Morphological and Topological Analysis of the Human Renal Arterial Tree Using μ-CT Scans of Corrosion Cast Specimens

This study addresses the analysis of morphological and topological relationships within the human renal arterial tree, which have significant implications for improving clinical practices, particularly in minimally invasive renal surgeries, where detailed information on vascular supply is necessary to preserve healthy parenchyma. Recent advances in imaging technologies, coupled with increased computational power, provide unprecedented detail in anatomical assessment and enable a comprehensive analysis. The presented investigation employed $33~\mu $ -CT scans of corrosion cast speciments of renal arterial trees, which underwent systematic processing, including binary reconstruction, artifact removal, cavity filling, skeletonization, and conversion into directed graph representations. This approach facilitated the computation of geometrical parameters and enabled morphological and topological analyses. The results confirm the feasibility of using $\mu $ -CT imaging to investigate both global population-level trends and intra-renal tree variations. Key findings reveal no strong individual predisposition for specific branching degrees, with trifurcations and quadfurcations being notable exceptions. Additionally, a significant inverse correlation between vessel length and diameter across generations was observed. The analysis of subtrees indicated which vessels supply specific renal segments, offering valuable information for preoperative planning, particularly in tumor surgeries. Key insights into vessel branching patterns highlight their relevance for renal surgeries. These findings have direct applications in enhancing algorithms for the reconstruction, segmentation, and visualization of preoperative CT scans by leveraging insights from $\mu $ -CT based vascular analysis. Despite limitations such as datasize constraints and artifacts inherent to historical corrosion cast specimens, this study provides a proof-of-concept framework. The proposed methodology lays the foundation for large-scale investigations and the integration of $\mu $ -CT-derived vascular patterns into patient-specific preoperative models, ultimately improving surgical navigation and patients’ outcomes. The suite of algorithms developed by us is easily scalable as more data becomes available and can be adapted for other tree-like structures, further enhancing their applicability.