Quantified Speckle Variance Optical Coherence Tomography Algorithm for Monitoring Blood Flow Dynamics

Speckle variance optical coherence tomography (SV-OCT) is a non-invasive imaging technique that can map blood flow in tissues by capturing speckle pattern variations from moving red blood cells. A key challenge of SV-OCT is distinguishing variance caused by blood flow from non-motion signals like shot noise. Here we show that noise contributions due to non-motion signals can be approximated as characteristic of the OCT hardware system, independent of the sample under consideration. We present a method for measuring these characteristics and a "quantified speckle variance" algorithm to standardize SV measurements reflecting variance from motion signals. This allows quantification of intensity fluctuations above the noise floor and enhances contrast. The qSV algorithm, validated using a phantom flow system, when applied to OCT images of skin, identified deep blood vessels with over 30 times the contrast of standard SV methods. Additionally, the qSV algorithm can differentiate between normal and restricted blood flow, highlighting its sensitivity to dynamic speckle patterns. Our approach standardizes angiography data analysis and reporting, enhancing its value for longitudinal studies and clinical applications in vascular imaging.