Maximum Likelihood Doppler Frequency Estimation Under Decorrelation Noise for Quantifying Flow in Optical Coherence Tomography

Recent hardware advances in optical coherence tomography (OCT) have led to ever higher A-scan rates. However, the estimation of blood flow axial velocities is limited by the presence and type of noise. Higher acquisition rates alone do not necessarily enable precise quantification of Doppler velocities, particularly if the estimator is suboptimal. In previous work, we have shown that the Kasai autocorrelation estimator is statistically suboptimal under conditions of additive white Gaussian noise. In addition, for practical OCT measurements of flow, decorrelation noise affects Doppler frequency estimation by broadening the signal spectrum. Here, we derive a general maximum likelihood estimator (MLE) for Doppler frequency estimation that takes into account additive white noise as well as signal decorrelation. We compare the decorrelation MLE with existing techniques using simulated and flow phantom data and find that it has better performance, achieving the Cramer-Rao lower bound. By making an approximation, we also provide an interpretation of this method in the Fourier domain. We anticipate that this estimator will be particularly suited for estimating blood flow in in vivo scenarios.