Spatial frequency domain spectroscopy of two layer media

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

Significance

Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, μ a and reduced scattering coefficient, μ s ' .

Aim

We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra.

Approach

We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where μ a = 0.44 to 2.45    cm - 1 , and μ s ' = 6.53 to 9.58    cm - 1 . We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model.

Results

When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for μ a and 1.53% for μ s ' , compared to the MCI's 50.9% for μ a and 24.6% for μ s ' . Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for μ a and μ s ' , respectively. The errors for MCI were about eight times higher.

Conclusions

The WIR model presents reliable use-error-robust optical property predictions from DRS data.

Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI)

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.

Quantifying the confounding effect of pigmentation on measured skin tissue optical properties: a comparison of colorimetry with spatial frequency domain imaging

SIGNIFICANCE

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging technique for separately quantifying tissue reduced scattering (μs  '  ) and absorption (μa) coefficients at multiple wavelengths, providing wide potential utility for clinical applications such as burn wound characterization and cancer detection. However, measured μs  '   and μa can be confounded by absorption from melanin in patients with highly pigmented skin. This issue arises because epidermal melanin is highly absorbing for visible wavelengths and standard homogeneous light-tissue interaction models do not properly account for this complexity. Tristimulus colorimetry (which quantifies pigmentation using the L  *   "lightness" parameter) can provide a point of comparison between μa, μs  '  , and skin pigmentation.

AIM

We systematically compare SFDI and colorimetry parameters to quantify confounding effects of pigmentation on measured skin μs  '   and μa. We assess the correlation between SFDI and colorimetry parameters as a function of wavelength.

APPROACH

μs  '   and μa from the palm and ventral forearm were measured for 15 healthy subjects with a wide range of skin pigmentation levels (Fitzpatrick types I to VI) using a Reflect RS® (Modulim, Inc., Irvine, California) SFDI instrument (eight wavelengths, 471 to 851 nm). L  *   was measured using a Chroma Meter CR-400 (Konica Minolta Sensing, Inc., Tokyo). Linear correlation coefficients were calculated between L  *   and μs  '   and between L  *   and μa at all wavelengths.

RESULTS

For the ventral forearm, strong linear correlations between measured L  *   and μs  '   values were observed at shorter wavelengths (R  >  0.92 at ≤659  nm), where absorption from melanin confounded the measured μs  '  . These correlations were weaker for the palm (R  <  0.59 at ≤659  nm), which has less melanin than the forearm. Similar relationships were observed between L  *   and μa.

CONCLUSIONS

We quantified the effects of epidermal melanin on skin μs  '   and μa measured with SFDI. This information may help characterize and correct pigmentation-related inaccuracies in SFDI skin measurements.

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.

A stepwise method for estimating optical properties of two-layer turbid media from spatial-frequency domain reflectance

This research was conducted to estimate the optical absorption and reduced scattering coefficients of two-layer turbid media using a stepwise method from the spatial-frequency domain reflectance generated by Monte Carlo (MC) simulation. The stepwise method's feasibility for optical property estimations was first investigated by comparing the reflectance generated by the diffusion model and MC simulation for one-layer and two-layer turbid media. The results showed that, with proper frequency selection, the one-layer model could be used for estimating the optical properties of the first layer of the two-layer turbid media. A sample-based calibration method was proposed for calibrating discrepancies of the reflectance between the diffusion model and MC simulation. This significantly improved the parameter estimation accuracy. Results showed that the stepwise method's parameter estimation efficacy and accuracy were much better than that for the one-step method. This was especially true when estimating the absorption coefficient. Absolute error contour maps were generated in order to determine the constraining conditions for the first-layer thickness. It was found that, when each layer's optical properties are within the range of 0.005 mm^-1 ≤ μa ≤ 0.04 mm^-1 and 0.69 mm^-1 ≤ μs'≤ 2.2 mm^-1, the first-layer's minimum thickness-for which the first layer's optical properties could be accurately estimated-could be as small as 0.2 mm. Further, the first layer's maximum thickness could not exceed 2.0 mm, in order to have acceptable estimations of the optical properties of the second layer.

Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging

Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.

Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2-f_x) for optical property extractions. The use of more than two frequencies (multi-f_x) can vastly improve the accuracy and reduce uncertainties in optical property estimates for some tissue types, but it has been limited in practice due to the slow speed of available inversion algorithms. We present a deep learning solution that eliminates this bottleneck by solving the multi-f_x inverse problem 300× to 100,000× faster, with equivalent or improved accuracy compared to competing methods. The proposed deep learning inverse model will help to enable real-time and highly accurate tissue measurements with SFDI.

