Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy

Noncontact Diffuse Reflectance Spectroscopy of Synovial Fluid Samples for Rapid Identification of Infections

Severe joint infections, such as septic arthritis, require rapid diagnostic testing of the synovial fluid aspirated from joints level so that a surgical team can be assembled quickly. We present a diffuse reflectance spectroscopy (DRS) system for noncontact determination of infection. Using a light-tight syringe holder and fiber optic probe, diffusely reflected light from 475 to 655 nm was acquired from 18 patient samples through the wall of a syringe in a noncontact and sterile manner. We determined the reflectance ratios at two different wavelengths-R490/R600 and R580/R600 and found statistically significant differences (p < 0.05) in both ratios between the infected and noninfected groups. Critically, the R490/R600 and R580/R600 ratios were significantly correlated with clinical biomarkers-the white blood cell (WBC) and red blood cell (RBC) counts, respectively. This study demonstrates the potential of DRS as a rapid diagnostic tool for joint infections.

Application of transfer learning for rapid calibration of spatially resolved diffuse reflectance probes for extraction of tissue optical properties

Significance

Treatment planning for light-based therapies including photodynamic therapy requires tissue optical property knowledge. This is recoverable with spatially resolved diffuse reflectance spectroscopy (DRS) but requires precise source-detector separation (SDS) determination and time-consuming simulations.

Aim

An artificial neural network (ANN) to map from DRS at multiple SDS to optical properties was created. This trained ANN was adapted to fiber-optic probes with varying SDS using transfer learning (TL).

Approach

An ANN mapping from measurements to Monte Carlo simulation to optical properties was created with one fiber-optic probe. A second probe with different SDS was used for TL algorithm creation. Data from a third were used to test this algorithm.

Results

The initial ANN recovered absorber concentration with RMSE = 0.29    μ M (7.5% mean error) and μ s ' at 665 nm (μ s , 665 ' ) with RMSE = 0.77    cm - 1 (2.5% mean error). For probe 2, TL significantly improved absorber concentration (0.38 versus 1.67    μ M RMSE, p = 0.0005 ) and μ ' s , 665 (0.71 versus 1.8    cm - 1 RMSE, p = 0.0005 ) recovery. A third probe also showed improved absorber (0.7 versus 4.1    μ M RMSE, p < 0.0001 ) and μ s , 665 ' (1.68 versus 2.08    cm - 1 RMSE, p = 0.2 ) recovery.

Conclusions

TL-based probe-to-probe calibration can rapidly adapt an ANN created for one probe to similar target probes, enabling accurate optical property recovery with the target probe.

Diffuse reflectance spectroscopy and imaging for non-invasive objective assessment of genitourinary syndrome of menopause: a pilot study

The genitourinary symptom of menopause (GSM) affects up to 65% of women, resulting in symptoms such as vulvovaginal dryness, discomfort, and dysuria, which significantly impacts quality of life. The current assessment methods rely on subjective questionnaires that can be influenced by individual differences, as well as invasive measurements that are time-consuming and not easily accessible. In this study, we explore the potential of a non-invasive and objective assessment tool called diffuse reflectance spectroscopy and imaging (DRSI) to evaluate tissue chromophores, including water, lipid, oxyhemoglobin, and deoxyhemoglobin. These measurements provide information about moisture content, lipid levels, oxygen saturation, and blood fraction, which can serve as surrogate markers for genital estrogen levels. Our findings reveal distinct differences in these chromophores among pre, peri, and postmenopausal subjects. By using lipid and blood fraction tissue chromophores in a K-Nearest Neighbour classifier model, we achieved a prediction accuracy of 65% compared to vaginal maturation index (VMI) that is clinically used to assess estrogen-related hormonal changes. When age was included as the third feature, the accuracy increased to 78%. We believe that by refining the study protocol and configuring the fiber probe to examine tissue chromophores both in the superficial vulva skin for epidermal water content and the deeper layers, DRSI has the potential to provide objective diagnosis and aid in monitoring the treatment outcome of GSM.

