MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package.

SIGNIFICANCE: Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes. AIM: Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method. APPROACH: The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations. RESULTS: The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation. CONCLUSIONS: Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.

Optical properties of PlatSil SiliGlass tissue-mimicking phantoms.

In this work, we revise the preparation procedure and conduct an in depth characterization of optical properties for the recently proposed silicone-based tissue-mimicking optical phantoms in the spectral range from 475 to 925 nm. The optical properties are characterized in terms of refractive index and its temperature dependence, absorption and reduced scattering coefficients and scattering phase function related quantifiers. The scattering phase function and related quantifiers of the optical phantoms are first assessed within the framework of the Mie theory by using the measured refractive index of SiliGlass and size distribution of the hollow silica spherical particles that serve as scatterers. A set of purely absorbing optical phantoms in cuvettes is used to evaluate the linearity of the absorption coefficient with respect to the concentration of black pigment that serves as the absorber. Finally, the optical properties in terms of the absorption and reduced scattering coefficients and the subdiffusive scattering phase function quantifier γ are estimated for a subset of phantoms from spatially resolved reflectance using deep learning aided inverse models. To this end, an optical fiber probe with six linearly arranged optical fibers is used to collect the backscattered light at small and large distances from the source fiber. The underlying light propagation modeling is based on the stochastic Monte Carlo method that accounts for all the details of the optical fiber probe.

Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network.

In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. 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 experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers.

Erratum: Optical properties of PlatSil SiliGlass tissue-mimicking phantoms: erratum.

[This corrects the article on p. 3753 in vol. 11.].

Determination of refractive index, size, and solid content of monodisperse polystyrene microsphere suspensions for the characterization of optical phantoms.

Monodisperse polystyrene microspheres are often utilized in optical phantoms since optical properties such as the scattering coefficient and the scattering phase function can be calculated using the Mie theory. However, the calculated values depend on the inherent physical parameters of the microspheres which include the size, refractive index, and solid content. These parameters are often provided only approximately or can be affected by long shelf times. We propose a simple method to obtain the values of these parameters by measuring the collimated transmission of polystyrene microsphere suspensions from which the wavelength-dependent scattering coefficient can be calculated using the Beer-Lambert law. Since a wavelength-dependent scattering coefficient of a single suspension is insufficient to uniquely derive the size, refractive index and solid content by the Mie theory, the crucial and novel step involves suspending the polystyrene microspheres in aqueous sucrose solutions with different sucrose concentrations that modulates the refractive index of the medium and yields several wavelength-dependent scattering coefficients. With the proposed method, we are able to obtain the refractive index within 0.2% in the wavelength range from 500 to 800 nm, the microsphere size to approximately 15 nm and solid content within 2% of their respective reference values.

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study.

Light propagation in biological tissues is frequently modeled by the Monte Carlo (MC) method, which requires processing of many photon packets to obtain adequate quality of the observed backscattered signal. The computation times further increase for detection schemes with small acceptance angles and hence small fraction of the collected backscattered photon packets. In this paper, we investigate the use of a virtually increased acceptance angle for efficient MC simulation of spatially resolved reflectance and estimation of optical properties by an inverse model. We devise a robust criterion for approximation of the maximum virtual acceptance angle and evaluate the proposed methodology for a wide range of tissue-like optical properties and various source configurations.

Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media.

Analytical expressions for sampling the scattering angle from a phase function in Monte Carlo simulations of light propagation are available only for a limited number of phase functions. Consequently, numerical sampling methods based on tabulated values are often required instead. By using Monte Carlo simulated reflectance, we compare two existing and propose an improved numerical sampling method and show that both the number of the tabulated values and the numerical sampling method significantly influence the accuracy of the simulated reflectance. The provided results and guidelines should serve as a good starting point for conducting computationally efficient Monte Carlo simulations with numerical phase function sampling.

Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance.

Estimation of optical properties from subdiffusive reflectance acquired at short source-detector separations is challenging due to the sensitivity to the underlying scattering phase function. In recent studies, a second-order similarity parameter γ has been increasingly used alongside the absorption and reduced scattering coefficients to account for some of the phase function variability. By using Monte Carlo simulations, we show that the influence of the scattering phase function on the subdiffusive reflectance for the biologically relevant variations can be captured sufficiently well by considering γ and a third-order similarity parameter δ. Utilizing this knowledge, we construct an inverse model that estimates the absorption and reduced scattering coefficients, γ and δ, from spatially resolved reflectance. Nearly an order of magnitude smaller errors of the estimated optical properties are obtained in comparison to the inverse model that only composes γ.

Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime.

We propose and objectively evaluate an inverse Monte Carlo model for estimation of absorption and reduced scattering coefficients and similarity parameter ? from spatially resolved reflectance (SRR) profiles in the subdiffusive regime. The similarity parameter ? carries additional information on the phase function that governs the angular properties of scattering in turbid media. The SRR profiles at five source-detector separations were acquired with an optical fiber probe. The inverse Monte Carlo model was based on a cost function that enabled robust estimation of optical properties from a few SRR measurements without a priori knowledge about spectral dependencies of the optical properties. Validation of the inverse Monte Carlo model was performed on synthetic datasets and measured SRR profiles of turbid phantoms comprising molecular dye and polystyrene microspheres. We observed that the additional similarity parameter ? substantially reduced the reflectance variability arising from the phase function properties and significantly improved the accuracy of the inverse Monte Carlo model. However, the observed improvement of the extended inverse Monte Carlo model was limited to reduced scattering coefficients exceeding ?15??cm?1, where the relative root-mean-square errors of the estimated optical properties were well within 10%.

Limitations of the commonly used simplified laterally uniform optical fiber probe-tissue interface in Monte Carlo simulations of diffuse reflectance.

Light propagation models often simplify the interface between the optical fiber probe tip and tissue to a laterally uniform boundary with mismatched refractive indices. Such simplification neglects the precise optical properties of the commonly used probe tip materials, e.g. stainless steel or black epoxy. In this paper, we investigate the limitations of the laterally uniform probe-tissue interface in Monte Carlo simulations of diffuse reflectance. In comparison to a realistic probe-tissue interface that accounts for the layout and properties of the probe tip materials, the simplified laterally uniform interface is shown to introduce significant errors into the simulated diffuse reflectance.

Detection of canine skin and subcutaneous tumors by visible and near-infrared diffuse reflectance spectroscopy.

Cancer is the main cause of canine morbidity and mortality. The existing evaluation of tumors requires an experienced veterinarian and usually includes invasive procedures (e.g., fine-needle aspiration) that can be unpleasant for the dog and the owner. We investigate visible and near-infrared diffuse reflectance spectroscopy (DRS) as a noninvasive optical technique for evaluation and detection of canine skin and subcutaneous tumors ex vivo and in vivo. The optical properties of tumors and skin were calculated in a spectrally constrained manner, using a lookup table-based inverse model. The obtained optical properties were analyzed and compared among different tumor groups. The calculated parameters of the absorption and reduced scattering coefficients were subsequently used for detection of malignant skin and subcutaneous tumors. The detection sensitivity and specificity of malignant tumors ex vivo were 90.0% and 73.5%, respectively, while corresponding detection sensitivity and specificity of malignant tumors in vivo were 88.4% and 54.6%, respectively. The obtained results show that the DRS is a promising noninvasive optical technique for detection and classification of malignant and benign canine skin and subcutaneous tumors. The method should be further investigated on tumors with common origin.

Properties of contact pressure induced by manually operated fiber-optic probes.

We assess the properties of contact pressure applied by manually operated fiber-optic probes as a function of the operator, probe contact area, and sample stiffness. First, the mechanical properties of human skin sites with different skin structures, thicknesses, and underlying tissues were studied by in vivo indentation tests. According to the obtained results, three different homogeneous silicone skin phantoms were created to encompass the observed range of mechanical properties. The silicon phantoms were subsequently used to characterize the properties of the contact pressure by 10 experienced probe operators employing fiber-optic probes with different contact areas. A custom measurement system was used to collect the time-lapse of diffuse reflectance and applied contact pressure. The measurements were characterized by a set of features describing the transient and steady-state properties of the contact pressure and diffuse reflectance in terms of rise time, optical coupling, average value, and variability. The average applied contact pressure and contact pressure variability were found to significantly depend on the probe operator, probe contact area, and surprisingly also on the sample stiffness. Based on the presented results, we propose a set of practical guidelines for operators of manual probes.