Influence of the phase function in generalized diffuse reflectance models: review of current formalisms and novel observations

Towards a sensing model using a random laser combined with diffuse reflectance spectroscopy

The previous research proves that the random laser emission reflects not only the scattering properties but also the absorption properties. The random laser is therefore considered a potential tool for optical properties sensing. Although the qualitative sensing using the random laser is extensively investigated, a quantitative measurement of optical properties is still rare. In this study, a generalized mathematical quantitative model using random laser combined with diffuse reflectance spectroscopy is proposed for optical sensing in turbid media. This model describes the gain effect of the active medium and the optical properties effect of the passive medium separately. Rhodamine 6G is used as the active medium. Intralipid and ink are employed to demonstrate the effect of the scattering and absorption, respectively. The peak wavelength shift of the random laser is proved to be an ideal sensing parameter for this sensing model. It is also revealed that the scaling parameters in the sensing model are interrelated and can be simplified to one. With this combined model, the direct sensing of optical properties in diverse turbid media is promising.

Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection

In this study, a general and systematical investigation of sub-diffuse reflectance spectroscopy is implemented. A Gegenbauer-kernel phase function-based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient μ a , reduced scattering coefficient μ s ' and sub-diffusive quantifier γ , simultaneously, at multiple source-detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase-lock-in technique. Furtherly, the trained neural-network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

Multi-Reflectance-Spectroscopy, Part I: Theory and Calculations Using Simulated Milk Samples

Optical methods are appropriate for monitoring of constituents in suspensions and emulsions. A simple multi-wavelength, multi-reflectance spectroscopic technique, called MRS-Technology, is introduced. Two different signals of a sample are measured: the reflectance from a small and from a large measuring volume corresponding to the reduced scattering coefficient μSCA' and to the sum of μSCA' and the absorption coefficient μ_ABS, respectively. Analytical relations between the MRS reflectance and μ_ABS as well as μSCA' are derived. The investigations on MRS method are carried out using milk as an example. For this purpose "virtual" milk samples are defined. μ_ABS and μSCA' are calculated by means of the Mie scattering theory in the ultraviolet-visible-shortwave near-infrared (UV-Vis-SWNIR) spectral range. Using this data analytical reflectances can be calculated based on MRS theory as well as numerical reflectances obtained by Monte Carlo (MC) simulation. Analytical and numerical results are compared and investigated. The spectral behavior of the analytical reflectances is very similar to that of the numerical MC reflectances in the case of medium and low absorptions. By means of simple multilinear regression techniques (MLR), simple correlations between fat and protein volume fractions and reflectances could be generated with acceptable root mean square error (RMSE) values. Each correlation shows that best results will be achieved by using reflectances at sample-specific wavelengths for small and large measuring volumes of a sample indicating the potential of the MRS-Technology.

Phase function estimation from a diffuse optical image via deep learning

Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey–Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey–Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

Needle-compatible miniaturized optoelectronic sensor for pancreatic cancer detection

Pancreatic cancer is one of the deadliest cancers, with a 5-year survival rate of <10%. The current approach to confirming a tissue diagnosis, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), requires a time-consuming, qualitative cytology analysis and may be limited because of sampling error. We designed and engineered a miniaturized optoelectronic sensor to assist in situ, real-time, and objective evaluation of human pancreatic tissues during EUS-FNA. A proof-of-concept prototype sensor, compatible with a 19-gauge hollow-needle commercially available for EUS-FNA, was constructed using microsized optoelectronic chips and microfabrication techniques to perform multisite tissue optical sensing. In our bench-top verification and pilot validation during surgery on freshly excised human pancreatic tissues (four patients), the fabricated sensors showed a comparable performance to our previous fiber-based system. The flexibility in source-detector configuration using microsized chips potentially allows for various light-based sensing techniques inside a confined channel such as a hollow needle or endoscopy.

