Significance Diffuse reflectance spectroscopy and imaging are powerful noninvasive tools for quantifying tissue biomarkers such as hemoglobin, melanin, and water. A theoretical model of light propagation is required to interpret these reflectance measurements and translate them into quantitative biomarker concentrations. Although Monte Carlo (MC) simulations offer the gold-standard in accuracy, their high computational burden renders them impractical for real-time iterative inverse solving. Conversely, standard analytical approximations can be efficient but often oversimplify tissue as a semi-infinite homogeneous medium, leading to significant errors when analyzing layered structures. Aim We aim to develop a generalized, computationally efficient forward model capable of rapidly predicting light transport in complex, multilayered tissue structures. Approach We present a generalized derivation and open-source Python implementation of the P1 diffusion approximation for predicting diffuse reflectance from tissue with an arbitrary number of layers. This framework utilizes a vectorized banded matrix approach to solve the fluence rate equations efficiently. The model was validated against 400 randomized MC simulations encompassing architectures ranging from one to eight layers. Results Diffuse reflectance spectra predicted by the generalized multilayer diffusion theory model show strong agreement with MC simulations in the diffusion regime (mu a <
Significance Traumatic and chronic wound healing is a complex process that often requires careful observation by healthcare professionals. Noninvasive optical technologies such as hyperspectral reflectance imaging and tissue autofluorescence imaging detect changes in optical properties related to physiological parameters such as tissue perfusion. Macroscopic hyperspectral systems with a large field of view to visualize tissue structure and perfusion are well suited for wound care. Aim Here, a custom-built benchtop hyperspectral and autofluorescence imaging system (HySAF) is optimized to measure tissue optical properties. It operates using a custom Python script to allow precise control over acquisition parameters. Approach HySAF was built using off-the-shelf components, and Python code was written to control primary devices. HySAF's performance was evaluated by imaging oxygenated and deoxygenated blood samples, scattering induced by nanoparticles, scorpion autofluorescence, and rat wounds. Results HySAF has a large field of view (FOV) with 176.7 mu m pixel resolution and 10 nm spectral resolution from 420 to 730 nm. Changes in absorbance were observed during blood deoxygenation, nanoparticle presence in cells, and healing wound tissue in rats, while autofluorescence distinguished anatomical parts of the scorpion. Conclusion HySAF is capable of detecting changes in sample fluorescence and absorbance across visible light with large FOV and could be implemented in clinical applications such as monitoring and assessing wound healing.
SignificanceAccurate characterization of light propagation in biological tissues is essential for applications such as phototherapy, photodermatology, photobiomodulation, and photodiagnosis. Because many organs are stratified, Monte Carlo simulations require sets of optical coefficients–absorption (μa), reduced scattering (μs′), anisotropy factor (g), and refractive index (n)–in addition to tissue geometry, to model light transport in each tissue layer. However, published values of these coefficients often differ significantly for a given tissue and wavelength.AimThis work aims to address the challenge of selecting representative optical coefficients for Monte Carlo simulations by compiling and critically evaluating reported optical properties of human skin (Phototype III) and muscle as an illustrative example.ApproachPublished optical coefficients reported for selected visible and near-infrared wavelengths were analyzed and compared. A statistical framework was applied to derive representative sets of optical coefficients for different skin layers from heterogeneous literature data.ResultsThe analysis revealed substantial variability among reported optical coefficients. Representative sets of absorption, reduced scattering, anisotropy, and refractive-index values were derived for distinct skin layers and muscle at selected wavelengths.ConclusionsThe proposed framework provides a practical approach for deriving representative optical coefficients from heterogeneous literature data and highlights the importance of standardized, multilayered tissue models for accurate and reproducible light-dosimetry simulations.