Noninvasive detection of diabetes mellitus based on skin fluorescence and diffuse reflectance spectroscopy

There is an urgent need for a mass population screening tool for diabetes. Skin tissue contains a large number of endogenous fluorophores and physiological parameter markers related to diabetes. We built an excitation-emission spectrum measurement system with the excited light sources of 365, 395, 415, 430, and 455 nm to extract skin characteristics. The modeling experiment was carried out to design and verify the accuracy of the recovery of tissue intrinsic discrete three-dimensional fluorescence spectrum. Blood oxygen modeling experiment results indicated the accuracy of the physiological parameter extraction algorithm based on the diffuse reflectance spectrum. A community population cohort study was carried out. The tissue-reduced scattering coefficient and scattering power of the diabetes were significantly higher than normal control groups. The Gaussian multi-peak fitting was performed on each excitation-emission spectrum of the subject. A total of 63 fluorescence features containing information such as Gaussian spectral curve intensity, central wavelength position, and variance were obtained from each person. Logistic regression was used to construct the diabetes screening model. The results showed that the area under the receiver operating characteristic curve of the model for predicting diabetes was 0.816, indicating a high diagnostic value. As a rapid and non-invasive detection method, it is expected to have high clinical value.

Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide, representing 11.3% of the diagnosed cancer cases and resulting in 10.2% (0.88 million) of the cancer related deaths in 2020. CRCs are typically detected at the late stage, which leads to high mortality and morbidity. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications. Patient survival could be increased by improving precision in surgical resection using accurate surgical guidance tools based on diffuse reflectance spectroscopy (DRS). DRS enables real-time tissue identification for potential cancer margin delineation through determination of the circumferential resection margin (CRM), while also supporting non-invasive and label-free approaches for laparoscopic surgery to avoid short-term complications of open surgery as suitable. In this study, we have estimated the scattering properties and chromophore concentrations based on 2949 DRS measurements of freshly excised ex vivo specimens of 47 patients, and used this estimation to classify normal colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630 μm source-detector distance (SDD; probe 1) and 2500 μm SDD (probe 2) to measure tissue layers ∼0.5-1 mm and ∼0.5-2 mm deep, respectively. By using the 5-fold cross-validation of machine learning models generated with the classification and regression tree (CART) algorithm, we achieved 95.9 ± 0.7% sensitivity, 98.9 ± 0.3% specificity, 90.2 ± 0.4% accuracy, and 95.5 ± 0.3% AUC for probe 1. Similarly, we achieved 96.9 ± 0.8% sensitivity, 98.9 ± 0.2% specificity, 94.0 ± 0.4% accuracy, and 96.7 ± 0.4% AUC for probe 2.

Application of Transfer Learning for Rapid Calibration of Spatially-resolved Diffuse Reflectance Probes for Extraction of Tissue Optical Properties

Significance

Treatment planning for light-based therapies including photodynamic therapy requires tissue optical property knowledge. These are recoverable with spatially-resolved diffuse reflectance spectroscopy (DRS), but requires precise source-detector separation (SDS) determination and time-consuming simulations.

Aim

An artificial neural network (ANN) to map from DRS at short SDS to optical properties was created. This trained ANN was adapted to fiber-optic probes with varying SDS using transfer learning.

Approach

An ANN mapping from measurements to Monte Carlo simulation to optical properties was created with one fiber-optic probe. A second probe with different SDS was used for transfer learning algorithm creation. Data from a third were used to test this algorithm.

Results

The initial ANN recovered absorber concentration with RMSE=0.29 µM (7.5% mean error) and µ s ' at 665 nm (µ s,665 ' ) with RMSE=0.77 cm -1 (2.5% mean error). For probe-2, transfer learning significantly improved absorber concentration (0.38 vs. 1.67 µM, p=0.0005) and µ s,665 ' (0.71 vs. 1.8 cm -1 , p=0.0005) recovery. A third probe also showed improved absorber (0.7 vs. 4.1 µM, p<0.0001) and µ s,665 ' (1.68 vs. 2.08 cm -1 , p=0.2) recovery.

Conclusions

A data-driven approach to optical property extraction can be used to rapidly calibrate new fiber-optic probes with varying SDS, with as few as three calibration spectra.

In situ detection of human glioma based on tissue optical properties using diffuse reflectance spectroscopy

Safely maximizing brain cancer removal without injuring adjacent healthy tissue is crucial for optimal treatment outcomes. However, it is challenging to distinguish cancer from noncancer intraoperatively. This study aimed to explore the feasibility of diffuse reflectance spectroscopy (DRS) as a label-free and real-time detection technology for discrimination between brain cancer and noncancer tissues. Fifty-five fresh cancer and noncancer specimens from 19 brain surgeries were measured with DRS, and the results were compared with co-registered clinical standard histopathology. Tissue optical properties were quantitatively obtained from the diffuse reflectance spectra and compared among different types of brain tissues. A machine learning-based classifier was trained to differentiate cancerous versus noncancerous tissues. Our method could achieve a sensitivity of 93% and specificity of 95% for discriminating high-grade glioma from normal white matter. Our results showed that DRS has the potential to be used for label-free, real-time in vivo cancer detection during brain surgery.

