RF Channel-Based Adaptive Beamforming for 3D Ultrasound Localization Microscopy

Abstract

Ultrasound Localization Microscopy (ULM) has been applied in various preclinical settings and in the clinic to reveal the microvasculature in deep organs. However, most ULM images employ standard Delay-and-Sum (DAS) beamforming. In standard ULM conditions, lengthy acquisition times are required to fully reconstruct small vessels due to the need for spatially isolated microbubbles, resulting in low temporal resolution. When microbubbles are densely packed, localizing a point spread function with significant main and side lobes becomes challenging due to matrix arrays low signal-to-noise ratio and spatial resolution. In this work, we applied adaptive beamforming such as high order DAS known as (pDAS), Coherence Factor (CF), Coherence Factor with Gaussian Filtering (CFGF), and statistical interpretation of beamforming (iMAP) to provide a more complete 3D ULM maps in vitro and in vivo (rat kidney). Specifically, the CF and 1MAP adaptive beamformers achieved higher resolution (32.9 microns and 27.2 microns respectively), as measured by the Fourier Shell Correlation (FSC), compared to the standard DAS beamformer, which had an FSC value of 38.6 microns.