Quantification of the optical properties of two-layered turbid media by simultaneously analyzing the spectral and spatial information of steady-state diffuse reflectance spectroscopy

Abstract

We applied hyperspectral imaging to measure spatially-resolved diffuse reflectance spectra in the visible range and an iterative inversion method based on forward Monte Carlo modeling to quantify optical properties of two-layered tissue models. We validated the inversion method using spectra experimentally measured from liquid tissue mimicking phantoms with known optical properties. Results of fitting simulated data showed that simultaneously considering the spatial and spectral information in the inversion process improves the accuracies of estimating the optical properties and the top layer thickness in comparison to methods fitting reflectance spectra measured with a single source-detector separation or fitting spatially-resolved reflectance at a single wavelength. Further development of the method could improve noninvasive assessment of physiological status and pathological conditions of stratified squamous epithelium and superficial stroma.

Keywords: (110.4234) Multispectral and hyperspectral imaging; (170.3660) Light propagation in tissues; (170.6510) Spectroscopy, tissue diagnostics; (170.7050) Turbid media; (300.6550) Spectroscopy, visible.

Fig. 1

Schematic diagram of the experimental setup. One source fiber and an imaging fiber bundle formed a simple fiber package whose distal end was in contact with the sample. The proximal end of the fiber bundle was positioned at the focal plane of the objective for the acquisition of hyperspectral images of the sample via the fiber bundle.

Fig. 2

Image of the proximal end of the fiber bundle captured with the microscope objective. The center of the fiber bundle and the pixel with the highest intensity determine the line passing through the center of the source fiber. The 8 green circles indicate the regions for measuring diffuse reflectance spectra.

Fig. 3

Spatially-resolved reflectance spectra of phantom 3 with SDSs from 0.4 mm to 1.8 mm and their corresponding fits.

Fig. 4

Extracted µ_a(λ) (red lines) and µ_s'(λ) (blue lines) of the phantom experiments. The solid lines represent the expected values based on phantom compositions and the dashed lines represent the spectra extracted from DRS data.

Fig. 5

Example of simulated diffuse reflectance spectra with noise and curve fitting results at multiple SDSs.