Peter Naglič

Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin

Measurement of diffuse reflectance spectra (DRS) is a common experimental approach for non-invasive determination of tissue optical properties, as well as objective monitoring of various tissue malformations. Propagation of light in scattering media is often treated in diffusion approximation (DA). The major advantage of this approach is that it leads to enclosed analytical solutions for tissues with layered structure, which includes human skin. Despite the fact that DA solutions were shown to be inaccurate near tissue boundaries, the practicality of this approach makes it quite popular, especially when attempting extraction of specific chromophore concentrations from measured DRS. In this study we analyze the discrepancies between DRS spectra as obtained by using the DA solutions for three-layer skin model and more accurate predictions from Monte Carlo (MC) modeling. Next, we analyze the artifacts which result from the above discrepancies when extracting the parameters of skin structure and composition by fitting the DA solutions to the MC spectra. The reliability and usefulness of such a fit is then tested also on measurements of seasonal changes in otherwise healthy human skin.

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

Abstract. We propose and objectively evaluate an inverse Monte Carlo model for estimation of absorption and reduced scattering coefficients and similarity parameter γ from spatially resolved reflectance (SRR) profiles in the subdiffusive regime. The similarity parameter γ 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 γ 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.

Abstract. 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.

Combining the diffusion approximation and Monte Carlo modeling in analysis of diffuse reflectance spectra from human skin

Light propagation in highly scattering biological tissues is often treated in the so-called diffusion approximation (DA). Although the analytical solutions derived within the DA are known to be inaccurate near tissue boundaries and absorbing layers, their use in quantitative analysis of diffuse reflectance spectra (DRS) is quite common. We analyze the artifacts in assessed tissue properties which occur in fitting of numerically simulated DRS with the DA solutions for a three-layer skin model. In addition, we introduce an original procedure which significantly improves the accuracy of such an inverse analysis of DRS. This procedure involves a single comparison run of a Monte Carlo (MC) numerical model, yet avoids the need to implement and run an inverse MC. This approach is tested also in analysis of experimental DRS from human skin.

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.

Extraction of optical properties in the sub-diffuse regime by spatially resolved reflectance spectroscopy

In this paper, the commonly used inverse Monte Carlo model based on absorption and reduced scattering coefficients is extended by a well-known similarity parameter γ (gamma), which carries additional information on the phase function. Sub-diffuse reflectance measurements at five source-detector separations were used to extract the absorption and reduced scattering coefficients and phase function information encapsulated in γ. The performance of the extended inverse Monte Carlo model was evaluated by simulated and experimental reflectance spectra of turbid phantoms. A three-fold increase in the accuracy of the extended inverse Monte Carlo model that incorporates γ was observed.

Accuracy of experimental data and Monte Carlo simulation lookup table-based inverse models for assessment of turbid media optical properties with diffuse reflectance spectroscopy

Diffuse reflectance spectroscopy is a popular approach for non-invasive assessment of optical properties in turbid media. The acquired spectra are analyzed by various light propagation models or by purely empirical methods. In this study, we quantitatively asses the experimental data and Monte Carlo lookup table-based inverse models by extracting the optical properties from the diffuse reflectance spectra of two carefully prepared turbid phantom sets with exactly defined optical properties. The first turbid phantom set was used for the creation of the experimental data-based lookup table model and calibration of the Monte Carlo lookup table-based inverse model. The second phantom set was used for the evaluation and comparative assessment of the two lookup table-based inverse models. In addition, we investigate the possible sources of errors introduced by the inverse models and show that the lookup table-based models disregard important information regarding the medium scattering phase function.

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

Abstract. 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.

Regression models for real-time estimation of optical and structural sample properties from subdiffusive spatially resolved reflectance

Timely estimation of optical properties from spatially resolved reflectance is a challenging task since the inverse light propagation model needs to be evaluated in real time. In this paper, we propose and extensively evaluate artificial neural network based regression model for estimation of optical and structural sample properties from spatially resolved reflectance acquired by optical fiber probes. We show that the proposed regression model can be prepared from datasets of Monte Carlo simulated spatially resolved reflectance and evaluated significantly faster than the frequently used dense lookup table inverse model. We observed computation time improvements exceeding 4 orders of magnitude. Moreover, the regression model can be easily extended to estimate more free parameters without reducing the estimation accuracy. Finally, we utilized the proposed regression model to estimate optical properties of human skin subjected to dynamically changing contact pressure applied by an optical fiber probe.