Approaches for calibration and validation of near-infrared optical methods for oxygenation monitoring

Pulse oximetry for arterial oxygenation monitoring and tissue oximetry for monitoring of cerebral oxygenation or muscle oxygenation are based on quantitative in vivo diffuse optical spectroscopy. However, in both cases the information on absolute or relative concentration of human tissue constituents and especially on hemoglobin oxygenation can often not be retrieved by model-based analysis. An in vivo calibration against an accepted reference measurement can be a practical alternative. Pulse oximeters and most of commercial cerebral tissue oximeters rely on empirical calibration based on invasive controlled human desaturation studies. As invasive in vivo tests on healthy subjects are ethically disputable and should be limited to exceptional cases this calibration practice is unsatisfactory. We present the current status and problems of calibration and validation in pulse oximetry and cerebral tissue oximetry including the pros and cons of in vivo as well as in vitro methods. We emphasize various digital and physical phantom approaches and discuss the prospects of their application and possible further developments.

Origin of improved depth penetration in dual-axis optical coherence tomography: a Monte Carlo study

Recent studies have demonstrated that extended imaging depth can be achieved using dual-axis optical coherence tomography (DA-OCT). By illuminating and collecting at an oblique angle, multiple forward scattered photons from large probing depths are preferentially detected. However, the mechanism behind the enhancement of imaging depth needs further illumination. Here, the signal of a DA-OCT system is studied using a Monte Carlo simulation. We modeled light transport in tissue and recorded the spatial and angular distribution of photons exiting the tissue surface. Results indicate that the spatial separation and offset angle created by the non-telecentric scanning configuration promote the collection of more deeply propagating photons than conventional on-axis OCT.

Light scattering methods for tissue diagnosis

Light scattering has become a common biomedical research tool, enabling diagnostic sensitivity to myriad tissue alterations associated with disease. Light-tissue interactions are particularly attractive for diagnostics due to the variety of contrast mechanisms that can be used, including spectral, angle-resolved, and Fourier-domain detection. Photonic diagnostic tools offer further benefit in that they are non-ionizing, non-invasive, and give real-time feedback. In this review, we summarize recent innovations in light scattering technologies, with a focus on clinical achievements over the previous ten years.

Real-time optical spectroscopic monitoring of nonirrigated lesion progression within atrial and ventricular tissues

Despite considerable advances in guidance of radiofrequency ablation (RFA) therapy for the treatment of cardiac arrhythmias, success rates have been hampered by a lack of tools for precise intraoperative evaluation of lesion extent. Near-infrared spectroscopic (NIRS) techniques are sensitive to tissue structural and biomolecular properties, characteristics that are directly altered by radiofrequency (RF) treatment. In this work, a combined NIRS-RFA catheter is developed for real-time monitoring of tissue reflectance during RF energy delivery. An algorithm is proposed for processing NIR spectra to approximate nonirrigated lesion depth in both atrial and ventricular tissues. The probe optical geometry was designed to bias measurement influence toward absorption enabling enhanced sensitivity to changes in tissue composition. A set of parameters termed "lesion optical indices" are defined encapsulating spectral differences between ablated and unablated tissue. Utilizing these features, a model for real-time tissue spectra classification and lesion size estimation is presented. Experimental validation conducted within freshly excised porcine cardiac specimens showed strong concordance between algorithm estimates and post-hoc tissue assessment.

Review of structured light in diffuse optical imaging

Diffuse optical imaging probes deep living tissue enabling structural, functional, metabolic, and molecular imaging. Recently, due to the availability of spatial light modulators, wide-field quantitative diffuse optical techniques have been implemented, which benefit greatly from structured light methodologies. Such implementations facilitate the quantification and characterization of depth-resolved optical and physiological properties of thick and deep tissue at fast acquisition speeds. We summarize the current state of work and applications in the three main techniques leveraging structured light: spatial frequency-domain imaging, optical tomography, and single-pixel imaging. The theory, measurement, and analysis of spatial frequency-domain imaging are described. Then, advanced theories, processing, and imaging systems are summarized. Preclinical and clinical applications on physiological measurements for guidance and diagnosis are summarized. General theory and method development of tomographic approaches as well as applications including fluorescence molecular tomography are introduced. Lastly, recent developments of single-pixel imaging methodologies and applications are reviewed.

