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 statistical correspondence between the reconstructed data and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.

翻译：肾血管作为资源分布网络,在肾的生理和病理生理方面起着重要作用,然而,由于空间和时间分辨率有限,没有任何成像技术能够评估肾血管的结构和功能。为了发展现实的肾功能计算机模拟,并开发以人工智能为基础的基于图像的新诊断方法,必须有一个现实的肾血管血管全面模型。我们提议了一个混合框架,以利用大型动脉的半自动分解和微CT扫描对皮层区域的估计作为起点,并采用全球构造优化算法生成小船。我们的结果显示,在重造数据与从大鼠肾中获取的关于光度和热力参数的现有解剖学数据之间,存在着统计上的对应关系。</s>