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.

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.

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.

Momentum transfer Monte Carlo for the simulation of laser speckle imaging and its application in the skin

Due to its simplicity and low cost, laser speckle imaging (LSI) has achieved widespread use in biomedical applications. However, interpretation of the blood-flow maps remains ambiguous, as LSI enables only limited visualization of vasculature below scattering layers such as the epidermis and skull. Here, we describe a computational model that enables flexible in-silico study of the impact of these factors on LSI measurements. The model uses Monte Carlo methods to simulate light and momentum transport in a heterogeneous tissue geometry. The virtual detectors of the model track several important characteristics of light. This model enables study of LSI aspects that may be difficult or unwieldy to address in an experimental setting, and enables detailed study of the fundamental origins of speckle contrast modulation in tissue-specific geometries. We applied the model to an in-depth exploration of the spectral dependence of speckle contrast signal in the skin, the effects of epidermal melanin content on LSI, and the depth-dependent origins of our signal. We found that LSI of transmitted light allows for a more homogeneous integration of the signal from the entire bulk of the tissue, whereas epi-illumination measurements of contrast are limited to a fraction of the light penetration depth. We quantified the spectral depth dependence of our contrast signal in the skin, and did not observe a statistically significant effect of epidermal melanin on speckle contrast. Finally, we corroborated these simulated results with experimental LSI measurements of flow beneath a thin absorbing layer. The results of this study suggest the use of LSI in the clinic to monitor perfusion in patients with different skin types, or inhomogeneous epidermal melanin distributions.

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.

Quantitative skin assessment using spatial frequency domain imaging (SFDI) in patients with or at high risk for pressure ulcers

BACKGROUND AND OBJECTIVE

Pressure ulcers (PU) are a significant problem facing the health system in the United States. Here, we present preliminary case studies demonstrating feasibility of Spatial Frequency Domain Imaging (SFDI) to assess skin status in high-risk populations and pre-existing wounds. SFDI is a wide-field non-contact optical imaging technology that uses structured light to obtain tissue optical properties and of tissue constituents. This study aims to determine the fit of SFDI for PU care and determine the next steps.

STUDY DESIGN/MATERIALS AND METHODS

Patients at risk for pressure ulcers were imaged using a near-infrared SFDI system. SFDI-derived images of tissue function (tissue hemoglobin, tissue oxygen saturation) and structure (tissue scattering) were then compared to each other as well as a blinded dermatologist's clinical impressions.

RESULTS

Four case series were chosen to demonstrate the imaging capability of this technology. The first scenario demonstrates normal skin of three patients without skin breakdown with spatially uniform measures of tissue oxygen saturation, scattering, and blood volume. The second scenario demonstrates a stage II PU; the third case shows non-blanchable erythema of an unstageable PU; a fourth scenario is a clinically indistinguishable skin rash versus early stages of a PU. In all these cases, we observe spatial changes in tissue constituents (decrease in tissue oxygen saturation, increased blood pooling, decreased scattering).

CONCLUSION

We have presented the first use of SFDI for pressure ulcer imaging and staging. This preliminary study demonstrates the feasibility of this optical technology to assess tissue oxygen saturation and blood volume status in a quantitative manner. With the proposed improvements in modeling and hardware, SFDI has potential to provide a means for pressure ulcer risk stratification, healing and staging. Lasers Surg. Med. 49:827-834, 2017 © 2017 Wiley Periodicals, Inc.

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.

Noncontact imaging of burn depth and extent in a porcine model using spatial frequency domain imaging

The standard of care for clinical assessment of burn severity and extent lacks a quantitative measurement. In this work, spatial frequency domain imaging (SFDI) was used to measure 48 thermal burns of graded severity (superficial partial, deep partial, and full thickness) in a porcine model. Functional (total hemoglobin and tissue oxygen saturation) and structural parameters (tissue scattering) derived from the SFDI measurements were monitored over 72 h for each burn type and compared to gold standard histological measurements of burn depth. Tissue oxygen saturation (stO₂) and total hemoglobin (ctHbT) differentiated superficial partial thickness burns from more severe burn types after 2 and 72 h, respectively (p < 0.01), but were unable to differentiate deep partial from full thickness wounds in the first 72 h. Tissue scattering parameters separated superficial burns from all burn types immediately after injury (p < 0.01), and separated all three burn types from each other after 24 h (p < 0.01). Tissue scattering parameters also showed a strong negative correlation to histological burn depth as measured by vimentin immunostain (r² > 0.89). These results show promise for the use of SFDI-derived tissue scattering as a correlation to burn depth and the potential to assess burn depth via a combination of SFDI functional and structural parameters.

