Abstract
Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic diffraction limit. However, significant challenges remain in localisation performance and data acquisition and processing time. Deep learning methods for ULM have shown promise to address these challenges, however, they remain limited by in vivo label scarcity and the simulation-to-reality domain gap. We present CycleULM, the first unified label-free deep learning framework for ULM. CycleULM learns a physics-emulating translation between the real contrast-enhanced ultrasound (CEUS) data domain and a simplified MB-only domain, leveraging the power of CycleGAN without requiring paired ground truth data. With this translation, CycleULM removes dependence on high-fidelity simulators or labelled data, and makes MB localisation and tracking substantially easier. Deployed as modular plug-and-play components within existing pipelines or as an end-to-end processing framework, CycleULM delivers substantial performance gains across both in silico and in vivo datasets. Specifically, CycleULM improves image contrast (contrast-to-noise ratio) by up to 15.3 dB and sharpens CEUS resolution with a 2.5{\times} reduction in the full width at half maximum of the point spread function. CycleULM also improves MB localisation performance, with up to +40% recall, +46% precision, and a -14.0 {\mu}m mean localisation error, yielding more faithful vascular reconstructions. Importantly, CycleULM achieves real-time processing throughput at 18.3 frames per second with order-of-magnitude speed-ups (up to ~14.5{\times}). By combining label-free learning, performance enhancement, and computational efficiency, CycleULM provides a practical pathway toward robust, real-time ULM and accelerates its translation to clinical applications.
Abstract (translated)
通过微泡（MB）定位和跟踪的超分辨率超声波，也称为超声定位显微镜（ULM），能够解析出超出声学衍射极限的微血管结构。然而，在定位性能和数据采集及处理时间方面仍然存在重大挑战。基于深度学习的方法在解决这些挑战方面显示出巨大潜力，但它们仍受限于体内标签稀缺以及仿真与现实场景之间的领域差距。我们提出了CycleULM，这是第一个统一的无标记深度学习框架用于超声定位显微镜（ULM）。 CycleULM 学习了一个仿物理现象的真实对比增强超声波（CEUS）数据域和简化后的MB仅存在域之间的转换，利用了CycleGAN的力量而不需配对的真实标签数据。通过这种转换，CycleULM消除了对高保真模拟器或标记数据的依赖，并使MB定位和跟踪变得更加容易。 作为现有流水线中的模块化即插即用组件部署或者作为一个端到端处理框架，CycleULM在基于仿真和体内数据集上都实现了显著性能提升。具体来说，CycleULM将图像对比度提高了高达15.3 dB（信噪比），并将CEUS分辨率提高了一半最大全宽的两倍半，这意味着点扩散函数的宽度减小了2.5倍。 此外，CycleULM改进了MB定位性能，召回率提高了多达40%，精度提高了46%，平均定位误差减少了14.0 μm，从而提供了更加忠实的血管重建。更重要的是，CycleULM实现了每秒18.3帧的真实时间处理吞吐量，并达到了数量级的速度提升（最高可达约14.5倍）。通过结合无标签学习、性能增强和计算效率，CycleULM为稳健且实时的超声定位显微镜提供了实用途径，并加速了其向临床应用的转化。
URL
https://arxiv.org/abs/2603.09840
