Ultrasound Localization Microscopy can resolve the microvascular bed down to a few micrometers. To achieve such performance microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which lead to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo on a rat brain acquisition. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 $\mu$m with an increase in resolution when compared against a conventional approach.

翻译：超声定位显微镜能够解析微米量级的微血管床。为实现这一性能，微泡造影剂必须灌注整个微血管网络。随后需对每个微泡进行单独定位并随时间追踪，以采样单个血管，通常需要处理数十万幅图像。为突破衍射极限并实现高密度网络重建，必须使用低浓度微泡，这会导致采集时间长达数分钟。传统处理流程无法应对邻近多个微泡的干扰，进一步限制了可达到的浓度。本研究通过提出一种深度学习方法解决了该问题，该方法利用高浓度微泡的超声采集数据来重建高密度血管网络。基于双光子显微镜分割的真实小鼠脑微血管网络，采用V-net架构训练了三维卷积神经网络。通过模拟微泡在微血管网络中流动的超声数据集，并以此作为真实标注训练3D-CNN追踪微泡。该3D-CNN方法在计算机模拟中利用部分数据进行了验证，并在大鼠脑部采集实验中进行了体内验证。计算机模拟结果显示，CNN重建血管网络的精度（81%）优于传统ULM框架（70%）。体内实验表明，与传统方法相比，CNN能够解析低至10微米的微血管，且分辨率有所提升。