Robustness of Diffuse Reflectance Spectra Analysis by Inverse Adding Doubling Algorithm

Analysing diffuse reflectance spectra to extract properties of biological tissue requires modelling of light transport within the tissue, considering its absorption, scattering, and geometrical properties. Due to the layered skin structure, skin tissue models are often divided into multiple layers with their associated optical properties. Typically, in the analysis, some model parameters defining these properties are fixed to values reported in the literature to speed up the fitting process and improve its performance. In the absence of consensus, various studies use different approaches in fixing the model parameters. This study aims to assess the effect of fixing various model parameters in the skin spectra fitting process on the accuracy and robustness of a GPU-accelerated two-layer inverse adding-doubling (IAD) algorithm. Specifically, the performance of the IAD method is determined for noiseless simulated skin spectra, simulated spectra with different levels of noise applied, and in-vivo measured reflectance spectra from hyperspectral images of human hands recorded before, during, and after the arterial occlusion. Our results suggest that fixing multiple parameters to a priori known values generally improves the robustness and accuracy of the IAD algorithm for simulated spectra. However, for in-vivo measured spectra, these values are unknown in advance and fixing optical parameters to incorrect values significantly deteriorates the overall performance. Therefore, we propose a method to improve the fitting performance by pre-estimating model parameters. Our findings could be considered in all future research involving the analysis of diffuse reflectance spectra to extract optical properties of skin tissue.