  Thanks to its capability of acquiring full-view frames at multiple kilohertz,
ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical
phenomena in the human body, with pioneering applications such as
ultrasensitive flow imaging in the cardiovascular system or shear-wave
elastography. The accuracy achievable with these motion estimation techniques
is strongly contingent upon two contradictory requirements: a high quality of
consecutive frames and a high frame rate. Indeed, the image quality can usually
be improved by increasing the number of steered ultrafast acquisitions, but at
the expense of a reduced frame rate and possible motion artifacts. To achieve
accurate motion estimation at uncompromised frame rates and immune to motion
artifacts, the proposed approach relies on single ultrafast acquisitions to
reconstruct high-quality frames and on only two consecutive frames to obtain
2-D displacement estimates. To this end, we deployed a convolutional neural
network-based image reconstruction method combined with a speckle tracking
algorithm based on cross-correlation. Numerical and in vivo experiments,
conducted in the context of plane-wave imaging, demonstrate that the proposed
approach is capable of estimating displacements in regions where the presence
of side lobe and grating lobe artifacts prevents any displacement estimation
with a state-of-the-art technique that relies on conventional delay-and-sum
beamforming. The proposed approach may therefore unlock the full potential of
ultrafast ultrasound, in applications such as ultrasensitive cardiovascular
motion and flow analysis or shear-wave elastography.
