  In Ultrasound Localization Microscopy (ULM), achieving high-resolution images
relies on the precise localization of contrast agent particles across a series
of beamformed frames. However, our study uncovers an enormous potential: The
process of delay-and-sum beamforming leads to an irreversible reduction of
Radio-Frequency (RF) channel data, while its implications for localization
remain largely unexplored. The rich contextual information embedded within RF
wavefronts, including their hyperbolic shape and phase, offers great promise
for guiding Deep Neural Networks (DNNs) in challenging localization scenarios.
To fully exploit this data, we propose to directly localize scatterers in RF
channel data. Our approach involves a custom super-resolution DNN using learned
feature channel shuffling, non-maximum suppression, and a semi-global
convolutional block for reliable and accurate wavefront localization.
Additionally, we introduce a geometric point transformation that facilitates
seamless mapping to the B-mode coordinate space. To understand the impact of
beamforming on ULM, we validate the effectiveness of our method by conducting
an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the
inaugural in vivo results from a wavefront-localizing DNN, highlighting its
real-world practicality. Our findings show that RF-ULM bridges the domain shift
between synthetic and real datasets, offering a considerable advantage in terms
of precision and complexity. To enable the broader research community to
benefit from our findings, our code and the associated SOTA methods are made
available at https://github.com/hahnec/rf-ulm.
