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.

翻译：在超声定位显微成像（ULM）中，实现高分辨率图像依赖于在一系列波束合成帧上对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通道数据发生不可逆的降质，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（包括双曲形状和相位）为引导深度神经网络应对具有挑战性的定位场景提供了巨大前景。为充分利用该数据，我们提出直接在射频通道数据中定位散射体。我们的方法涉及一种定制化的超分辨率深度神经网络，其采用学习型特征通道混洗、非极大值抑制以及半全局卷积模块来实现可靠且准确的波前定位。此外，我们引入了一种几何点变换，以便于无缝映射至B模坐标空间。为理解波束合成对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方法的有效性。我们展示了波前定位深度神经网络的首个体内成像结果，凸显其实际应用价值。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域偏移，在精度和复杂度方面均具有显著优势。为使更广泛的研究社群受益于我们的发现，相关代码及所对比的最先进方法已在https://github.com/hahnec/rf-ulm 公开。