In Ultrasound Localization Microscopy (ULM), achieving high-resolution images
relies on the precise localization of contrast agent particles across
consecutive 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) 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
signals. Our approach involves a custom super-resolution DNN using learned
feature channel shuffling and a novel semi-global convolutional sampling block
tailored for reliable and accurate localization in RF input data. Additionally,
we introduce a geometric point transformation that facilitates seamless mapping
between B-mode and RF spaces. To validate the effectiveness of our method and
understand the impact of beamforming, we conduct an extensive comparison with
State-Of-The-Art (SOTA) techniques in ULM. We present the inaugural in vivo
results from an RF-trained DNN, highlighting its real-world practicality. Our
findings show that RF-ULM bridges the domain gap 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
this https URL

采用超分辨率深度神经网络和几何点变换的射频超声成像技术在定位过程中发现有巨大潜力，可以有效地提高精度和复杂性，弥合了合成和真实数据集之间的领域差距。