  Ultrasound Localization Microscopy (ULM) enables imaging of vascular
structures in the micrometer range by accumulating contrast agent particle
locations over time. Precise and efficient target localization accuracy remains
an active research topic in the ULM field to further push the boundaries of
this promising medical imaging technology. Existing work incorporates
Delay-And-Sum (DAS) beamforming into particle localization pipelines, which
ultimately determines the ULM image resolution capability. In this paper we
propose to feed unprocessed Radio-Frequency (RF) data into a super-resolution
network while bypassing DAS beamforming and its limitations. To facilitate
this, we demonstrate label projection and inverse point transformation between
B-mode and RF coordinate space as required by our approach. We assess our
method against state-of-the-art techniques based on a public dataset featuring
in silico and in vivo data. Results from our RF-trained network suggest that
excluding DAS beamforming offers a great potential to optimize on the ULM
resolution performance.

Image and Video Processing;Computer Vision and Pattern Recognition;Medical Physics