Measuring and imaging of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy

Significance

Evaluation of biological chromophore levels is useful for detection of various skin diseases, including cancer, monitoring of health status and tissue metabolism, and assessment of clinical and physiological vascular functions. Clinically, it is useful to assess multiple different chromophores in vivo with a single technique or instrument.

Aim

To investigate the possibility of estimating the concentration of four chromophores, bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin from diffuse reflectance spectra in the visible region.

Approach

A new diffuse reflectance spectroscopic method based on the multiple regression analysis aided by Monte Carlo simulations for light transport was developed to quantify bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin. Three different experimental animal models were used to induce hyperbilirubinemia, hypoxemia, and melanogenesis in rats.

Results

The estimated bilirubin concentration increased after ligation of the bile duct and reached around 18    mg / dl at 50 h after the onset of ligation, which corresponds to the reference value of bilirubin measured by a commercially available transcutaneous bilirubin meter. The concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin decreased and increased, respectively, as the fraction of inspired oxygen decreased. Consequently, the tissue oxygen saturation dramatically decreased. The time course of melanin concentration after depilation of skin on the back of rats was indicative of the supply of melanosomes produced by melanocytes of hair follicles to the growing hair shaft.

Conclusions

The results of our study showed that the proposed method is capable of the in vivo evaluation of percutaneous bilirubin level, skin hemodynamics, and melanogenesis in rats, and that it has potential as a tool for the diagnosis and management of hyperbilirubinemia, hypoxemia, and pigmented skin lesions.

Importance sampling-accelerated simulation of full-spectrum backscattered diffuse reflectance

The Monte Carlo (MC) method is one of the most widely used numerical tools to model the light interaction with tissue. However, due to the low photon collection efficiency and the need to simulate the entire emission spectrum, it is computationally expensive to simulate the full-spectrum backscattered diffuse reflectance (F-BDR). Here, we propose an acceleration scheme based on importance sampling (IS). We derive the biasing sampling function tailored for simulating BDR based on the two-term scattering phase function (TT). The parameters of the TT function at different wavelengths are directly obtained by fitting the Mie scattering phase function. Subsequently, we incorporate the TT function and its corresponding biased function into the redefined IS process and realize the accelerated simulation of F-BDR. Phantom simulations based on the Fourier-domain optical coherence tomography (FD-OCT) are conducted to demonstrate the efficiency of the proposed method. Compared to the original simulator without IS, our proposed method achieves a 373× acceleration in simulating the F-BDR of the multi-layer phantom with a relative mean square error (rMSE) of less than 2%. Besides, by parallelly computing A-lines, our method enables the simulation of an entire B-scan in less than 0.4 hours. To our best knowledge, it is the first time that a volumetric OCT image of a complex phantom is simulated. We believe that the proposed acceleration method can be readily applied to fast simulations of various F-BDR-dependent applications. The source codes of this manuscript are also publicly available online.

Analysis of relative error in perturbation Monte Carlo simulations of radiative transport

Significance

Perturbation and differential Monte Carlo (pMC/dMC) methods, used in conjunction with nonlinear optimization methods, have been successfully applied to solve inverse problems in diffuse optics. Application of pMC to systems over a large range of optical properties requires optimal "placement" of baseline conventional Monte Carlo (cMC) simulations to minimize the pMC variance. The inability to predict the growth in pMC solution uncertainty with perturbation size limits the application of pMC, especially for multispectral datasets where the variation of optical properties can be substantial.

Aim

We aim to predict the variation of pMC variance with perturbation size without explicit computation of perturbed photon weights. Our proposed method can be used to determine the range of optical properties over which pMC predictions provide sufficient accuracy. This method can be used to specify the optical properties for the reference cMC simulations that pMC utilizes to provide accurate predictions over a desired optical property range.

Approach

We utilize a conventional error propagation methodology to calculate changes in pMC relative error for Monte Carlo simulations. We demonstrate this methodology for spatially resolved diffuse reflectance measurements with ±20% scattering perturbations. We examine the performance of our method for reference simulations spanning a broad range of optical properties relevant for diffuse optical imaging of biological tissues. Our predictions are computed using the variance, covariance, and skewness of the photon weight, path length, and collision distributions generated by the reference simulation.

