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 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 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：在超声定位显微镜（ULM）中，获取高分辨率图像依赖于在一系列波束合成帧中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：延时累加波束合成过程会导致射频通道数据的不可逆缩减，而其对定位的影响尚未得到充分探索。射频波前中嵌入的丰富上下文信息，包括其双曲形状和相位，为引导深度神经网络在具有挑战性的定位场景中提供了巨大前景。为充分利用这些数据，我们提出直接在射频通道数据中对散射体进行定位。我们的方法涉及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、非极大值抑制以及半全局卷积模块，以实现可靠且精确的波前定位。此外，我们引入了一种几何点变换，便于在射频和B模式坐标空间之间进行无缝映射。为理解波束合成对ULM的影响，我们通过与最新技术进行广泛对比验证了方法的有效性。我们展示了来自射频训练深度神经网络的首次体内实验结果，突显其实用价值。我们的研究发现表明，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具有显著优势。为使更广泛的研究社区受益于我们的发现，我们的代码及相关最新方法已在https://github.com/hahnec/rf-ulm公开。