Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy

Rapid diffused optical imaging for accurate 3D estimation of subcutaneous tissue features

Conventional light imaging in living tissues is limited to depths under 100 μm by the significant tissue scattering. Consequently, few commercial imaging devices can image tissue lesions beneath the surface, or measure their invasion depth, critical in dermatology. We present 3D-multisite diffused optical imaging (3D-mDOI) an approach that combines photon migration techniques from diffuse optical tomography, with automated controls and image analysis techniques for estimating lesion's depth via its optical coefficients. 3D-mDOI is a non-invasive, low-cost, fast, and contact-free instrument capable of estimating subcutaneous tissue structures volumes through multisite-acquisition of re-emitted light diffusion on the sample surface. It offers rapid estimation of Breslow depth, essential for staging melanoma. To standardize the performance, 3D-mDOI employs customized calibrations using physical tissue phantoms, to explore the system's 3D reconstruction capabilities. We find that 3D-mDOI can reconstruct lesions up to 5 mm below the surface, requiring ∼300 s of computation time.

Heuristic absorption calculation in bilayered media from a white Monte Carlo dataset

Monte Carlo (MC) simulations can adequately describe photon migration in layered media; however, storing and querying the resulting dataset may be computationally prohibitive when detailed path data are needed for each photon trajectory. A heuristic approach that significantly reduces the stored information to the average path length traveled by the photons detected in each layer is proposed. Its accuracy is evaluated by comparing it with the exact time point spread function (TPSF) for a bilayered medium. This method, almost exact for small variations in absorption, is potentially useful to provide a small dataset for lookup tables to be used in inverse problems.

Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra

The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the experimental spectra collected with a medical device, and spectra generated by the photons transport numerical simulations. This comparison is usually limited to spectral shape due to the absence of intensity standardization of the experimental DR spectra. This study proposes to theoretically (using photometric calculation) and experimentally (from experimental spectra acquired on optical phantom) establish a corrective factor to obtain common intensity unit for experimental and simulated signals.

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.

Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.

Proposal for a Skin Layer-Wise Decomposition Model of Spatially-Resolved Diffuse Reflectance Spectra Based on Maximum Depth Photon Distributions: A Numerical Study

In the context of cutaneous carcinoma diagnosis based on in vivo optical biopsy, Diffuse Reflectance (DR) spectra, acquired using a Spatially Resolved (SR) sensor configuration, can be analyzed to distinguish healthy from pathological tissues. The present contribution aims at studying the depth distribution of SR-DR-detected photons in skin from the perspective of analyzing how these photons contribute to acquired spectra carrying local physiological and morphological information. Simulations based on modified Cuda Monte Carlo Modeling of Light transport were performed on a five-layer human skin optical model with epidermal thickness, phototype and dermal blood content as variable parameters using (i) wavelength-resolved scattering and absorption properties and (ii) the geometrical configuration of a multi-optical fiber probe implemented on an SR-DR spectroscopic device currently used in clinics. Through histograms of the maximum probed depth and their exploitation, we provide numerical evidence linking the characteristic penetration depth of the detected photons to their wavelengths and four source–sensor distances, which made it possible to propose a decomposition of the DR signals related to skin layer contributions.

Color interaction between resin composite layers: An overview

OBJECTIVE

Resin composite (RC) layering is increasingly popular in restorative dentistry. However, little is known regarding the color interaction between RC layers. The objective of this article is to review the current literature on color interaction between RC layers and its effect on the overall appearance of the double-layer restoration.

OVERVIEW

A literature review was conducted on theoretical and experimental in vitro studies that tested layered RC systems using different thicknesses. This article discusses specimen design, color measurement methods and devices, parameters defined as outcomes, and RC shades and systems used in the studies of double-layered RC that were reviewed here. Relevant clinical findings and gaps that still exist in the literature were also highlighted.

CONCLUSIONS

Various methods to test layered RC were found in the literature. Each study had a unique design and provided valuable information on color interaction between the enamel and dentin layers that could be applied to clinical use. For more predictable esthetic outcomes, manufacturers could provide tailored recommendations specific for their RC systems, stating the recommended thickness and shade for each layer used in the RC layering technique.

