector separations (SDSs) and at a wide range of sample albedo is revealed. To create such a model, we first employed a GPU (Graphic Processing Unit) based Monte Carlo model to calculate the reflectance at various sample optical property combinations and established a database at high speed. The database was then utilized to train an artificial neural network (ANN) for determining the sample absorption and reduced scattering coefficients from the reflectance measured at several SDSs without applying spectral constraints. The robustness of the produced ANN model was rigorously validated. We evaluated the performance of a successfully trained ANN using tissue simulating phantoms. We a...Continue Reading
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Mar 16, 2016·Biomedical Optics Express·Shih-Yu TzengSheng-Hao Tseng
Mar 16, 2016·Biomedical Optics Express·Yu-Wen ChenSheng-Hao Tseng
Jul 26, 2016·Biomedical Optics Express·Yu-Wen ChenSheng-Hao Tseng
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