A hybrid approach to full-scale reconstruction of renal arterial network

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.

Automated summary

The renal vasculature plays a crucial role in kidney function both in normal physiology and in disease. However, current imaging techniques lack sufficient resolution to assess its structure and function. To develop realistic models and diagnostic methods, we propose a hybrid framework that combines automated segmentation and optimization algorithms to create subject-specific models of the renal vascular network. Our results demonstrate a close agreement with existing anatomical data, validating the accuracy of our approach.