用稀疏超声定位显微镜数据重建脑血管血流动力学，提升成像精度。
Reconstructing 3D Neural Hemodynamics using Sparse Ultrasound Localization Microscopy Data
- 基于层流模型与随机变分推断，从稀疏数据中恢复血流速度。
- 在模拟和大鼠脑部成像中验证，可准确重建多种血流动态参数。
- 生成压力梯度与不确定性图，适合神经功能研究者使用。
超声定位显微镜（ULM）在功能成像中展现出巨大潜力，可重建深层微血管结构。然而，由于微泡轨迹数量有限，其血流动力学重建受制于数据稀疏性，无法完整采样单个血管的速度分布。本文提出一种方法，通过随机变分推断求解层流模型，利用稀疏的ULM速度图重建血流动力学。该方法不仅恢复血管几何结构与流速图，还生成两个新地图：压力梯度图与估计不确定性图。通过仿真和3D大鼠脑成像，我们系统评估了不同稀疏度对血流动态量化与可视化的影响，证明了该方法在处理稀疏ULM数据时的有效性。利用稀疏ULM数据准确重建广泛血流动态参数及其不确定性，有助于检测细微且动态的脑活动。
原文摘要 · Abstract (English)
Ultrasound Localization Microscopy (ULM) has presented great potential in functional imaging, benefiting from its ability to reconstruct deep microvasculature. However, the hemodynamic reconstruction is compromised by sparsity in the ULM data, as a limited number of MB tracks cannot sample the complete speed profile in one vessel. Here, we propose to reconstruct hemodynamics using sparse ULM velocity maps by solving a laminar flow model through stochastic variational inference. In addition to vascular geometry and flow velocity maps, the proposed method generates two new ULM maps - a pressure gradient map and a map describing uncertainty of the estimation. By investigating the effect of sparsity in ULM maps on the quantification and visualization of hemodynamics, we demonstrate the effectiveness of the proposed method in dealing with sparse ULM maps via simulations and 3D rat brain imaging. Accurately reconstructing a broad range of hemodynamic parameters and associate uncertanties using sparse ULM data may help detect subtle and dynamic brain activity.
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