Hybrid method to estimate two-layered superficial tissue optical properties from simulated data of diffuse reflectance spectroscopy

Diffuse reflectance and machine learning techniques to differentiate colorectal cancer ex vivo

In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at 410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations, those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal cancer.

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.