Noncontact imaging of burn depth and extent in a porcine model using spatial frequency domain imaging

The standard of care for clinical assessment of burn severity and extent lacks a quantitative measurement. In this work, spatial frequency domain imaging (SFDI) was used to measure 48 thermal burns of graded severity (superficial partial, deep partial, and full thickness) in a porcine model. Functional (total hemoglobin and tissue oxygen saturation) and structural parameters (tissue scattering) derived from the SFDI measurements were monitored over 72 h for each burn type and compared to gold standard histological measurements of burn depth. Tissue oxygen saturation (stO₂) and total hemoglobin (ctHbT) differentiated superficial partial thickness burns from more severe burn types after 2 and 72 h, respectively (p < 0.01), but were unable to differentiate deep partial from full thickness wounds in the first 72 h. Tissue scattering parameters separated superficial burns from all burn types immediately after injury (p < 0.01), and separated all three burn types from each other after 24 h (p < 0.01). Tissue scattering parameters also showed a strong negative correlation to histological burn depth as measured by vimentin immunostain (r² > 0.89). These results show promise for the use of SFDI-derived tissue scattering as a correlation to burn depth and the potential to assess burn depth via a combination of SFDI functional and structural parameters.

Polarization-sensitive hyperspectral imaging in vivo: a multimode dermoscope for skin analysis

Attempts to understand the changes in the structure and physiology of human skin abnormalities by non-invasive optical imaging are aided by spectroscopic methods that quantify, at the molecular level, variations in tissue oxygenation and melanin distribution. However, current commercial and research systems to map hemoglobin and melanin do not correlate well with pathology for pigmented lesions or darker skin. We developed a multimode dermoscope that combines polarization and hyperspectral imaging with an efficient analytical model to map the distribution of specific skin bio-molecules. This corrects for the melanin-hemoglobin misestimation common to other systems, without resorting to complex and computationally intensive tissue optical models. For this system's proof of concept, human skin measurements on melanocytic nevus, vitiligo, and venous occlusion conditions were performed in volunteers. The resulting molecular distribution maps matched physiological and anatomical expectations, confirming a technologic approach that can be applied to next generation dermoscopes and having biological plausibility that is likely to appeal to dermatologists.

Microcirculation assessment using an individualized model for diffuse reflectance spectroscopy and conventional laser Doppler flowmetry

Microvascular assessment would benefit from co-registration of blood flow and hemoglobin oxygenation dynamics during stimulus response tests. We used a fiber-optic probe for simultaneous recording of white light diffuse reflectance (DRS; 475-850 nm) and laser Doppler flowmetry (LDF; 780 nm) spectra at two source-detector distances (0.4 and 1.2 mm). An inverse Monte Carlo algorithm, based on a multiparameter three-layer adaptive skin model, was used for analyzing DRS data. LDF spectra were conventionally processed for perfusion. The system was evaluated on volar forearm recordings of 33 healthy subjects during a 5-min systolic occlusion protocol. The calibration scheme and the optimal adaptive skin model fitted DRS spectra at both distances within 10%. During occlusion, perfusion decreased within 5 s while oxygenation decreased slowly (mean time constant 61 s; dissociation of oxygen from hemoglobin). After occlusion release, perfusion and oxygenation increased within 3 s (inflow of oxygenized blood). The increased perfusion was due to increased blood tissue fraction and speed. The supranormal hemoglobin oxygenation indicates a blood flow in excess of metabolic demands. In conclusion, by integrating DRS and LDF in a fiber-optic probe, a powerful tool for assessment of blood flow and oxygenation in the same microvascular bed has been presented.

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.

Noninvasive clinical assessment of port-wine stain birthmarks using current and future optical imaging technology: a review

Port-wine stain (PWS) birthmarks are one class of benign congenital vascular malformation. Laser therapy is the most successful treatment modality of PWS. Unfortunately, this approach has limited efficacy, with only 10% of patients experiencing complete blanching of the PWS. To address this problem, several research groups have developed technologies and methods designed to study treatment outcome and improve treatment efficacy. This article reviews seven optical imaging techniques currently in use or under development to assess treatment efficacy, focusing on: reflectance spectrophotometers/tristimulus colorimeters; laser Doppler flowmetry and laser Doppler imaging; cross-polarized diffuse reflectance colour imaging system; reflectance confocal microscopy; optical coherence tomography; spatial frequency domain imaging; and laser speckle imaging.