Results

We find that our methodology performs best when used in conjunction with reference cMC simulations that utilize Russian Roulette (RR) method. Specifically, we demonstrate that for a proximal detector placed immediately adjacent to the source, we can estimate the pMC relative error within 5% of the true value for scattering perturbations in the range of [ - 15 % , + 20 % ] . For a distal detector placed at ∼ 3 transport mean free paths relative to the source, our method provides relative error estimates within 20% for scattering perturbations in the range of [ - 8 % , + 15 % ] . Moreover, reference simulations performed at lower ( μ s ' / μ a ) values showed better performance for both proximal and distal detectors.

Conclusions

These findings indicate that reference simulations utilizing continuous absorption weighting (CAW) with the Russian Roulette method and executed using optical properties with a low ( μ s ' / μ a ) ratio spanning the desired range of μ s values, are highly advantageous for the deployment of pMC to obtain radiative transport estimates over a wide range of optical properties.

Rapid Quantification of Tissue Perfusion Properties with a Two-Stage Look-Up Table

Tissue perfusion properties reveal crucial information pertinent to clinical diagnosis and treatment. Multispectral spatial frequency domain imaging (SFDI) is an emerging imaging technique that has been widely used to quantify tissue perfusion properties. However, slow processing speed limits its usefulness in real-time imaging applications. In this study, we present a two-stage look-up table (LUT) approach that accurately and rapidly quantifies optical (absorption and reduced scattering maps) and perfusion (total hemoglobin and oxygen saturation maps) properties using stage-1 and stage-2 LUTs, respectively, based on reflectance images at 660 and 850 nm. The two-stage LUT can be implemented on both CPU and GPU computing platforms. Quantifying tissue perfusion properties using the simulated diffuse reflectance images, we achieved a quantification speed of 266, 174, and 74 frames per second for three image sizes 512 × 512, 1024 × 1024, and 2048 × 2048 pixels, respectively. Quantification of tissue perfusion properties was highly accurate with only 3.5% and 2.5% error for total hemoglobin and oxygen saturation quantification, respectively. The two-stage LUT has the potential to be integrated with dual-sensor cameras to enable real-time quantification of tissue hemodynamics.

Picosecond-resolved fluorescence resonance energy transfer (FRET) in diffuse reflectance spectroscopy explores biologically relevant hidden molecular contacts in a non-invasive way

The potentiality of Förster resonance energy transfer (FRET) for studying molecular interactions inside biological tissues with improved spatial (Angström) and temporal (picosecond) resolution is well established. On the other hand, the efficacy of diffuse reflectance spectroscopy (DRS) that uses optical radiation in order to determine physiological parameters including haemoglobin, and oxygen saturation is well known. Here we have made an attempt to combine diffuse reflectance spectroscopy (DRS) with picosecond-resolved FRET in order to show improvement in the exploration of molecular contacts in biological tissue models. We define the technique as ultrafast time-resolved diffuse reflectance spectroscopy (UTRDRS). The illuminated photon of the fluorophore from the surface of the tissue-mimicking layers carries the hidden information of the molecular contact. In order to investigate the validation of the Kubelka-Munk (KM) formulism for the developed UTRDRS technique in tissue phantoms, we have studied the propagation of incandescent and picosecond-laser light through several layers of cellulose membranes. While picosecond-resolved FRET in the diffuse reflected light confirms the hidden nano-contact (4.6 nm) of two different dye layers (8-anilino-1-naphthalenesulfonic acid and Nile blue), high-resolution optical microscopy on the cross-section of the layers reveals the proximity and contacts of the layers with limited spatial resolution (∼300 nm). We have also investigated two biologically relevant molecules, namely carboxyfluorescein and haemoglobin, in tissue phantom layers in order to show the efficacy of the UTRDRS technique. Overall, our studies based on UTRDRS in tissue mimicking layers may have potential applications in non-invasive biomedical diagnosis for patients suffering from skin diseases.

Quantification of soft tissue parameters from spatially resolved diffuse reflectance finite element models

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique that helps in clinical diagnosis of various tissue microcirculation and skin pigmentation disorders based on collected backscattered light from multi-layered tissue. The extraction of the optical properties from the reflectance spectrum using analytical solutions is laborious. Model-based light tissue interaction studies help in quantifying the optical properties. This work presents the use of finite element models of light tissue interaction for this purpose. A bilayer model mimicking human skin was considered and the diffused reflectance spectra at multiple detector points were generated using finite element modelling for varying melanin concentration, epidermal thickness, blood volume fraction, oxygen saturation and scattering components. The reflectance value based on varying optical parameters from multiple detection points lead to the generation of a look-up table (LUT), which is further used for finding the tissue parameters that contribute to the spatially resolved reflectance values. The tissue parameters estimated after inverse modelling showed a high degree of agreement with the expected tissue parameters for a test dataset different from the training dataset.

