Adaptive beamforming combined with decision theory-based detection for ultrasound localization microscopy

Ultrasound Localisation Microscopy (ULM) is an imaging framework which consists in tracking microbubbles (MBs) on ultrasound (US) images to estimate their trajectory and thus the map of the vascular network. ULM algorithms takes as input US images usually beamformed with the delay-and-sum (DAS) method. In a previous study, we have shown that adaptive beamforming enhances the results of ULM on simulated data by detecting more MBs and localizing them more precisely. In another study, we introduced a new MB detection method based on decision theory. In this paper, adaptive beamformers such as Capon, pDAS and iMAP combined to this detection method are applied on in vivo rat brain data. Results show that this combination allow to identify more MBs and thus to represent more vessels in the ULM vascular network map.