Proposal for a Skin Layer-Wise Decomposition Model of Spatially-Resolved Diffuse Reflectance Spectra Based on Maximum Depth Photon Distributions: A Numerical Study

Abstract

In the context of cutaneous carcinoma diagnosis based on in vivo optical biopsy, Diffuse Reflectance (DR) spectra, acquired using a Spatially Resolved (SR) sensor configuration, can be analyzed to distinguish healthy from pathological tissues. The present contribution aims at studying the depth distribution of SR-DR-detected photons in skin from the perspective of analyzing how these photons contribute to acquired spectra carrying local physiological and morphological information. Simulations based on modified Cuda Monte Carlo Modeling of Light transport were performed on a five-layer human skin optical model with epidermal thickness, phototype and dermal blood content as variable parameters using (i) wavelength-resolved scattering and absorption properties and (ii) the geometrical configuration of a multi-optical fiber probe implemented on an SR-DR spectroscopic device currently used in clinics. Through histograms of the maximum probed depth and their exploitation, we provide numerical evidence linking the characteristic penetration depth of the detected photons to their wavelengths and four source–sensor distances, which made it possible to propose a decomposition of the DR signals related to skin layer contributions.

Probing depth in diffuse reflectance spectroscopy of biotissues: a Monte Carlo study

“…To increase the efficiency of the MC modeling in a layered medium, axial symmetry of the problem was employed, and the fiber detectors were replaced by the ring detectors as in Colas et al [25] with proper normalization for the detector area. The light source was placed at the center of the sample surface.…”

“…The advantage of MC technique in the estimation of the measurement volume and the probing depth is tracking individual trajectories of photons that reach the detector for a particular SDS and contribute to DRS signal. Moreover, since MC approach is more flexible in considering a multilayer structure or real morphology-based geometry, it is usually a method of choice for the probing depth analysis in DRS [7,25].…”

“…MC approach is also widely used to simulate the signals acquired by DRS and to solve the inverse problem of extracting tissue optical properties from the measured spectra [25]. In the study [26], tissue scattering coefficients and chromophore content are extracted from spatially-resolved diffuse reflectance spectra basing on artificial neural network trained with a MC simulated DRS for variation of four-layered tissue model parameters.…”

“…The dependencies of optical densities on the depth and thickness were derived from the MC simulations of light transport in two-layered skin tissue model with the embedded bloodcontaining region within the dermis layer. In [25], to quantify light penetration in skin tissue, the histograms of the maximum depth for photons detected at several SDSs were analyzed using MC technique. Simulations were performed for varying the model parameters such as epidermal thickness, phototype (which defines melanin volume concentration), dermal blood concentration, as well as for skin models simulating cutaneous carcinoma.…”

“…In this study we perform the analysis of the spectral dependence of the probing depth in the wavelength range of 480-900 nm and for SDSs in the range of 1.75-7.00 mm using MC approach. In contrast to paper [25] where DRS probing of human skin has been conventionally implemented with smaller SDSs, we aim at analyzing configurations that allow to probe the entire skin thickness. Since strong spectral dispersion of biotissue optical properties results in different probing depth at different wavelength ranges, we evaluated the optimal wavelength range for reconstruction of StO 2 based on DRS spectra simulated for different tissue geometries and StO 2 levels.…”