KEYWORDS: Monte Carlo methods, Data modeling, Reflectivity, Optical properties, Computer simulations, Spatial frequencies, Signal to noise ratio, Sensors, Scattering, Imaging systems
We propose neural network-based regression model for efficient Monte Carlo simulations of subdiffusive reflectance for spatial frequency domain imaging with low NA and validate the methodology with inverse models for estimation of optical properties.
We present a multi-layered and voxel-based Monte Carlo methods with auxiliary utilities implemented in Python for user-friendly, open-source and multi-purpose modeling of light propagation in turbid media based on PyOpenCL computational platform.
We present a simple approach to determine the refractive index of polystyrene microspheres which are frequently utilized as scatterers in turbid phantoms. The approach is based on Mie theory and transmittance measurements of polystyrene microspheres suspended in media with different refractive indices allowing simultaneous optimization of the diameter and refractive index of the polystyrene microspheres. The refractive index of the medium is changed through the addition of sucrose. Based on our preliminary results, the estimated refractive index of polystyrene microspheres deviates from the literature values by 0.2% and the estimated diameter by 20 nm from the nominal value provided by the manufacturer.
Reflectance spectroscopy shows itself as a useful tool to characterize turbid media, such as biological tissues. The light backscattered from the medium is usually collected by imaging systems or optical fiber probes. In this work we used an optical fiber probe, with a linear arrangement of the source and detection fibers that allows spatially resolved reflectance (SRR) measurements. Through the use of inverse model, the collected SRR can be exploited to estimate the optical properties of the turbid medium. The estimation process involves matching of the measured and simulated SRR that accounts for all the details of the measurement setting. At small source-detector separations and/or non-negligible absorbance, the reflectance becomes highly dependent on the scattering phase function of the medium, which can be efficiently described by the higher order Legendre moments and related scattering phase function quantifiers (PFQ). In our previous studies, we utilized the Gegenbauer Kernel (GK) scattering phase function to describe the light propagation in turbid samples. However, the domain of GK-based PFQs is quite small and fails to fully encompass the scattering phase functions of microspherical suspensions, typically used for calibration and validation of SRR measurement settings. This limitation could be overcome by utilizing scattering phase function models with a large PFQ domain that may also lead to more accurate and robust inverse model predictions. To verify this hypothesis, we evaluate various scattering phase function models that maximize the PFQ domain and experimentally validate the inverse models by SRR collected from optical phantoms and various turbid samples.
KEYWORDS: Monte Carlo methods, Reflectivity, Computer simulations, Optical properties, Scattering, Signal to noise ratio, Photon transport, Error analysis, Optical fibers, Data modeling
Monte Carlo (MC) method is regarded as the gold standard for modeling the light transport in biological tissues. Due to the stochastic nature of the MC method, many photon packets need to be processed to obtain an adequate quality of the simulated reflectance. The number of required photon packets further increases if the numerical aperture of the detection scheme is low. Consequently, extensively long simulation times may be required to obtain adequate quality of the reflectance for such detection schemes. In this paper we propose an efficient regression model that maps reflectance simulated at the maximum acceptance angle of 90◦ to the reflectance corresponding to a much smaller realistic acceptance angle. The results of validation on spatially resolved reflectance and inverse models for estimation of optical properties show that the regression models are accurate and do not introduce additional errors into the spatially resolved reflectance or the optical properties estimated by appropriate inverse models from the regressed reflectance.
Experimental setup geometry in Monte Carlo (MC) simulations is often simplified to shorten computation times. We investigate the effect of these simplifications on the accuracy of the spatial frequency domain (SFD) reflectance. We also introduce a new detection scheme in the MC method that eliminates the often overlooked errors arising from the Hankel transform of the spatially discretized reflectance profiles to SFD reflectance. Finally, we propose and evaluate an artificial neural network-based framework capable of estimating high-definition maps of optical properties in real-time.
To overcome the drawbacks of the commonly used lookup table inverse models, we propose a novel custom OpenCL™- accelerated artificial neural network inverse model for spatial frequency domain imaging (https://bitbucket.org/xopto /rftroop). Utilizing a mid-range graphics processing unit, the proposed inverse model can estimate high-definition (1920 × 1080) maps of the absorption and reduced scattering coefficients and two scattering phase function related quantifiers at a rate of more than 50 frames per second. We show that the artificial neural network inverse model can be seamlessly extended to estimate multiple optical properties independently, thus providing a versatile framework that allows introduction of new quantifiers.
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.
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