Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a complex probe-sample interface

Stochastic Monte Carlo method (MC) is often used to model light propagation in biological tissues. Since many photon packets need to be process to attain good quality of the simulated data, the experimental geometry in MC simulations is usually substantially simplified to shorten the computation times. However, such simplifications have been shown to introduce large simulation errors when using optical fiber probes. In our previous study, we have shown that the frequently used laterally uniform probe-sample interface simplification can introduce significant errors into the MC simulations of spatially resolved reflectance (SRR) potentially exceeding 200 %. Unfortunately, using full details of the probe tip in the MC simulations breaks down the radial symmetry of the detection scheme. Consequently, the simulation time required to obtain a good quality SRR increases by about two orders of magnitude. In this study, we introduce a framework for efficient and accurate MC simulations of SRR acquired by optical fiber probes that accounts for all the details of the probe tip including reflectance from the stainless steel and the refractive indices of the epoxy fill and optical fibers. For this purpose, we introduce an efficient regression model that maps SRR obtained through fast MC simulations based on simplified geometry to the SRR simulated by full details of the probe-sample interface. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and build inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function related quantifiers.