CLINICAL SIGNIFICANCE

With increased variability in RC shades and manufacturers, it is often difficult to predict the final overall color of restoration with multiple layers. Advancing research on RC layering will help clinicians deliver the best customized restorative treatment tailored to the needs and expectations of their patients. Future research along these lines can enhance the efficiency of chair time and will increase the satisfaction of both clinicians and patients.

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.

Optical investigation of three-dimensional human skin equivalents: A pilot study

Human skin equivalents (HSEs) are three-dimensional living models of human skin that are prepared in vitro by seeding cells onto an appropriate scaffold. They recreate the structure and biological behaviour of real skin, allowing the investigation of processes such as keratinocyte differentiation and interactions between the dermal and epidermal layers. However, for wider applications, their optical and mechanical properties should also replicate those of real skin. We therefore conducted a pilot study to investigate the optical properties of HSEs. We compared Monte Carlo simulations of (a) real human skin and (b) two-layer optical models of HSEs with (c) experimental measurements of transmittance through HSE samples. The skin layers were described using a hybrid collection of optical attenuation coefficients. A linear relationship was observed between the simulations and experiments. For samples thinner than 0.5 mm, an exponential increase in detected power was observed due to fewer instances of absorption and scattering.

Classification of diffuse light emission profiles for distinguishing skin layer penetration of a needle-free jet injection

In this work, a system is developed for tracking the skin layer to which a needle-free jet injection of fluid has penetrated by incorporating a laser beam into the jet, and measuring the diffuse light emitted from skin tissue. Monitoring the injection in this way offers the ability to improve the reliability of drug delivery with this transdermal delivery method. A laser beam, axially aligned with a jet of fluid, created a distribution of diffuse light around the injection site that varied as the injection progressed. High-speed videography was used to capture the diffuse light emission from laser-coupled jet injections into samples of porcine skin, fat, and muscle. The injection produced a distribution of diffuse light around the injection site that varied as the injection descended. A classifier, trained to distinguish whether the light source was located in the fat or muscle from surface intensity profile measurements, correctly identified the injected layer in 97.2 % of the cases when cross-examined against estimates using the light distribution emitted from the side of the sample.

Validation of an Inverse Fitting Method of Diffuse Reflectance Spectroscopy to Quantify Multi-Layered Skin Optical Properties

Skin consists of epidermis and dermis layers that have distinct optical properties. The quantification of skin optical properties is commonly achieved by modeling photon propagation in tissue using Monte Carlo (MC) simulations and iteratively fitting experimentally measured diffuse reflectance spectra. In order to speed up the inverse fitting process, time-consuming MC simulations have been replaced by artificial neural networks to quickly calculate reflectance spectra given tissue geometric and optical parameters. In this study the skin was modeled to consist of three layers and different scattering properties of the layers were considered. A new inverse fitting procedure was proposed to improve the extraction of chromophore-related information in the skin, including the hemoglobin concentration, oxygen saturation and melanin absorption. The performance of the new inverse fitting procedure was evaluated on 40 sets of simulated spectra. The results showed that the fitting procedure without knowing the epidermis thickness extracted chromophore information with accuracy similar to or better than fitting with known epidermis thickness, which is advantageous for practical applications due to simpler and more cost-effective instruments. In addition, the melanin volume fraction multiplied by the thickness of the melanin-containing epidermis layer was estimated more accurately than the melanin volume fraction itself. This product has the potential to provide a quantitative indicator of melanin absorption in the skin. In-vivo cuff occlusion experiments were conducted and skin optical properties extracted from the experiments were comparable to the results of previously reported in vivo studies. The results of the current study demonstrated the applicability of the proposed method to quantify the optical properties related to major chromophores in the skin, as well as scattering coefficients of the dermis. Therefore, it has the potential to be a useful tool for quantifying skin optical properties in vivo.

