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.

提出采用半自动分割技术和全局构造优化算法构建个体化肾血管网络模型，以实现肾功能计算机模拟和基于人工智能的诊断方法，并通过一只大鼠肾脏的解剖数据对模型进行有效性验证。