Validation of an Inverse Fitting Method of Diffuse Reflectance Spectroscopy to Quantify Multi-Layered Skin Optical Properties

Noninvasive Detection of the Skin Structure and Inversed Retrieval of Chromophore Information Based on Diffuse Reflectance Spectroscopy

The detection of skin's structure lays the foundation for personalized laser surgery of vascular skin disease, which can be noninvasively achieved by diffuse reflectance spectroscopy (DRS). A two-step inverse Monte Carlo radiation method based on DRS under two source-detector separations was proposed to quantify the skin structure, including chromophore concentration (melanin f m and hemoglobin f b), epidermal thickness t epi, average vessel diameter D ves, depth d pws and thickness t pws of the vascular layer for diseased skin. The method fitted the simulated DRS to the measured DRS iteratively, differences between which were described by a specific objective function to amplify blood absorption at 500-600 nm, and D ves, d pws, and t pws were estimated based on f m, f b, and t pws fitted in the first step. The results showed that the two-step method dramatically improve the inversion accuracy with mean errors of f m, f b, t pws, and d pws less than 5%.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Absorption and reduced scattering coefficients in epidermis and dermis from a Swedish cohort study

Significance

Knowledge of optical properties is important to accurately model light propagation in tissue, but in vivo reference data are sparse.

Aim

The aim of our study was to present in vivo skin optical properties from a large Swedish cohort including 3809 subjects using a three-layered skin model and spatially resolved diffuse reflectance spectroscopy (Periflux PF6000 EPOS).

Approach

Diffuse reflectance spectra (475 to 850 nm) at 0.4 and 1.2 mm source-detector separations were analyzed using an inverse Monte Carlo method. The model had one epidermis layer with variable thicknesses and melanin-related absorptions and two dermis layers with varying hemoglobin concentrations and equal oxygen saturations. The reduced scattering coefficient was equal across all layers.

Results

Median absorption coefficients (mm - 1 ) in the upper dermis ranged from 0.094 at 475 nm to 0.0048 at 850 nm and similarly in the lower dermis from 0.059 to 0.0035. The reduced scattering coefficient (mm - 1 ) ranged from 3.22 to 1.20, and the sampling depth (mm) ranged from 0.23 to 0.38 (0.4 mm separation) and from 0.49 to 0.68 (1.2 mm separation). There were differences in optical properties across sex, age groups, and BMI categories.

Conclusions

Reference material for skin optical properties is presented.

Robustness of diffuse reflectance spectra analysis by inverse adding doubling algorithm

Analysing diffuse reflectance spectra to extract properties of biological tissue requires modelling of light transport within the tissue, considering its absorption, scattering, and geometrical properties. Due to the layered skin structure, skin tissue models are often divided into multiple layers with their associated optical properties. Typically, in the analysis, some model parameters defining these properties are fixed to values reported in the literature to speed up the fitting process and improve its performance. In the absence of consensus, various studies use different approaches in fixing the model parameters. This study aims to assess the effect of fixing various model parameters in the skin spectra fitting process on the accuracy and robustness of a GPU-accelerated two-layer inverse adding-doubling (IAD) algorithm. Specifically, the performance of the IAD method is determined for noiseless simulated skin spectra, simulated spectra with different levels of noise applied, and in-vivo measured reflectance spectra from hyperspectral images of human hands recorded before, during, and after the arterial occlusion. Our results suggest that fixing multiple parameters to a priori known values generally improves the robustness and accuracy of the IAD algorithm for simulated spectra. However, for in-vivo measured spectra, these values are unknown in advance and fixing optical parameters to incorrect values significantly deteriorates the overall performance. Therefore, we propose a method to improve the fitting performance by pre-estimating model parameters. Our findings could be considered in all future research involving the analysis of diffuse reflectance spectra to extract optical properties of skin tissue.

Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.