In vivo spatial frequency domain spectroscopy of two layer media

Monitoring of tissue blood volume and local oxygen saturation can inform the assessment of tissue health, healing, and dysfunction. These quantities can be estimated from the contribution of oxyhemoglobin and deoxyhemoglobin to the absorption spectrum of the dermis. However, estimation of blood related absorption in skin can be confounded by the strong absorption of melanin in the epidermis and epidermal thickness and pigmentation varies with anatomic location, race, gender, and degree of disease progression. Therefore, a method is desired that decouples the effect of melanin absorption in the epidermis from blood absorption in the dermis for a large range of skin types and thicknesses. A previously developed inverse method based on a neural network forward model was applied to simulated spatial frequency domain reflectance of skin for multiple wavelengths in the near infrared. It is demonstrated that the optical thickness of the epidermis and absorption and reduced scattering coefficients of the dermis can be determined independently and with minimal coupling. Then, the same inverse method was applied to reflectance measurements from a tissue simulating phantom and in vivo human skin. Oxygen saturation and total hemoglobin concentrations were estimated from the volar forearms of weakly and strongly pigmented subjects using a standard homogeneous model and the present two layer model.

Spatial frequency domain imaging of port wine stain biochemical composition in response to laser therapy: a pilot study

BACKGROUND AND OBJECTIVE

Objective methods to assess port wine stain (PWS) response to laser treatment have been the subject of various research efforts for several years. Herein, we present a pilot study using a newly developed, light emitting diode (LED) based spatial frequency domain imaging (SFDI) device to record quantitatively biochemical compositional changes in PWS after laser therapy.

STUDY DESIGN/PATIENTS AND METHODS

A SFDI system was used to image before, and after, five PWS treatment sessions [n = 4 subjects (one subject was imaged before and after two consecutive laser treatments)]. SFDI derived wide-field optical properties (absorption and scattering) and tissue chromophore concentrations including oxy-hemoglobin (ctO(2) Hb), deoxy-hemoglobin (ctHHb), total hemoglobin (ctTHb), and tissue oxygen saturation (stO(2) ) are presented for skin imaged prior to and immediately after laser treatment. The SFDI derived images were analyzed by comparing the above measurements in PWS to those of normal skin and tracking changes immediately after laser exposure.

RESULTS

Elevated oxy-hemoglobin (>20%) and tissue oxygen saturation (>5%) were measured in all PWS lesions and compared to values for normal skin prior to treatment. Laser treatment resulted in an increase in deoxy-hemoglobin (>100%), decrease in tissue oxygen saturation (>10%), and reduced scattering (>15%) in all PWS lesions. One subject was followed before and after two consecutive laser treatments and the overall improvement in PWS lesion blanching was quantitatively assessed by measuring a 45% decrease in dermal blood volume.

CONCLUSION

SFDI is a rapid non-contact wide-field optical technique that shows potential as an imaging device that can be used to quantify biochemical compositional changes in PWS after laser therapy. Future work will investigate the potential of SFDI to provide intra-operative guidance for laser therapy of PWS lesions on an individual patient basis.

Diffuse optical imaging using spatially and temporally modulated light

The authors describe the development of diffuse optical imaging (DOI) technologies, specifically the use of spatial and temporal modulation to control near infrared light propagation in thick tissues. We present theory and methods of DOI focusing on model-based techniques for quantitative, in vivo measurements of endogenous tissue absorption and scattering properties. We specifically emphasize the common conceptual framework of the scalar photon density wave for both temporal and spatial frequency-domain approaches. After presenting the history, theoretical foundation, and instrumentation related to these methods, we provide a brief review of clinical and preclinical applications from our research as well as our outlook on the future of DOI technology.

Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy

Model based data analysis of diffuse reflectance spectroscopy data enables the estimation of optical and structural tissue parameters. The aim of this study was to present an inverse Monte Carlo method based on spectra from two source-detector distances (0.4 and 1.2 mm), using a multilayered tissue model. The tissue model variables include geometrical properties, light scattering properties, tissue chromophores such as melanin and hemoglobin, oxygen saturation and average vessel diameter. The method utilizes a small set of presimulated Monte Carlo data for combinations of different levels of epidermal thickness and tissue scattering. The path length distributions in the different layers are stored and the effect of the other parameters is added in the post-processing. The accuracy of the method was evaluated using Monte Carlo simulations of tissue-like models containing discrete blood vessels, evaluating blood tissue fraction and oxygenation. It was also compared to a homogeneous model. The multilayer model performed better than the homogeneous model and all tissue parameters significantly improved spectral fitting. Recorded in vivo spectra were fitted well at both distances, which we previously found was not possible with a homogeneous model. No absolute intensity calibration is needed and the algorithm is fast enough for real-time processing.