A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Potential of Raman-Reflectance Combination in Quantifying Liver Steatosis and Fat Droplet Size: Evidence From Monte Carlo Simulations and Phantom Studies

This study explores a combined strategy of Raman and reflectance spectroscopy for quantifying liver fat content and fat droplet size, crucial in assessing donor livers. By using Monte Carlo simulations and experimental setups with oil-in-water phantoms, our findings indicate that Raman scattering can solely differentiate between varying fat contents. At the same time, reflectance intensity is influenced by both fat content and oil droplet size, with a more pronounced sensitivity to fat droplet size. This study demonstrates the efficacy of combined Raman and reflectance spectroscopy in assessing liver steatosis and fat droplet size, potentially aiding in assessing donor livers for transplantation.

Assessment of spatially offset Raman spectroscopy to detect differences in bone matrix quality

Since spatially offset Raman spectroscopy (SORS) can acquire biochemical measurements of tissue quality through light scattering materials, we investigated the feasibility of this technique to acquire Raman bands related to the fracture resistance of bone. Designed to maximize signals at different offsets, a SORS probe was used to acquire spectra from cadaveric bone with and without skin-like tissue phantoms attenuating the light. Autoclaving the lateral side of femur mid-shafts from 5 female and 5 male donors at 100 °C and again at 120 °C reduced the yield stress of cortical beams subjected to three-point bending. It did not affect the volumetric bone mineral density or porosity. Without tissue phantoms, autoclaving affected more Raman characteristics of the organic matrix when determined by peak intensity ratios, but fewer matrix properties depended on the three offsets (5 mm, 6 mm, and 7 mm) when determined by band area ratios. The cut-off in the thickness of the tissue phantom layers was ∼4 mm for most properties, irrespective of offset. Matching trends when spectra were acquired without phantom layers between bone and the probe, ν1PO43-/Amide III and ν1PO43-/(proline + OH-proline) were higher and lower in the non-treated bone than in the autoclaved bone, respectively, when the thickness of tissue phantom layers was 4 mm. The layers, however, caused a loss of sensitivity to autoclaving-related changes in ν3CO3/ν1PO43- and crystallinity. Without advanced post-processing of Raman spectra, SORS acquisition through turbid layers can detect changes in Raman properties of bone that accompany a loss in bone strength.

Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation

Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.

Model-based characterization platform of fiber optic extended-wavelength diffuse reflectance spectroscopy for identification of neurovascular bundles

SIGNIFICANCE

Fiber-optic extended-wavelength diffuse reflectance spectroscopy (EWDRS) using both visible/near-infrared and shortwave-infrared detectors enables improved detection of spectral absorbances arising from lipids, water, and collagen and has demonstrated promise in a variety of applications, including detection of nerves and neurovascular bundles (NVB). Development of future applications of EWDRS for nerve detection could benefit from the use of model-based analyses including Monte Carlo (MC) simulations and evaluation of agreement between model systems and empirical measurements.

AIM

The aim of this work is to characterize agreement between EWDRS measurements and simulations and inform future applications of model-based studies of nerve-detecting applications.

APPROACH

A model-based platform consisting of an ex vivo microsurgical nerve dissection model, unique two-layer optical phantoms, and MC model simulations of fiber-optic EWDRS spectroscopic measurements were used to characterize EWDRS and compare agreement across models. In addition, MC simulations of an EWDRS measurement scenario are performed to provide a representative example of future analyses.

RESULTS

EWDRS studies performed in the common chicken thigh femoral nerve microsurgical dissection model indicate similar spectral features for classification of NVB versus adjacent tissues as reported in porcine models and human subjects. A comparison of measurements from unique EWDRS issue mimicking optical phantoms and MC simulations indicates high agreement between the two in homogeneous and two-layer optical phantoms, as well as in dissected tissues. Finally, MC simulations of measurement over a simulated NVB indicate the potential of future applications for measurement of nerve plexus.

CONCLUSIONS

Characterization of agreement between fiber-optic EWDRS measurements and MC simulations demonstrates strong agreement across a variety of tissues and optical phantoms, offering promise for further use to guide the continued development of EWDRS for translational applications.

Development of a mobile phone camera-based transcutaneous bilirubinometer for low-resource settings

Newborns in high-income countries are routinely screened for neonatal jaundice using transcutaneous bilirubinometery (TcB). In low-and middle-income countries, TcB is not widely used due to a lack of availability; however, mobile-phone approaches for TcB could help expand screening opportunities. We developed a mobile phone-based approach for TcB and validated the method with a 37 patient multi-ethnic pilot study. We include a custom-designed snap-on adapter that is used to create a spatially resolved diffuse reflectance detection configuration with the illumination provided by the mobile-phone LED flash. Monte-Carlo models of reflectance from neonatal skin were used to guide the design of an adapter for filtered red-green-blue (RGB) mobile-phone camera reflectance measurements. We extracted measures of reflectance from multiple optimized spatial-offset regions-of-interest (ROIs) and a linear model was developed and cross-validated. This resulted in a correlation between total serum bilirubin and mobile-phone TcB estimated bilirubin with a R ²= 0.42 and Bland-Altman limits of agreement of +6.4 mg/dL to -7.0 mg/dL. These results indicate that a mobile phone with a modified adapter can be utilized to measure neonatal bilirubin values, thus creating a novel tool for neonatal jaundice screening in low-resource settings.