Real-time oncological guidance using diffuse reflectance spectroscopy in electrosurgery: the effect of coagulation on tissue discrimination

In breast surgery, a lack of knowledge about what is below the tissue surface may lead to positive tumor margins and iatrogenic damage. Diffuse reflectance spectroscopy (DRS) is a spectroscopic technique that can distinguish between healthy and tumor tissue making it a suitable technology for intraoperative guidance. However, because tumor surgeries are often performed with an electrosurgical knife, the effect of a coagulated tissue layer on DRS measurements must be taken into account. It is evaluated whether real-time DRS measurements obtained with a photonic electrosurgical knife could provide useful information of tissue properties also when tissue is coagulated and cut. The size of the coagulated area is determined and the effect of its presence on DR spectra is studied using ex vivo porcine adipose and muscle tissue. A coagulated tissue layer with a depth of 0.1 to 0.4 mm is observed after coagulating muscle with an electrosurgical knife. The results show that the effect of coagulating adipose tissue is negligible. Using the fat/water ratio's calculated from the measured spectra of the photonic electrosurgical knife, it was possible to determine the distance from the instrument tip to a tissue transition during cutting. In conclusion, the photonic electrosurgical knife can determine tissue properties of coagulated and cut tissue and has, therefore, the potential to provide real-time feedback about the presence of breast tumor margins during cutting, helping surgeons to establish negative margins and improve patient outcome.

Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts

Diffuse reflectance spectroscopy (DRS) is a probe-based spectral biopsy technique used in cancer studies to quantify tissue reduced scattering (μs') and absorption (μa) coefficients and vary in source-detector separation (SDS) to fine-tune sampling depth. In subcutaneous murine tumor allografts or xenografts, a key design requirement is ensuring that the source light interrogates past the skin layer into the tumor without significantly sacrificing signal-to-noise ratio (target of ≥15  dB). To resolve this requirement, a DRS probe was designed with four SDSs (0.75, 2.00, 3.00, and 4.00 mm) to interrogate increasing tissue volumes between 450 and 900 nm. The goal was to quantify percent errors in extracting μa and μs', and to quantify sampling depth into subcutaneous Balb/c-CT26 colon tumor allografts. Using an optical phantom-based experimental method, lookup-tables were constructed relating μa,μs', diffuse reflectance, and sampling depth. Percent errors were <10  %   and 5% for extracting μa and μs', respectively, for all SDSs. Sampling depth reached up to 1.6 mm at the first Q-band of hemoglobin at 542 nm, the key spectral region for quantifying tissue oxyhemoglobin concentration. This work shows that the DRS probe can accurately extract optical properties and the resultant physiological parameters such as total hemoglobin concentration and tissue oxygen saturation, from sufficient depth within subcutaneous Balb/c-CT26 colon tumor allografts. Methods described here can be generalized for other murine tumor models. Future work will explore the feasibility of the DRS in quantifying volumetric tumor perfusion in response to anticancer therapies.

Hybrid method to estimate two-layered superficial tissue optical properties from simulated data of diffuse reflectance spectroscopy

An iterative curve fitting method has been applied in both simulation [J. Biomed. Opt.17, 107003 (2012)JBOPFO1083-366810.1117/1.JBO.17.10.107003] and phantom [J. Biomed. Opt.19, 077002 (2014)JBOPFO1083-366810.1117/1.JBO.19.7.077002] studies to accurately extract optical properties and the top layer thickness of a two-layered superficial tissue model from diffuse reflectance spectroscopy (DRS) data. This paper describes a hybrid two-step parameter estimation procedure to address two main issues of the previous method, including (1) high computational intensity and (2) converging to local minima. The parameter estimation procedure contained a novel initial estimation step to obtain an initial guess, which was used by a subsequent iterative fitting step to optimize the parameter estimation. A lookup table was used in both steps to quickly obtain reflectance spectra and reduce computational intensity. On simulated DRS data, the proposed parameter estimation procedure achieved high estimation accuracy and a 95% reduction of computational time compared to previous studies. Furthermore, the proposed initial estimation step led to better convergence of the following fitting step. Strategies used in the proposed procedure could benefit both the modeling and experimental data processing of not only DRS but also related approaches such as near-infrared spectroscopy.

Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations

A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.

