Regression models for real-time estimation of optical and structural sample properties from subdiffusive spatially resolved reflectance

Abstract

Timely estimation of optical properties from spatially resolved reflectance is a challenging task since the inverse light propagation model needs to be evaluated in real time. In this paper, we propose and extensively evaluate artificial neural network based regression model for estimation of optical and structural sample properties from spatially resolved reflectance acquired by optical fiber probes. We show that the proposed regression model can be prepared from datasets of Monte Carlo simulated spatially resolved reflectance and evaluated significantly faster than the frequently used dense lookup table inverse model. We observed computation time improvements exceeding 4 orders of magnitude. Moreover, the regression model can be easily extended to estimate more free parameters without reducing the estimation accuracy. Finally, we utilized the proposed regression model to estimate optical properties of human skin subjected to dynamically changing contact pressure applied by an optical fiber probe.