Optical property recovery with spatially-resolved diffuse reflectance at short source-detector separations using a compact fiber-optic probe

We describe a compact fiber-optic probe (2 mm outside diameter) that utilizes spatially-resolved diffuse reflectance for tissue optical property recovery. Validation was performed in phantoms containing Intralipid 20% as scatterer, and methylene blue (MB), MnTPPS, and/or India ink as absorbers. Over a range of conditions, the reduced scattering coefficient was recovered with a root mean square error (RMSE) of 0.86-2.7 cm^-1 (average error = 3.8%). MB concentration was recovered with RMSE = 0.26-0.52 µM (average error = 15.0%), which did not vary with inclusion of MnTPPS (p=0.65). This system will be utilized to determine optical properties in human abscesses, in order to generate treatment plans for photodynamic therapy.

Rapid quantification of tissue perfusion properties with a two-stage look-up table: a simulation study.. [DOI: 10.1101/2021.11.04.467306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Tissue perfusion properties reveal crucial information pertinent to clinical diagnosis and treatment. Multispectral spatial frequency domain imaging (SFDI) is an emerging imaging technique that has been widely used to quantify tissue perfusion properties. However, slow processing speed limits its usefulness in real-time imaging applications. In this study, we present a two-stage look-up table (LUT) approach that accurately and rapidly quantifies optical (absorption and reduced scattering maps) and perfusion (total hemoglobin and oxygen saturation maps) properties using stage-1 and stage-2 LUTs, respectively, based on reflectance images at 660nm and 850nm. The two-stage LUT can be implemented on both CPU and GPU computing platforms. Quantifying tissue perfusion properties using the simulated diffuse reflectance images, we achieved a quantification speed of 266, 174, and 74 frames per second for three image sizes 512×512, 1024×1024, and 2048×2048 pixels, respectively. Quantification of tissue perfusion properties was highly accurate with only 3.5% and 2.5% error for total hemoglobin and oxygen saturation quantification, respectively. The two-stage LUT has the potential to be adopted in existing SFDI applications to enable real-time imaging capability of tissue hemodynamics.

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging

SIGNIFICANCE

Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications.

AIM

Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes.

APPROACH

A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue.

RESULTS

The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes.

CONCLUSION

These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

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.

Study of cervical precancerous lesions detection by spectroscopy and support vector machine

BACKGROUND AND OBJECTIVE

Diffuse reflectance spectroscopy (DRS) offers a fast, non-invasive, and low-cost alternative for cervical cancer diagnosis. We aim to develop a method for screening precancerous lesions based on DRS.

MATERIAL AND METHODS

Characteristic parameters of cervical tissue were extracted from spectra, including optical characteristic parameters such as absorption and scattering coefficients, and some slope and area parameters of the spectrum. Data were randomly divided into training (60%) and test (40%) sets. Of the 210 included patients, 166 were healthy, 22 had erosion of the cervix, and 31 had cervical intraepithelial neoplasia (CIN). The support vector machine (SVM) algorithm was used to classify normal and abnormal cervical tissue based on 11 characteristic parameters.

RESULTS

The SVM with linear kernel function, applied on the training data, could distinguish tissue with lesions from healthy tissue with an accuracy of 1.00. When the classifiers were applied to the test set, erosion of cervix and CIN could be discriminated from healthy tissue with an accuracy of 0.95 (±0.03).

CONCLUSIONS

This research shows that the diagnostic algorithm can be valuable for non-invasive diagnosis of cervical cancer. This is a significant step toward the development of a tool for tissue assessment of cervical cancer.

Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response

SIGNIFICANCE

Many studies in colorectal cancer (CRC) use murine ectopic tumor models to determine response to treatment. However, these models do not replicate the tumor microenvironment of CRC. Physiological information of treatment response derived via diffuse reflectance spectroscopy (DRS) from murine primary CRC tumors provide a better understanding for the development of new drugs and dosing strategies in CRC.

AIM

Tumor response to chemotherapy in a primary CRC model was quantified via DRS to extract total hemoglobin content (tHb), oxygen saturation (StO2), oxyhemoglobin, and deoxyhemoglobin in tissue.

APPROACH

A multimodal DRS and imaging probe (0.78 mm outside diameter) was designed and validated to acquire diffuse spectra longitudinally-via endoscopic guidance-in developing colon tumors under 5-fluoruracil (5-FU) maximum-tolerated (MTD) and metronomic regimens. A filtering algorithm was developed to compensate for positional uncertainty in DRS measurements Results: A maximum increase in StO2 was observed in both MTD and metronomic chemotherapy-treated murine primary CRC tumors at week 4 of neoadjuvant chemotherapy, with 21  ±  6  %   and 17  ±  6  %   fold changes, respectively. No significant changes were observed in tHb.

