Development and validation of a reflectance-based approach for oxygen saturation estimation using high-speed hyperspectral imaging

During image-guided surgery and anastomosis procedures, it is important to monitor physiological status and assess tissue perfusion. Hyperspectral imaging (HSI) can be used to estimate superficial tissue oxygen saturation (sO₂), where native reflectance measurements are converted to absorbance for spectral unmixing and sO₂ estimation. However, keeping the reflectance data in its native form may improve accuracy. We developed a reflectance-based approach for sO₂ estimation with a high-speed HSI system, leveraging band-filtering and data processing techniques to improve performance. Our approach is validated with blood oxygenation and tissue-mimicking phantoms, utilizing a blood-gas analyzer (BGA) to obtain ground-truth measurements. The reflectance-based approach achieved a median error of 5.0% and 5.3% with the blood oxygenation and tissue-mimicking phantoms, respectively, effectively halving the error achieved with an absorbance-based approach (10.4%). Overall, we present a novel reflectance-based approach for sO₂ estimation that achieves superior performance compared to a conventional absorbance-based technique. Paired with high-speed HSI and robust BGA validation, this approach offers strong potential for clinical translation. Thus, the reflectance-based HSI sO₂ estimation approach can have many applications in image-guided surgery and interventional procedures.