Spectral refractive index assessment of turbid samples by combining spatial frequency near-infrared spectroscopy with Kramers-Kronig analysis

A practical algorithm for estimating the wavelength-dependent refractive index (RI) of a turbid sample in the spatial frequency domain with the aid of Kramers-Kronig (KK) relations is presented. In it, phase-shifted sinusoidal patterns (structured illumination) are serially projected at a high spatial frequency onto the sample surface (mouse scalp) at different near-infrared wavelengths while a camera mounted normally to the sample surface captures the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial absorption maps by logarithmic function, and once the absorption coefficient information is obtained, the imaginary part (k) of the complex RI (CRI), based on Maxell's equations, can be calculated. Using the data represented by k, the real part of the CRI (n) is then resolved by KK analysis. The wavelength dependence of n  (  λ  )   is then fitted separately using four standard dispersion models: Cornu, Cauchy, Conrady, and Sellmeier. In addition, three-dimensional surface-profile distribution of n is provided based on phase profilometry principles and a phase-unwrapping-based phase-derivative-variance algorithm. Experimental results demonstrate the capability of the proposed idea for sample's determination of a biological sample's RI value.

Application of spatially modulated near-infrared structured light to study changes in optical properties of mouse brain tissue during heatstress

Heat stress (HS) is a medical emergency defined by abnormally elevated body temperature that causes biochemical, physiological, and hematological changes. The goal of the present research was to detect variations in optical properties (absorption, reduced scattering, and refractive index coefficients) of mouse brain tissue during HS by using near-infrared (NIR) spatial light modulation. NIR spatial patterns with different spatial phases were used to differentiate the effects of tissue scattering from those of absorption. Decoupling optical scattering from absorption enabled the quantification of a tissue's chemical constituents (related to light absorption) and structural properties (related to light scattering). Technically, structured light patterns at low and high spatial frequencies of six wavelengths ranging between 690 and 970 nm were projected onto the mouse scalp surface while diffuse reflected light was recorded by a CCD camera positioned perpendicular to the mouse scalp. Concurrently to pattern projection, brain temperature was measured with a thermal camera positioned slightly off angle from the mouse head while core body temperature was monitored by thermocouple probe. Data analysis demonstrated variations from baseline measurements in a battery of intrinsic brain properties following HS.

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.

Spatially and spectrally resolved particle swarm optimization for precise optical property estimation using diffuse-reflectance spectroscopy

This paper presents a new approach to estimate optical properties (absorption and scattering coefficients µa and µs) of biological tissues from spatially-resolved spectroscopy measurements. A Particle Swarm Optimization (PSO)-based algorithm was implemented and firstly modified to deal with spatial and spectral resolutions of the data, and to solve the corresponding inverse problem. Secondly, the optimization was improved by fitting exponential decays to the two best points among all clusters of the "particles" randomly distributed all over the parameter space (µs, µa) of possible solutions. The consequent acceleration of all the groups of particles to the "best" curve leads to significant error decrease in the optical property estimation. The study analyzes the estimated optical property error as a function of the various PSO parameter combinations, and several performance criteria such as the cost-function error and the number of iterations in the algorithms proposed. The final one led to error values between ground truth and estimated values of µs and µa less than 6%.

Towards monitoring dysplastic progression in the oral cavity using a hybrid fiber-bundle imaging and spectroscopy probe

Intraepithelial dysplasia of the oral mucosa typically originates in the proliferative cell layer at the basement membrane and extends to the upper epithelial layers as the disease progresses. Detection of malignancies typically occurs upon visual inspection by non-specialists at a late-stage. In this manuscript, we validate a quantitative hybrid imaging and spectroscopy microendoscope to monitor dysplastic progression within the oral cavity microenvironment in a phantom and pre-clinical study. We use an empirical model to quantify optical properties and sampling depth from sub-diffuse reflectance spectra (450–750 nm) at two source-detector separations (374 and 730 μm). Average errors in recovering reduced scattering (5–26 cm⁻¹) and absorption coefficients (0–10 cm⁻¹) in hemoglobin-based phantoms were approximately 2% and 6%, respectively. Next, a 300 μm-thick phantom tumor model was used to validate the probe’s ability to monitor progression of a proliferating optical heterogeneity. Finally, the technique was demonstrated on 13 healthy volunteers and volume-averaged optical coefficients, scattering exponent, hemoglobin concentration, oxygen saturation, and sampling depth are presented alongside a high-resolution microendoscopy image of oral mucosa from one volunteer. This multimodal microendoscopy approach encompasses both structural and spectroscopic reporters of perfusion within the tissue microenvironment and can potentially be used to monitor tumor response to therapy.

