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 .

中文翻译：

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RF-ULM： 从射频波前学习的超声定位显微镜


在超声定位显微镜 （ULM） 中，实现高分辨率图像依赖于造影剂颗粒在一系列波束成形框架上的精确定位。然而，我们的研究揭示了一个巨大的潜力：延迟和波束成形过程导致射频 （RF） 信道数据不可逆地减少，而其对定位的影响在很大程度上仍未得到探索。RF 波前中嵌入的丰富上下文信息，包括其双曲线形状和相位，为在具有挑战性的定位场景中指导深度神经网络 （DNN） 提供了巨大的希望。为了充分利用这些数据，我们建议直接在射频信道数据中定位散射体。我们的方法涉及使用学习特征通道改组、非极大值抑制和半全局卷积块的自定义超分辨率 DNN，以实现可靠和准确的波前定位。此外，我们还引入了几何点变换，有助于无缝映射到 B 模式坐标空间。为了了解波束成形对 ULM 的影响，我们通过与最先进的 （SOTA） 技术进行广泛比较来验证我们方法的有效性。我们展示了波前定位 DNN 的首个体内结果，突出了其实际实用性。我们的结果表明，RF-ULM 在合成数据集和真实数据集之间的域转移之间架起了桥梁，在精度和复杂性方面具有相当大的优势。为了使更广泛的研究界能够从我们的发现中受益，我们的代码和相关的 SOTA 方法可在 https://github.com/hahnec/rf-ulm 上获得。