Algorithm Comparison for Oxygen Saturation Estimation in Whole Blood Using Diffuse Reflectance Hyperspectral Imaging

Hyperspectral imaging (HSI) has been used in vivo to estimate the oxygenation level (SO₂) of whole blood inside blood vessels. However, little literature is devoted to validating the estimation. The goal of this research is to compare the performance of three commonly used methods: the Kubelka-Munk algorithm, optical density algorithm, and inverse Monte Carlo method, for the task of estimating SO₂ from diffuse reflectance spectra. Validation was performed using two data types: Monte Carlo simulated data and ex vivo sheep blood that was deoxygenated using sodium dithionite (Na₂S₂O₄). The SO₂ of sheep blood was confirmed using a blood gas analyzer. We measured the diffuse reflectance using two types of HSI cameras: a push-broom and a snapshot HSI camera system. Our results showed that visible wavelengths (400 - 600 nm) are preferred for micro-vessels with diameters less than 100 microns, and near-infrared wavelengths (600 - 900 nm) are preferred for larger vessels. We determined experimentally that sodium dithionite consistently deoxygenated whole blood by 35% for every 1 mg/mL of sodium dithionite to hemoglobin. It was found that the inverse Monte Carlo method performed well in estimating SO₂, with an average estimation error of 5.53% and 6.97% for ex vivo sheep blood and Monte Carlo spectra, respectively. Our findings provide a basis for determining future methods for in vivo SO₂ estimations, including surgical HSI applications that rely on accurate perfusion assessment.