In Ultrasound Localization Microscopy (ULM),achieving high-resolution images relies on the precise localization of contrast agent particles across consecutive beam-formed 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 wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping between RF and 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 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：在超声定位显微镜（ULM）中，实现高分辨率图像依赖于在连续波束合成帧上精确对造影剂粒子进行定位。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。射频波前中蕴含的丰富上下文信息，包括其双曲形状和相位，为在具有挑战性的定位场景中引导深度神经网络提供了巨大前景。为充分利用这些数据，我们提出直接在射频信号中对散射体进行定位。我们的方法包括一个定制的超分辨率深度神经网络，该网络采用学习到的特征通道混洗以及一种专为可靠且精确的波前定位而设计的新型半全局卷积采样模块。此外，我们引入了一种几何点变换，便于在射频与B模式坐标空间之间实现无缝映射。为了理解波束合成对ULM的影响，我们通过与现有最先进技术进行广泛比较来验证我们方法的有效性。我们展示了首个来自射频训练深度神经网络的在体结果，凸显了其在实际应用中的实用性。我们的研究结果表明，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，我们的代码及相关最先进方法已在 https://github.com/hahnec/rf-ulm 上开源。