CONCLUSION

Our study demonstrates the feasibility of DRS to quantify response to treatment in primary CRC models.

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low-cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.

Methods of extraction of optical properties from diffuse reflectance measurements of ex-vivo human colon tissue using thin film silicon photodetector arrays

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a promising technique for characterization of colon tissue. Herein, two methods for extracting the reduced scattering and absorption coefficients ( μ s ' ( λ ) and μ a ( λ ) ) from SRDRS data using lookup tables of simulated diffuse reflectance are reported. Experimental measurements of liquid tissue phantoms performed with a custom multi-pixel silicon SRDRS sensor spanning the 450 - 750 nm wavelength range were used to evaluate the extraction methods, demonstrating that the combined use of spatial and spectral data reduces extraction error compared to use of spectral data alone. Additionally, SRDRS measurements of normal and tumor ex-vivo human colon tissue are presented along with μ s ' ( λ ) and μ a ( λ ) extracted from these measurements.

Quantitative in vivo detection of adipose tissue browning using diffuse reflectance spectroscopy in near-infrared II window

White adipose tissue (WAT) and brown adipose tissue (BAT) biologically function in an opposite way in energy metabolism. BAT induces energy consumption by heat production while WAT mainly stores energy in the form of triglycerides. Recent progress in the conversion of WAT cells to "beige" or "brown-like" adipocytes in animals, having functional similarity to BAT, spurred a great interest in developing the next-generation therapeutics in the field of metabolic disorders. Though magnetic resonance imaging and positron emission tomography could detect classical BAT and WAT in animals and humans, it is of a great challenge in detecting the "browning" process in vivo. Here, to the best of our knowledge, for the first time, we present a simple, cost-effective, label-free fiber optic-based diffuse reflectance spectroscopy measurement in the near infrared II window (~1050-1400 nm) for the quantitative detection of browning in a mouse model in vivo. We could successfully quantify the browning of WAT in a mouse model by estimating the lipid fraction, which serves as an endogenous marker. Lipid fraction exhibited a gradual decrease from WAT to BAT with beige exhibiting an intermediate value. in vivo browning process was also confirmed with standard molecular and biochemical assays.

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.

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.

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.

RGB camera-based imaging of cerebral tissue oxygen saturation, hemoglobin concentration, and hemodynamic spontaneous low-frequency oscillations in rat brain following induction of cortical spreading depression

To evaluate cerebral hemodynamics and spontaneous low-frequency oscillations (SLFOs) of cerebral blood flow in rat brain, we investigated an imaging method using a digital RGB camera. In this method, the RGB values were converted into tristimulus values in the CIE (Commission Internationale de l'Eclairage) XYZ color space, which is compatible with the common RGB working spaces. Monte Carlo simulation for light transport in tissue was then used to specify the relationship among the tristimulus XYZ values and the concentrations of oxygenated hemoglobin (CHbO), deoxygenated hemoglobin (CHbR), and total hemoglobin (CHbT) and cerebral tissue oxygen saturation (StO2). Applying the fast Fourier transform to each pixel of the sequential images of CHbT along the timeline, SLFOs of cerebral blood volume were visualized as a spatial map of power spectral density (PSD) at specific frequencies related to vasomotion. To confirm the feasibility of this method, we performed in vivo experiments using exposed rat brain during a cortical spreading depression (CSD) evoked by topical application of KCl. Cerebral hemodynamic responses to CSD such as initial hypoperfusion, profound hyperemia, and post-CSD oligemia and hypoxemia were successfully visualized with this method. At the transition to the hyperemia phase from hypoperfusion, CHbO and StO2 were significantly increased, which implied vasodilatation in arterioles and increased cerebral blood volume in response to CSD. In the wake of the hyperemic phase, CHbO and CHbT were significantly reduced to 25 ± 12% and 3.5 ± 1% of baseline, respectively, suggesting long-lasting vasoconstriction after CSD. In this persistent oligemia, StO2 significantly dropped to at most 23 ± 12% of the level before CSD, indicating long-lasting hypoxemia. The PSD value of SLFOs in CHbT for arteriole regions during CSD was significantly reduced to 28 ± 20% of baseline with respect to the pre-CSD level, which was correlated with the reduction in StO2. The results showed the possibility of RGB camera-based diffuse reflectance spectroscopy imaging for evaluating cerebral hemodynamics and SLFOs under normal and pathologic conditions.

