利用稀疏超声定位显微镜数据重建三维神经血流动力学
Reconstructing 3D Neural Hemodynamics using Sparse Ultrasound Localization Microscopy Data
AI总结：
针对超声定位显微镜数据稀疏导致血流动力学重建受限的问题，提出基于随机变分推断求解层流模型的方法，生成速度、压力梯度和不确定性图，并通过仿真和三维大鼠脑成像验证其有效性。
摘要：
超声定位显微镜（ULM）因其能够重建深层微血管结构而在功能成像中展现出巨大潜力。然而，由于有限数量的微泡轨迹无法采样单个血管内的完整速度分布，ULM数据的稀疏性影响了血流动力学重建。本文提出通过随机变分推断求解层流模型，利用稀疏ULM速度图重建血流动力学。除了血管几何结构和血流速度图，该方法还生成两种新的ULM图——压力梯度图和描述估计不确定性的图。通过研究ULM图中稀疏性对血流动力学量化和可视化的影响，我们通过仿真和三维大鼠脑成像证明了该方法在处理稀疏ULM图方面的有效性。利用稀疏ULM数据准确重建广泛的血流动力学参数及其相关不确定性，可能有助于检测微妙且动态的脑活动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
