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

Abstract

An iterative curve fitting method has been applied in both simulation [J. Biomed. Opt.17, 107003 (2012)JBOPFO1083-366810.1117/1.JBO.17.10.107003] and phantom [J. Biomed. Opt.19, 077002 (2014)JBOPFO1083-366810.1117/1.JBO.19.7.077002] studies to accurately extract optical properties and the top layer thickness of a two-layered superficial tissue model from diffuse reflectance spectroscopy (DRS) data. This paper describes a hybrid two-step parameter estimation procedure to address two main issues of the previous method, including (1) high computational intensity and (2) converging to local minima. The parameter estimation procedure contained a novel initial estimation step to obtain an initial guess, which was used by a subsequent iterative fitting step to optimize the parameter estimation. A lookup table was used in both steps to quickly obtain reflectance spectra and reduce computational intensity. On simulated DRS data, the proposed parameter estimation procedure achieved high estimation accuracy and a 95% reduction of computational time compared to previous studies. Furthermore, the proposed initial estimation step led to better convergence of the following fitting step. Strategies used in the proposed procedure could benefit both the modeling and experimental data processing of not only DRS but also related approaches such as near-infrared spectroscopy.

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

“…The use of Monte Carlo simulations to create Look-Up tables (LUT), and more recently, the use of ML algorithms to estimate the optical properties of tissues have been reported. [33][34][35][36] An example of the use of mathematical models to estimate the optical properties of tissues is described in Ref. 33, where the authors defined a formula and fitted it to empirical data to retrieve the µ a values to evaluate the goodness of the fit.…”

“…In Ref. 34, the authors used previously generated LUT to find an initial estimation of the optical properties. Using this initial estimation in a Monte Carlo simulation, they generated the diffuse reflectance spectrum to compare with measured spectra.…”

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

“…In the first part of procedure Y, only spectra in hemoglobin absorption bands of 410-440 nm and 530-580 nm are considered. Therefore, the sensitivity to hemoglobin absorption is expected to be increased compared to procedure Z where the full spectra are fitted without spectral specificity [21]. Table 2 summarizes errors in tissue geometric and optical parameters extracted from the 40 sets of simulated spectra using the six combinations of inverse fitting procedures.…”

“…In the first part of procedure Y, only spectra in hemoglobin absorption bands of 410-440 nm and 530-580 nm are considered. Therefore, the sensitivity to hemoglobin absorption is expected to be increased compared to procedure Z where the full spectra are fitted without spectral specificity [21].…”

“…For modeling the skin which consists of a thin layer of epidermis on top of the dermis, two or more layered tissue models are more appropriate than semi-infinite homogeneous models [10][11][12][13][14][15][16][17][18][19][20][21]. To reduce the time for running repeated MC simulations when extracting tissue optical properties, many studies have proposed to construct empirical relations [10][11][12][13][14], look-up tables [15,16], or artificial neural networks [18][19][20]22] to quickly calculate the reflectance at tissue surface given tissue geometric and optical properties.…”