In Vivo Evaluation of Cerebral Hemodynamics and Tissue Morphology in Rats during Changing Fraction of Inspired Oxygen Based on Spectrocolorimetric Imaging Technique

During surgical treatment for cerebrovascular diseases, cortical hemodynamics are often controlled by bypass graft surgery, temporary occlusion of arteries, and surgical removal of veins. Since the brain is vulnerable to hypoxemia and ischemia, interruption of cerebral blood flow reduces the oxygen supply to tissues and induces irreversible damage to cells and tissues. Monitoring of cerebral hemodynamics and alteration of cellular structure during neurosurgery is thus crucial. Sequential recordings of red-green-blue (RGB) images of in vivo exposed rat brains were made during hyperoxia, normoxia, hypoxia, and anoxia. Monte Carlo simulation of light transport in brain tissue was used to specify relationships among RGB-values and oxygenated hemoglobin concentration (C_HbO), deoxygenated hemoglobin concentration (C_HbR), total hemoglobin concentration (C_HbT), hemoglobin oxygen saturation (StO₂), and scattering power b. Temporal courses of C_HbO, C_HbR, C_HbT, and StO₂ indicated physiological responses to reduced oxygen delivery to cerebral tissue. A rapid decrease in light scattering power b was observed after respiratory arrest, similar to the negative deflection of the extracellular direct current (DC) potential in so-called anoxic depolarization. These results suggest the potential of this method for evaluating pathophysiological conditions and loss of tissue viability.

Compact dual-mode diffuse optical system for blood perfusion monitoring in a porcine model of microvascular tissue flaps

In reconstructive surgery, the ability to detect blood flow interruptions to grafted tissue represents a critical step in preventing postsurgical complications. We have developed and pilot tested a compact, fiber-based device that combines two complimentary modalities-diffuse correlation spectroscopy (DCS) and diffuse reflectance spectroscopy-to quantitatively monitor blood perfusion. We present a proof-of-concept study on an in vivo porcine model (n=8). With a controllable arterial blood flow supply, occlusion studies (n=4) were performed on surgically isolated free flaps while the device simultaneously monitored blood flow through the supplying artery as well as flap perfusion from three orientations: the distal side of the flap and two transdermal channels. Further studies featuring long-term monitoring, arterial failure simulations, and venous failure simulations were performed on flaps that had undergone an anastomosis procedure (n=4). Additionally, benchtop verification of the DCS system was performed on liquid flow phantoms. Data revealed relationships between diffuse optical measures and state of occlusion as well as the ability to detect arterial and venous compromise. The compact construction of the device, along with its noninvasive and quantitative nature, would make this technology suitable for clinical translation.

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.

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.

Flexible silicon sensors for diffuse reflectance spectroscopy of tissue

Diffuse reflectance spectroscopy (DRS) is being used in exploratory clinical applications such as cancer margin assessment on excised tissue. However, when interrogating nonplanar tissue anomalies can arise from non-uniform pressure. Herein is reported the design, fabrication, and test of flexible, thin film silicon photodetectors (PDs) bonded to a flexible substrate designed for use in conformal DRS. The PDs have dark currents and responsivities comparable to conventional Si PDs, and were characterized while flat and while flexed at multiple radii of curvature using liquid phantoms mimicking adipose and malignant breast tissue. The DRS and nearest neighbor crosstalk results were compared with Monte Carlo simulations, showing good agreement between simulation and experiment.

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.

Novel diffuse optics system for continuous tissue viability monitoring - extended recovery in vivo testing in a porcine flap model

In reconstructive surgery, tissue perfusion/vessel patency is critical to the success of microvascular free tissue flaps. Early detection of flap failure secondary to compromise of vascular perfusion would significantly increase the chances of flap salvage. We have developed a compact, clinically-compatible monitoring system to enable automated, minimally-invasive, continuous, and quantitative assessment of flap viability/perfusion. We tested the system's continuous monitoring capability during extended non-recovery surgery using an in vivo porcine free flap model. Initial results indicated that the system could assess flap viability/perfusion in a quantitative and continuous manner. With proven performance, the compact form constructed with cost-effective components would make this system suitable for clinical translation.