Quantitative diagnosis of tissue microstructure with wide-field high spatial frequency domain imaging

Non-contact and minimally invasive endoscopic optical imaging is an invaluable diagnostic tool for tissue examination and cancer screening. The point sampling techniques with high sensitivity to the tissue microenvironment are time consuming and often not affordable in clinics. There is a major clinical need for a large field-of-view (FOV) rapid screening method to highlight subtle tissue microstructural alterations. To address this unmet need, we have developed High Spatial Frequency Domain Imaging (HSFDI)-a non-contact imaging modality that spatially maps the tissue microscopic scattering structures over a large field of view (>1cm2). Based on an analytical reflectance model of sub-diffusive light from forward-peaked highly scattering media, HSFDI quantifies the spatially-resolved parameters of the light scattering phase function (i.e., the backscattering probability and the light spreading length) from the reflectance of structured light modulated at high spatial frequencies. Enhanced signal to noise ratio (SNR) is achieved at even ultra-high modulation frequencies with single snapshot multiple frequency demodulation (SSMD). The variations in tissue microstructures, including the strength of the background (pudding) refractive index fluctuation and the prominent scattering structure (plum) morphology, can then be inferred. After validation with optical phantoms, measurements of fresh ex vivo tissue samples revealed significant contrast and differentiation of the phase function parameters between different types and disease states (normal, inflammatory, and cancerous) of tissue whereas tissue absorption and reduced scattering coefficients only show modest changes. HSFDI may provide wide-field images of microscopic structural biomarkers unobtainable with either diffuse light imaging or point-based optical sampling. Potential clinical applications include the rapid screening of excised tissue and the noninvasive examination of suspicious lesions during operation.

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.

Self-Calibration Phenomenon for Near-Infrared Clinical Measurements: Theory, Simulation, and Experiments

An irradiated turbid medium scatters the light in accordance to its optical properties. Near-infrared (NIR) clinical methods, which are based on spectral-dependent absorption, suffer from an inherent error due to spectral-dependent scattering. We present here a unique spatial point, that is, iso-pathlength (IPL) point, on the surface of a tissue at which the intensity of re-emitted light remains constant. This scattering-indifferent point depends solely on the medium geometry. On the basis of this natural phenomenon, we suggest a novel optical method for self-calibrated clinical measurements. We found that the IPL point exists in both cylindrical and semi-infinite tissue geometries (Supporting Information, Video file). Finally, in vivo human finger and mice measurements are used to validate the crossing point between the intensity profiles of two wavelengths. Hence, measurements at the IPL point yield an accurate absorption assessment while eliminating the scattering dependence. This finding can be useful for oxygen saturation determination, NIR spectroscopy, photoplethysmography measurements, and a wide range of optical sensing methods for physiological aims.

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.

Finite element simulation of light transfer in turbid media under structured illumination

The spatial-frequency domain (SFD) imaging technique allows us to estimate the optical properties of biological tissues in a wide field of view. The technique is, however, prone to error in measurement because the two crucial assumptions used for deriving the analytical solution to the diffusion approximation cannot be met perfectly in practical applications. This research mainly focused on modeling light transfer in turbid media under the normal incidence of structured illumination using the finite element method (FEM). Finite element simulations were performed for 50 simulation samples with different combinations of optical absorption and scattering coefficients for varying spatial frequencies, and the results were then compared with the analytical method and Monte Carlo simulation. Relationships between diffuse reflectance and dimensionless absorption and dimensionless scattering coefficients were investigated. The results indicated that the FEM provided reasonable results for diffuse reflectance, compared with the analytical method. Both the FEM and the analytical method overestimated the reflectance for μ_tr/f_x values of greater than 2 and underestimated the reflectance for μ_tr/f_x values of smaller than 2. Larger values of μs'/μ_a yielded better diffuse reflectance estimations than did those of smaller than 10. The reflectance increased nonlinearly with the dimensionless scattering, whereas the reflectance decreased linearly with the dimensionless absorption. It was also observed that diffuse reflectance was relatively stable and insensitive to μs' when the dimensionless scattering was larger than 50. Overall results demonstrate that the FEM is effective for modeling light transfer in turbid media and can be used to explore the effects of crucial parameters for the SFD imaging technique.

