Abstract
.Significance: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neural networks (ANN) determining the absorption coefficient one wavelength at a time.Aim: To present an ANN-based method that directly outputs the oxygen saturation and the hemoglobin concentration using the shape of the measured spectra as input.Approach: A probe-based DRS setup with dual source-detector separations in the visible wavelength range was used. ANNs were trained on spectra generated from a three-layer tissue model with oxygen saturation and hemoglobin concentration as target.Results: Modeled evaluation data with realistic measurement noise showed an absolute root-mean-square (RMS) deviation of 5.1% units for oxygen saturation estimation. The relative RMS deviation for hemoglobin concentration was 13%. This accuracy is at least twice as good as our previous nonlinear optimization method. On blood-intralipid phantoms, the RMS deviation from the oxygen saturation derived from partial oxygen pressure measurements was 5.3% and 1.6% in two separate measurement series. Results during brachial occlusion showed expected patterns.Conclusions: The presented method, directly assessing oxygen saturation and hemoglobin concentration, is fast, accurate, and robust to noise.
Highlights
In optical fiber-based diffuse reflectance spectroscopy (DRS), white light is illuminating tissue, and backscattered light is detected at single or multiple source–detector (s-d) separations
The calculation of tissue chromophores, which is most often the aim for DRS methods, is commonly done using inverse modeling based on diffusion theory[3,4,5] or Monte Carlo techniques,[6,7] where modeled DRS data are fitted to measured
We have previously developed a three-layer skin model for analyzing data acquired using DRS13 and DRS integrated with LDF,[7] using two s-d separations (0.4 and 1.2 mm)
Summary
In optical fiber-based diffuse reflectance spectroscopy (DRS), white light is illuminating tissue, and backscattered light is detected at single or multiple source–detector (s-d) separations. The s-d separations are chosen to allow for contrasting scattering (μs0), absorption (μa), and tissue geometrical effects. Tissue absorption of light in the visible wavelength range is due to major chromophores, such as hemoglobin, mainly oxyhemoglobin and deoxyhemoglobin. Epidermal melanin and carotenoids are present affecting foremost short wavelengths,[1] whereas for higher wavelengths water and lipid absorption is significant.[2] The calculation of tissue chromophores, which is most often the aim for DRS methods, is commonly done using inverse modeling based on diffusion theory[3,4,5] or Monte Carlo techniques,[6,7] where modeled DRS data are fitted to measured
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