Fast 3D Super-Resolution Ultrasound With Adaptive Weight-Based Beamforming

Abstract

Objective 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 high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. Method This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p -th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo . Results Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS. Conclusion The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments. Significance Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging

“…Accurate microbubble localization is crucial for super-resolution imaging to overcome the diffraction limit. However, the detection of microbubbles is highly susceptible to clutter signals, especially ultrasonic signal attenuation noise, electronic noise, and thermal noise (Huang et al 2021, Yan et al 2023. AC-HOSVD is conducive to uncoupling separated microbubble signals from clutter, thus improving the reliability of blood flow imaging.…”

3D Acoustic Wave Sparsely Activated Localization Microscopy With Phase Change Contrast Agents

“…Super-resolution image quality is strongly dependent upon acquisition time and MB concentration, and hence is challenging to improve. Strategies such as MB uncoupling, MB separation, or improved beamforming have been trialed with success [34][35][36] but are still restricted by the limitations of using MBs. By increasing the frequency at which PCCAs are activated in the microcirculation, the acquisition time could be reduced.…”

Transthoracic ultrasound localization microscopy of myocardial vasculature in patients

“…B-mode images were reconstructed using the delay-and-sum beamforming method with coherent angle compounding 40 , keeping the linearity of image intensity to meet the requirement of the SVD that was used in tissue motion correction. CEUS images were reconstructed using the CV beamforming method 41 to reduce the side lobes and noise, benefitting the MB isolation for super-localization. The CV beamformer calculates an adaptive weight for each pixel using the ratio of the squared coherent sum to the variance across all the channels and all the steering angles.…”