Adaptive Color Doppler for Axial Velocity Imaging of Microvessel Networks

Directional filtering has been applied to distinguish between the ascending and descending flows in functional ultrasound imaging; however, it can lead to incorrect measurement of the flow speed and direction when using the directional filtering-based improved directional color Doppler ultrasound (iCD_US) velocimetry. Specifically, in cases where the frequency spectrum bandwidth of a unidirectional flow extends into both negative and positive frequency domains, directional filtering may erroneously produce bidirectional velocities. Here, we propose an adaptive color Doppler ultrasound (aCD_US) technique, which addresses this issue by analyzing the envelope of the Doppler spectrum and then adaptively using the whole spectrum integration or directional filtering-based approach to estimate the flow velocity. The proposed aCD_US was validated through numerical simulations and phantom experiments under various flow conditions, demonstrating superior performance in estimating axial velocities of unidirectional, bidirectional, and horizontal flows. Notably, numerical simulations showed that aCD_US achieved over 90% directional accuracy and less than 15% velocity deviation at signal-to-noise ratios (SNRs) larger than -1 dB. In vivo, experiments on mouse cerebral blood flow further highlighted its advantages, with aCD_US surpassing conventional color Doppler velocimetry and iCD_US in reconstructing axial flow velocity maps. The quantitative comparison between aCD_US and vULM shows a strong overall correlation in their axial velocity measurements, with a Pearson correlation coefficient of 0.760 ( ${p} =0.000$ ). These results demonstrate the advantage of aCD_US in precise microvessel network velocity quantification and its potential to advance microvascular imaging accuracy in both research and clinical applications.