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.

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.

Using electron microscopy to calculate optical properties of biological samples

The microscopic structural origins of optical properties in biological media are still not fully understood. Better understanding these origins can serve to improve the utility of existing techniques and facilitate the discovery of other novel techniques. We propose a novel analysis technique using electron microscopy (EM) to calculate optical properties of specific biological structures. This method is demonstrated with images of human epithelial colon cell nuclei. The spectrum of anisotropy factor g, the phase function and the shape factor D of the nuclei are calculated. The results show strong agreement with an independent study. This method provides a new way to extract the true phase function of biological samples and provides an independent validation for optical property measurement techniques.

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%.

Wide-field quantitative imaging of tissue microstructure using sub-diffuse spatial frequency domain imaging

Localized measurements of scattering in biological tissue provide sensitivity to microstructural morphology but have limited utility to wide-field applications, such as surgical guidance. This study introduces sub-diffusive spatial frequency domain imaging (sd-SFDI), which uses high spatial frequency illumination to achieve wide-field sampling of localized reflectances. Model-based inversion recovers macroscopic variations in the reduced scattering coefficient [Formula: see text] and the phase function backscatter parameter (γ). Measurements in optical phantoms show quantitative imaging of user-tuned phase-function-based contrast with accurate decoupling of parameters that define both the density and the size-scale distribution of scatterers. Measurements of fresh ex vivo breast tissue samples revealed, for the first time, unique clustering of sub-diffusive scattering properties for different tissue types. The results support that sd-SFDI provides maps of microscopic structural biomarkers that cannot be obtained with diffuse wide-field imaging and characterizes spatial variations not resolved by point-based optical sampling.

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.

Diagnosis of the phase function of random media from light reflectance

Light reflectance has been widely used to diagnose random media in both in situ and in vivo applications. The quantification of the phase function of the medium from reflectance measurements, however, remains elusive due to the lack of an explicit connection between the light reflectance profile and the phase function. Here we first present an analytical model for reflectance of scattered light at an arbitrary source-detector separation by forward-peaked scattering media such as biological tissue and cells. The model incorporates the improved small-angle scattering approximation (SAA) to radiative transfer for sub-diffusive light reflectance and expresses the dependence of the light reflectance on the phase function of the scattering medium in a closed form. A spreading length scale, l_Θ, is found to characterise subdiffusive light reflectance at the high spatial frequency (close separation) limit. After validation by Monte Carlo simulations, we then demonstrate the application of the model in accurate determination of the complete set of optical properties and the phase function of a turbid medium from the profile of subdiffusive and diffusive light reflectance.

Quantifying phase function influence in subdiffusively backscattered light

Light backscattering at short source-detector separations is considerably influenced by the scattering phase function of a turbid medium. We seek to more precisely relate a medium's subdiffusive backscattering to the angular scattering characteristics of its microstructure. First, we demonstrate the inability of the scattering asymmetry g1 = < cos θ > to predict phase function influence on backscattering and reveal ambiguities related to the established phase function parameter γ. Through the use of high-order similarity relations, we introduce a new parameter that more accurately relates a scattering phase function to its subdiffusive backscattering intensity. Using extensive analytical forward calculations based on solutions to the radiative transfer equation in the spatial domain and spatial frequency domain, we demonstrate the superiority of our empirically derived quantifier σ over the established parameter γ.

Sub-diffuse optical biomarkers characterize localized microstructure and function of cortex and malignant tumor

This study uses a sub-diffusive light transport model to analyze fiber-optic measurements of reflectance spectra to recover endogenous tissue biomarkers and to correct raw fluorescence emissions for distortions from background optical properties. Measurements in tissue-simulating phantoms validated accurate recovery of the reduced scattering coefficient [(0.3-3.4  mm-1), error 10%], blood volume fraction [(1-3 vol%), error 7%], and a dimensionless metric of anisotropic scattering, γ, that is sensitive to submillimeter tissue ultrastructure [(1.29-2.06), error 11%]. In vivo sub-diffusive optical data acquired during clinical neurosurgeries characterize differences in microstructure (γ), perfusion (blood volume), and metabolism (PpIX fluorescence) between normal cortex and malignant tumor.