SignificanceAcute mesenteric ischemia (AMI) is associated with high mortality, and accurate assessment of intestinal viability and the extent of ischemic injury remains challenging. Microvascular alterations may precede overt mucosal structural damage but are difficult to monitor dynamically.AimWe aim to investigate the ability of OCT/OCTA to dynamically quantify intestinal mucosal microvascular alterations during AMI and to determine their temporal and vessel-diameter-dependent associations with histological injury.ApproachAMI was induced in mice by mesenteric artery ligation. In a longitudinal cohort (n=8), OCT was performed at baseline and 0.5, 1, 2, and 4 h after ischemia to quantify vessel length and vessel perfused density (VPD) in three diameter categories (<20 μm, 20 to 50 μm, and >50 μm). In a separate cross-sectional cohort (n=40), mice were randomly assigned to sham or ischemia groups (n=8 each). OCT was also used to detect the vascular parameters of intestinal mucosal microcirculation. Histological injury was assessed by villus height, crypt depth, and Chiu’s score. Correlations between microvascular and histological parameters were analyzed. Receiver operating characteristic curve was used to assess vascular parameters for discriminating severe intestinal injury.ResultsIschemia induced time- and diameter-dependent microvascular impairment. With the prolongation of ischemia, both vessel length and VPD decreased significantly across all vessel diameter categories (P<0.05). The length and VPD of vessels >50 μm decreased significantly faster than those of vessels <20 μm (P<0.05). Notably, reductions in the vascular parameters of 20 to 50 μm and >50 μm vessels were observed as early as 0.5 h after ischemia and preceded significant elevation of Chiu’s score. Villus height and crypt depth progressively decreased, whereas Chiu’s score increased with prolonged ischemia. OCT-derived vascular parameters across all diameter categories were positively correlated with villus height and crypt depth and negatively correlated with Chiu’s score (all P<0.05). In addition, OCT-derived vascular parameters had significant diagnostic performance for severe mucosal injury (Chiu′s score≥3).ConclusionsOCT enables real-time and quantitative assessment of intestinal mucosal microvascular alterations in AMI. Diameter-dependent microvascular impairment precedes overt histological injury and is associated with the severity of mucosal damage. These findings support the potential use of OCTA for intraoperative assessment of intestinal viability and provide a basis for future identification of ischemic transition zones and optimization of bowel resection boundaries.
SignificanceWe identified key spatial frequency domain imaging (SFDI) features for assessing burn wound severity. We built upon our previous two-step regression framework, using SFDI-calibrated reflectance (Rd) maps to predict collagen denaturation depth (CDD) and healing outcome of burn wounds. We used feature selection methods to identify key parameters from a large data set (40 features, including eight wavelengths, each at five spatial frequencies).AimWe aimed to identify a reduced set of key SFDI features (i.e., pairs of spatial frequencies and wavelengths) for assessing burn wound severity.ApproachWe utilized a graded-severity porcine burn model (n=4) with eight burn contact durations (5 to 40 s). Multiple linear regression (MLR) was employed to predict CDD from Rd obtained within 24 h post-burn. Subsequently, logistic regression (LogR) was used to predict day-28 healing outcomes based on the predicted CDD. In this study, we fielded three feature selection algorithms (i.e., standardized ordinary least squares, least absolute shrinkage and selection operator, and minimum redundancy maximum relevance) to identify key SFDI features in the MLR model. The resulting prediction then dictates the LogR outputs. We assessed the models for accuracy and fit performance against those of the 40-feature original model.ResultsA subset of 13 LASSO features matched the predictive performance of the 40-feature baseline (RMSE 98 versus 105 μm; within fold-to-fold noise), and a minimum set of eight LASSO features (minimum features with acceptable errors) closely approximated it. Across all three methods, the consistently top-ranked features were planar (0 mm−1) reflectance at 526, 591, and 621 nm.ConclusionsUsing feature selection algorithms, we identified preliminary candidate features that may be suitable for developing a future SFDI system to assess burn wound severity with reduced complexity. This has the potential to enable a more compact design compared with the current research-grade SFDI system.
