Microbubble detection with adaptive beamforming for Ultrasound Localization Microscopy

Abstract

Ultrasound Localisation Microscopy (ULM) is an imaging framework which consists of following ultrasound contrast agents, microbubbles, in time, on ultrasound images. The three main steps of ULM are: detecting microbubbles by reducing tissue signal, localizing them with subwavelength precision and tracking their trajectories. ULM performances were evaluated in different studies throughout metrics such as localisation accuracy or capacity to filter the tissues. In parallel, adaptive beamforming offers narrower Point Spread Function (PSF) and/or better tissue filtering than delay-and-sum method classically used within ULM. In this paper, the ability of adaptive beamformers to enhance ULM performances is evaluated, with a particular focus on the trade-off between acquisition time and bubble concentration to achieve super-resolution results.