Quantitative multi-spectral oxygen saturation measurements independent of tissue optical properties

Imaging of tissue oxygenation is important in several applications associated with patient care. Optical sensing is commonly applied for assessing oxygen saturation but is often restricted to local measurements or else it requires spectral and spatial information at the expense of time. Many methods proposed so far require assumptions on the properties of measured tissue. In this study we investigated a computational method that uses only multispectral information and quantitatively computes tissue oxygen saturation independently of tissue optical properties. The method is based on linear transformations of measurements in three isosbestic points. We investigated the ideal isosbestic point combination out of six isosbestic points available for measurement in the visible and near-infrared region that enable accurate oxygen saturation computation. We demonstrate this method on controlled tissue mimicking phantoms having different optical properties and validated the measurements using a gas analyzer. A mean error of 2.9 ± 2.8% O2 Sat was achieved. Finally, we performed pilot studies in tissues in-vivo by measuring dynamic changes in fingers subjected to vascular occlusion, the vasculature of mouse ears and exposed mouse organs.