A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network
This work addresses the need for subject-specific renal vascular models to improve kidney function simulations and diagnostic methods, representing an incremental advancement in medical imaging and computational modeling.
The paper tackled the problem of lacking realistic full-scale models of the renal vasculature for simulations and AI-based diagnostics by proposing a hybrid framework that combines semi-automated segmentation with an optimization algorithm, achieving statistical correspondence with anatomical data from a rat kidney in morphometric and hemodynamic parameters.
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.