Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, 2100, Denmark.
Department of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark.
Sci Rep. 2023 May 9;13(1):7569. doi: 10.1038/s41598-023-34739-y.
The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.
肾脏血管系统作为一种资源分配网络，在肾脏的生理学和病理生理学中起着重要作用。然而，由于空间和时间分辨率有限，没有任何成像技术可以评估肾脏血管的结构和功能。为了对肾功能进行逼真的计算机模拟，并开发基于人工智能的新的基于图像的诊断方法，有必要建立一个真实的肾脏血管网络全尺度模型。我们提出了一种混合框架，通过使用半自动化分割大动脉和从小鼠 CT 扫描中估计皮质面积作为起点，采用全局构造优化算法生成较小的血管，来构建特定于个体的肾脏血管网络模型。我们的结果表明，重建的血管与从大鼠肾脏获得的现有解剖数据在形态计量学和血流动力学参数方面具有密切的一致性。
