🤖 AI Summary
Existing cerebral artery feature extraction methods—whether manual or automatic—suffer from high user dependency, insufficient validation, and limited quantification capability for subtle vascular changes. To address these limitations, we propose ArteryX, a semi-supervised framework integrating a vessel-tracking network with an in silico quantitative validation mechanism. The vessel-tracking component, implemented in MATLAB, performs keypoint localization and graph-structured modeling to mitigate vessel discontinuities and erroneous connections. The validation module employs a synthetic vascular phantom with predefined ground truth, enabling standardized, objective performance benchmarking. Evaluated on TOF-MRA data from patients with cerebral small vessel disease, ArteryX processes each case in 10–15 minutes with minimal manual intervention. It demonstrates significantly higher sensitivity to subtle vascular alterations compared to state-of-the-art semi-automatic approaches, underscoring its potential for clinical translation.
📝 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.