ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework

TL;DR

ArteryX is a semi-supervised MATLAB toolbox that accurately extracts and validates cerebral artery features from MRA images, improving sensitivity to subtle vascular changes and supporting clinical and research applications.

Contribution

The paper introduces ArteryX, a novel vessel-fused network-based framework with a validation system, enhancing artery feature extraction accuracy and standardization over existing methods.

Findings

ArteryX achieves processing times of 10-15 minutes per subject.

It demonstrates improved sensitivity to subtle vascular changes.

Outperforms existing semi-automated artery analysis methods.

Abstract

Cerebrovascular pathology significantly contributes to cognitive decline and neurological disorders, underscoring the need for advanced tools to assess vascular integrity. Three-dimensional Time-of-Flight Magnetic Resonance Angiography (3D TOF MRA) is widely used to visualize cerebral vasculature, however, clinical evaluations generally focus on major arterial abnormalities, overlooking quantitative metrics critical for understanding subtle vascular changes. Existing methods for extracting structural, geometrical and morphological arterial features from MRA - whether manual or automated - face challenges including user-dependent variability, steep learning curves, and lack of standardized quantitative validations. We propose a novel semi-supervised artery evaluation framework, named ArteryX, a MATLAB-based toolbox that quantifies vascular features with high accuracy and efficiency, achieving processing times ~10-15 minutes per subject at 0.5 mm resolution with minimal user intervention. ArteryX employs a vessel-fused network based landmarking approach to reliably track and manage tracings, effectively addressing the issue of dangling/disconnected vessels. Validation on human subjects with cerebral small vessel disease demonstrated its improved sensitivity to subtle vascular changes and better performance than an existing semi-automated method. Importantly, the ArteryX toolbox enables quantitative feature validation by integrating an in-vivo like artery simulation framework utilizing vessel-fused graph nodes and predefined ground-truth features for specific artery types. Thus, the ArteryX framework holds promise for benchmarking feature extraction toolboxes and for seamless integration into clinical workflows, enabling early detection of cerebrovascular pathology and standardized comparisons across patient cohorts to advance understanding of vascular contributions to brain health.