3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming

TL;DR

This paper introduces adaptive weight-based coherence beamformers, including a variance-based method, to enhance 3D super-resolution ultrasound imaging by improving microbubble localization and reducing artifacts.

Contribution

The study develops and compares novel adaptive coherence beamformers for 3D SRUS, demonstrating significant improvements over standard methods in simulations and in vivo experiments.

Findings

Adaptive beamformers narrow main lobes and suppress side lobes.

Variance-based beamformer performs best in simulations and experiments.

Enhanced 3D SR images of microflow phantom and rabbit kidney achieved.

Abstract

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.