Doppler OCT clutter rejection using variance minimization and offset extrapolation.

Area of Science:

• Biomedical Optics
• Medical Imaging
• Fluid Dynamics

Background:

• Doppler optical coherence tomography (OCT) enables high-resolution mapping of flow velocities.
• Accurate velocity determination relies on precise identification of the signal's center of rotation.
• Current methods, like high-pass filtering, introduce significant bias, particularly at low velocities or high noise levels.

Purpose of the Study:

• To demonstrate the bias inherent in the high-pass filtering method for determining the center of rotation in Doppler OCT.
• To propose and evaluate two novel methods for Doppler OCT clutter rejection to mitigate this bias.
• To enhance the accuracy of Doppler OCT measurements, especially for challenging low-velocity and high-noise scenarios.

Main Methods:

• Demonstrated bias in the standard high-pass filtering technique for center of rotation determination.
• Developed and tested two novel methods: variance minimization and offset extrapolation for clutter rejection.
• Evaluated the proposed methods against high-pass filtering using Kasai, autocorrelation fitting, and maximum likelihood estimation algorithms.

Main Results:

• The proposed variance minimization and offset extrapolation methods significantly reduce bias in center of rotation estimation.
• Improved accuracy in resultant velocity measurements by up to 60 percentage points compared to high-pass filtering.
• Reduced flow conservation error by 30% in *in vivo* rodent brain cortex cerebral blood flow imaging.

Conclusions:

• Novel variance minimization and offset extrapolation methods offer superior Doppler OCT clutter rejection.
• These advanced techniques enhance the accuracy of flow velocity measurements, overcoming limitations of traditional methods.
• The findings are particularly impactful for *in vivo* applications requiring precise low-flow or high-noise measurements, such as cerebral blood flow imaging.