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：肾血管系统作为资源分配网络，在肾脏生理与病理生理过程中均发挥重要作用。然而，由于空间和时间分辨率的限制，现有成像技术无法评估肾血管系统的结构与功能。为开发基于人工智能的肾脏功能真实计算机模拟与新型影像诊断方法，需构建肾血管系统的真实全尺寸模型。我们提出一种混合框架以建立个体化的肾血管网络模型：以微CT扫描中大型动脉的半自动分割及皮质区域估算为起点，采用全局构造优化算法生成更小血管。结果表明，重建数据与大鼠肾脏现有解剖数据在形态测量学和血流动力学参数方面具有统计一致性。