Noninvasive assessment of hemodynamic and brain metabolism parameters following closed head injury in a mouse model by comparative diffuse optical reflectance approaches

Optical techniques have gained substantial interest over the past four decades for biomedical imaging due to their unique advantages, which may suggest their use as alternatives to conventional methodologies. Several optical techniques have been successfully adapted to clinical practice and biomedical research to monitor tissue structure and function in both humans and animal models. This paper reviews the analysis of the optical properties of brain tissue in the wavelength range between 500 and 1000 nm by three different diffuse optical reflectance methods: spatially modulated illumination, orthogonal diffuse light spectroscopy, and dual-wavelength laser speckle imaging, to monitor changes in brain tissue morphology, chromophore content, and metabolism following head injury. After induction of closed head injury upon anesthetized mice by weight-drop method, significant changes in hemoglobin oxygen saturation, blood flow, and metabolism were readily detectible by all three optical setups, up to 1 h post-trauma. Furthermore, the experimental results clearly demonstrate the feasibility and reliability of the three methodologies, and the differences between the system performances and capabilities are also discussed. The long-term goal of this line of study is to combine these optical systems to study brain pathophysiology in high spatiotemporal resolution using additional models of brain trauma. Such combined use of complementary algorithms should fill the gaps in each system's capabilities, toward the development of a noninvasive, quantitative tool to expand our knowledge of the principles underlying brain function following trauma, and to monitor the efficacy of therapeutic interventions in the clinic.

Impact of one-layer assumption on diffuse reflectance spectroscopy of skin

Diffuse reflectance spectroscopy (DRS) can be used to noninvasively measure skin properties. To extract skin properties from DRS spectra, you need a model that relates the reflectance to the tissue properties. Most models are based on the assumption that skin is homogenous. In reality, skin is composed of multiple layers, and the homogeneity assumption can lead to errors. In this study, we analyze the errors caused by the homogeneity assumption. This is accomplished by creating realistic skin spectra using a computational model, then extracting properties from those spectra using a one-layer model. The extracted parameters are then compared to the parameters used to create the modeled spectra. We used a wavelength range of 400 to 750 nm and a source detector separation of 250 μm. Our results show that use of a one-layer skin model causes underestimation of hemoglobin concentration [Hb] and melanin concentration [mel]. Additionally, the magnitude of the error is dependent on epidermal thickness. The one-layer assumption also causes [Hb] and [mel] to be correlated. Oxygen saturation is overestimated when it is below 50% and underestimated when it is above 50%. We also found that the vessel radius factor used to account for pigment packaging is correlated with epidermal thickness.

Accurate extraction of optical properties and top layer thickness of two-layered mucosal tissue phantoms from spatially resolved reflectance spectra

We are reporting on an experimental investigation of a movable diffuse reflectance spectroscopy system to extract diagnostically relevant optical properties of two-layered tissue phantoms simulating mucosae that are covered with stratified squamous epithelium. The reflectance spectra were measured at multiple sourcedetector separations using two imaging fiber bundles in contact with the phantoms, one with its optical axis perpendicular to the sample surface (perpendicular probe) and the other with its distal end beveled and optical axis tilted at 45 deg (oblique probe). Polystyrene microspheres and purified human hemoglobin were used to make tissue phantoms whose scattering and absorption properties could be well controlled and theoretically predicted. Monte Carlo simulations were used to predict the reflectance spectra for system calibration and an iterative curve fitting that simultaneously extracted the top layer reduced scattering coefficient, thickness, bottom layer reduced scattering coefficient, and hemoglobin concentration of the phantoms. The errors of the recovered parameters ranged from 7% to 20%. The oblique probe showed higher accuracy in the extracted top layer reduced scattering coefficient and thickness than the perpendicular probe. The developed system and data analysis methods provide a feasible tool to quantify the optical properties in vivo.

Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy

The sampling depth of light for diffuse reflectance spectroscopy is analyzed both experimentally and computationally. A Monte Carlo (MC) model was used to investigate the effect of optical properties and probe geometry on sampling depth. MC model estimates of sampling depth show an excellent agreement with experimental measurements over a wide range of optical properties and probe geometries. The MC data are used to define a mathematical expression for sampling depth that is expressed in terms of optical properties and probe geometry parameters.