Toward reliable retrieval of functional information of papillary dermis using spatially resolved diffuse reflectance spectroscopy

Spatially resolved diffuse reflectance spectroscopy (SRDRS) has been employed to quantify tissue optical properties and its interrogation volume is majorly controlled by the source-to-detector separations (SDSs). To noninvasively quantify properties of dermis, a SRDRS setup that includes SDS shorter than 1 mm is required. It will be demonstrated in this study that Monte Carlo simulations employing the Henyey-Greenstein phase function cannot always precisely predict experimentally measured diffuse reflectance at such short SDSs, and we speculated this could be caused by the non-negligible backward light scattering at short SDSs that cannot be properly modeled by the Henyey-Greenstein phase function. To accurately recover the optical properties and functional information of dermis using SRDRS, we proposed the use of the modified two-layer (MTL) geometry. Monte Carlo simulations and phantom experiment results revealed that the MTL probing geometry was capable of faithfully recovering the optical properties of upper dermis. The capability of the MTL geometry in probing the upper dermis properties was further verified through a swine study, and it was found that the measurement results were reasonably linked to histological findings. Finally, the MTL probe was utilized to study psoriatic lesions. Our results showed that the MTL probe was sensitive to the physiological condition of tissue volumes within the papillary dermis and could be used in studying the physiology of psoriasis.

Model-based analysis on the influence of spatial frequency selection in spatial frequency domain imaging

Frequency variation in spatial frequency domain imaging is a powerful tool for adjusting the penetration depth of the imaging signal and the parameter sensitivity toward absorption and diffusive and subdiffusive scattering. Through our computational analysis, using an analytical solution of the radiative transfer equation, we add quantitation to this tool by linking the different spatial frequency regimes to their relative information content and to their absolute depth sensitivity. Special focus is placed on high spatial frequencies by analysis of the phase function parameter γ and its significance and ambiguity in describing subdiffusive scattering.

Subdiffusion reflectance spectroscopy to measure tissue ultrastructure and microvasculature: model and inverse algorithm

Reflectance measurements acquired from within the subdiffusion regime (i.e., lengthscales smaller than a transport mean free path) retain much of the original information about the shape of the scattering phase function. Given this sensitivity, many models of subdiffusion regime light propagation have focused on parametrizing the optical signal through various optical and empirical parameters. We argue, however, that a more useful and universal way to characterize such measurements is to focus instead on the fundamental physical properties, which give rise to the optical signal. This work presents the methodologies that used to model and extract tissue ultrastructural and microvascular properties from spatially resolved subdiffusion reflectance spectroscopy measurements. We demonstrate this approach using ex-vivo rat tissue samples measured by enhanced backscattering spectroscopy.

Review of diverse optical fibers used in biomedical research and clinical practice

Optical fiber technology has significantly bolstered the growth of photonics applications in basic life sciences research and in biomedical diagnosis, therapy, monitoring, and surgery. The unique operational characteristics of diverse fibers have been exploited to realize advanced biomedical functions in areas such as illumination, imaging, minimally invasive surgery, tissue ablation, biological sensing, and tissue diagnosis. This review paper provides the necessary background to understand how optical fibers function, to describe the various categories of available fibers, and to illustrate how specific fibers are used for selected biomedical photonics applications. Research articles and vendor data sheets were consulted to describe the operational characteristics of conventional and specialty multimode and single-mode solid-core fibers, double-clad fibers, hard-clad silica fibers, conventional hollow-core fibers, photonic crystal fibers, polymer optical fibers, side-emitting and side-firing fibers, middle-infrared fibers, and optical fiber bundles. Representative applications from the recent literature illustrate how various fibers can be utilized in a wide range of biomedical disciplines. In addition to helping researchers refine current experimental setups, the material in this review paper will help conceptualize and develop emerging optical fiber-based diagnostic and analysis tools.

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.