Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R-2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R-2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This research investigates the mismatches between measurements and simulations by studying the influences from light source and the incident and detection angle on diffuse reflectance profiles. After reducing the mismatches caused by these factors, metamodels linking optical properties with diffuse reflectance profiles were built based on measured and simulated data, both achieving high prediction performance with R-2 values above 0.96, allowing for the creation of simulation-based metamodel applicable to different measurement configurations.