Based on the diameter and position information of small vessels obtained by transcranial super-resolution imaging using 3 MHz low-frequency chirp plane waves, a Gaussian-like non-linear compression was adopted to compress the blood flow signals in spatiotemporal filtering (STF) data to a precise region, and then estimate the blood flow velocity field inside the region over the adjacent time intervals using ultrasound imaging velocimetry (UIV). Imaging parameters, such as the mechanical index (MI), frame rate, and microbubble (MB) concentration, are critical during the estimation of velocity fields over a short time at high MB contrast agent concentrations. These were optimized through experiments and algorithms, in which dividing the connected domain was proposed to calculate MB cluster spot centroid spacing (SCS) and the spot-to-flow area ratio (SFAR) to determine the suitable MB concentration. The results of the in vitro experiments showed that the estimation of the small vessel flow velocity field was consistent with the theoretical results; the velocity field resolution for vessels with diameters of 0.5 mm and 0.3 mm was 36 μm and 21 μm, and the error between the mean velocity and the theoretical value was 0.7 % and 0.67 %, respectively.
Keywords: Non-linear compression; Super-resolution imaging; Transcranial ultrasound; Ultrasound imaging velocimetry; Velocity field estimation.
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