An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis

TL;DR

CaravelMetrics is an automated, scalable framework that models cerebrovascular networks from 3D scans, enabling multiscale analysis of vascular morphology and topology across large populations.

Contribution

It introduces a novel automated graph-based framework for large-scale cerebrovascular analysis, integrating regional parcellation and feature extraction.

Findings

Reproducible vessel graphs capture age and sex variations.

Identifies education-related increases in vascular complexity.

Supports population-level cerebrovascular studies.

Abstract

We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The framework integrates atlas-based regional parcellation, centerline extraction, and graph construction to compute fifteen morphometric, topological, fractal, and geometric features. The features can be estimated globally from the complete vascular network or regionally within arterial territories, enabling multiscale characterization of cerebrovascular organization. Applied to 570 3D TOF-MRA scans from the IXI dataset (ages 20-86), CaravelMetrics yields reproducible vessel graphs capturing age- and sex-related variations and education-associated increases in vascular complexity, consistent with findings reported in the literature. The framework provides a scalable and fully automated approach for quantitative cerebrovascular feature extraction, supporting normative modeling and population-level studies of vascular health and aging.