Quantitative assessment of flow velocity-estimation algorithms for optical Doppler tomography imaging

We present a quantitative comparison of three categories of velocity estimation algorithms, including centroid techniques (the adaptive centroid technique and the weighted centroid technique), the slidingwindow filtering technique, and correlation techniques (autocorrelation and cross correlation). We introduce, among these five algorithms, two new algorithms: weighted centroid and sliding-window filtering. Simulations and in vivo blood flow data are used to assess the velocity estimation accuracies of these algorithms. These comparisons demonstrate that the sliding-window filtering technique is superior to the other techniques in terms of velocity estimation accuracy and robustness to noise.