Ultrasound Localization Microscopy (ULM) enables imaging of vascular structures in the micrometer range by accumulating contrast agent particle locations over time. Precise and efficient target localization accuracy remains an active research topic in the ULM field to further push the boundaries of this promising medical imaging technology. Existing work incorporates Delay-And-Sum (DAS) beamforming into particle localization pipelines, which ultimately determines the ULM image resolution capability. In this paper we propose to feed unprocessed Radio-Frequency (RF) data into a super-resolution network while bypassing DAS beamforming and its limitations. To facilitate this, we demonstrate label projection and inverse point transformation between B-mode and RF coordinate space as required by our approach. We assess our method against state-of-the-art techniques based on a public dataset featuring in silico and in vivo data. Results from our RF-trained network suggest that excluding DAS beamforming offers a great potential to optimize on the ULM resolution performance.

翻译：超声定位显微成像（ULM）通过随时间累积造影剂粒子位置，能够在微米范围内对血管结构进行成像。精确高效的靶点定位精度仍是ULM领域活跃的研究课题，旨在进一步推动这一前景广阔的医学影像技术的发展极限。现有研究将延迟求和（DAS）波束成形纳入粒子定位流程，这最终决定了ULM图像的分辨率能力。本文提出将未处理的射频（RF）数据直接输入超分辨率网络，从而绕过DAS波束成形及其局限性。为此，我们展示了该方法所需的B模式与RF坐标空间之间的标签投影与逆向点变换。我们基于包含计算机模拟数据和活体数据的公开数据集，将所提方法与现有最优技术进行了评估。采用RF训练网络的结果表明，排除DAS波束成形在优化ULM分辨率性能方面具有巨大潜力。