Doppler OCT clutter rejection using variance minimization and offset extrapolation.

Doppler optical coherence tomography (OCT) is widely used for high-resolution mapping of flow velocities and is based on analysis of temporal changes in the phase of an OCT signal (i.e., how fast the OCT signal rotates in the complex plane). Determination of the rate of phase change or rotation speed critically depends on the center of rotation. Here, we demonstrate the bias in high-pass filtering, the current widely used method to determine the center of rotation, and propose two advanced methods for Doppler OCT clutter rejection. The bias in the high-pass filtering method becomes increasingly significant with lower velocities or larger signal noise. Two novel methods based on variance minimization and offset extrapolation can potentially reduce this bias and thereby improve the accuracy of Doppler OCT measurements of flow velocities, even for low-velocity and/or high-noise signals. The two novel methods and the current standard method (high-pass filtering) have been tested in combination with several currently used velocity measurement algorithms: Kasai, autocorrelation function fitting, and maximum likelihood estimation. The newly proposed methods are shown to improve the accuracy in both the center of rotation and resultant velocity by up to 60 percentage points and reduce the flow conservation error by 30% when applied to cerebral blood flow imaging of the rodent brain cortex.