In vivo preclinical verification of a multimodal diffuse reflectance and correlation spectroscopy system for sensing tissue perfusion

In reconstructive surgery, impeded blood flow in microvascular free flaps due to a compromise in arterial or venous patency secondary to blood clots or vessel spasms can rapidly result in flap failures. Thus, the ability to detect changes in microvascular free flaps is critical. In this paper, we report progress on in vivo pre-clinical testing of a compact, multimodal, fiber-based diffuse correlation and reflectance spectroscopy system designed to quantitatively monitor tissue perfusion in a porcine model's surgically-grafted free flap. We also describe the device's sensitivity to incremental blood flow changes and discuss the prospects for continuous perfusion monitoring in future clinical translational studies.

Design verification of a compact system for detecting tissue perfusion using bimodal diffuse optical technologies

It is essential to monitor tissue perfusion during and after reconstructive surgery, as restricted blood flow can result in graft failures. Current clinical procedures are insufficient to monitor tissue perfusion, as they are intermittent and often subjective. To address this unmet clinical need, a compact, low-cost, multimodal diffuse correlation spectroscopy and diffuse reflectance spectroscopy system was developed. We verified system performance via tissue phantoms and experimental protocols for rigorous bench testing. Quantitative data analysis methods were employed and tested to enable the extraction of tissue perfusion parameters. This design verification study assures data integrity in future in vivo studies.

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%.

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.

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.

Diffuse photon density wave measurements and Monte Carlo simulations

Diffuse photon density wave (DPDW) methodology is widely used in a number of biomedical applications. Here, we present results of Monte Carlo simulations that employ an effective numerical procedure based upon a description of radiative transfer in terms of the Bethe–Salpeter equation. A multifrequency noncontact DPDW system was used to measure aqueous solutions of intralipid at a wide range of source–detector separation distances, at which the diffusion approximation of the radiative transfer equation is generally considered to be invalid. We find that the signal–noise ratio is larger for the considered algorithm in comparison with the conventional Monte Carlo approach. Experimental data are compared to the Monte Carlo simulations using several values of scattering anisotropy and to the diffusion approximation. Both the Monte Carlo simulations and diffusion approximation were in very good agreement with the experimental data for a wide range of source–detector separations. In addition, measurements with different wavelengths were performed to estimate the size and scattering anisotropy of scatterers.

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

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

Multi-Wavelength Photoacoustic Visualization of High Intensity Focused Ultrasound Lesions

High intensity focused ultrasound (HIFU) thermal therapies are limited by deficiencies in existing image-guidance techniques. Previous studies using single-wavelength photoacoustic (PA) imaging have demonstrated that HIFU lesions generate contrast with respect to native tissues but have not sufficiently assessed lesion extent. The purpose of this study is to demonstrate feasibility of characterization of in vitro HIFU ablation lesion dimensions using 3D multi-wavelength PA imaging. Fresh porcine cardiac and liver tissue samples were embedded in agar phantoms and ablated using a 2.5 MHz small-animal HIFU system. Both 2D and 3D multi-wavelength photoacoustic-ultrasonic (PAUS) scans were performed in the near-infrared (NIR) range to characterize the change in the absorption spectrum of tissues following ablation and were compared to stained gross pathology to assess treatment margins and lesion extent. Comprehensive 2D multi-wavelength PA imaging yielded a spectrum in ablated tissue that did not display the characteristic local maximum in the optical absorption spectrum of deoxy-hemoglobin (Hb) near 760 nm. Two-dimensional tissue characterization map (TCM) images reconstructed from 3D TCM volumes reliably characterized lesion area and showed >70% area agreement with stained gross pathology. In addition, tissue samples were heated via water bath and concurrently interrogated with 2D PAUS imaging. PA signal exhibited an initial amplitude increase across all wavelengths, corresponding to an initial temperature increase, before then exhibiting a spectral change. This study suggests that multi-wavelength PA imaging has potential to obtain accurate characterization of HIFU lesion extent and may be better suited to guide HIFU ablation therapies during clinical treatments than single-wavelength methods.

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.

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.

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

A two-layer Monte Carlo lookup table-based inverse model is validated with two-layered phantoms across physiologically relevant optical property ranges. Reflectance data for source-detector separations of 370 μm and 740 μm were collected from these two-layered phantoms and top layer thickness, reduced scattering coefficient and the top and bottom layer absorption coefficients were extracted using the inverse model and compared to the known values. The results of the phantom verification show that this method is able to accurately extract top layer thickness and scattering when the top layer thickness ranges from 0 to 550 μm. In this range, top layer thicknesses were measured with an average error of 10% and the reduced scattering coefficient was measured with an average error of 15%. The accuracy of top and bottom layer absorption coefficient measurements was found to be highly dependent on top layer thickness, which agrees with physical expectation; however, within appropriate thickness ranges, the error for absorption properties varies from 12-25%.

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.