  Super-resolution ultrasound (SRUS) imaging through localising and tracking
sparse microbubbles has been shown to reveal microvascular structure and flow
beyond the wave diffraction limit. Most SRUS studies use standard delay and sum
(DAS) beamforming, where large main lobe and significant side lobes make
separation and localisation of densely distributed bubbles challenging,
particularly in 3D due to the typically small aperture of matrix array probes.
This study aims to improve 3D SRUS by implementing a low-cost 3D coherence
beamformer based on channel signal variance, as well as two other adaptive
weight-based coherence beamformers: nonlinear beamforming with p-th root
compression and coherence factor. The 3D coherence beamformers, together with
DAS, are compared in computer simulation, on a microflow phantom, and in vivo.
Simulation results demonstrate that the adaptive weight-based beamformers can
significantly narrow the main lobe and suppress the side lobes for modest
computational cost. Significantly improved 3D SR images of microflow phantom
and a rabbit kidney are obtained through the adaptive weight-based beamformers.
The proposed variance-based beamformer performs best in simulations and
experiments.
