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 https://github.com/hahnec/rf-ulm.

翻译：摘要：在超声定位显微镜（ULM）中，实现高分辨率图像依赖于对波束合成连续帧中造影剂粒子的精确定位。然而，我们的研究揭示了一个巨大的潜力：延迟求和波束合成过程会导致射频（RF）数据的不可逆缩减，而其对于定位的影响在很大程度上尚未被探索。RF波前中蕴含的丰富上下文信息，包括其双曲线形状和相位，为引导深度神经网络（DNN）应对具有挑战性的定位场景提供了巨大前景。为充分利用这些数据，我们提出直接在RF信号中对散射体进行定位。我们的方法包含一个定制的超分辨率深度神经网络，采用可学习的特征通道洗牌技术和一个新颖的半全局卷积采样模块，专门针对RF输入数据中可靠且精确的定位而设计。此外，我们引入了几何点变换机制，以促进B模式和RF空间之间的无缝映射。为验证我们方法的有效性并理解波束合成的影响，我们与超声定位显微镜领域的最先进（SOTA）技术进行了广泛对比。我们展示了由RF训练的深度神经网络首次获得的体内实验结果，突显了其实用价值。研究结果表明，RF-ULM弥合了合成数据集与真实数据集之间的领域差距，在精度和复杂度方面提供了显著优势。为使更广泛的研究社区能够受益于我们的发现，我们的代码及相关的SOTA方法已在https://github.com/hahnec/rf-ulm上公开。