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.
Abstract (translated)
脑血管病变在认知功能下降和神经性疾病的发生中扮演着重要角色，这凸显了评估血管完整性所需先进工具的重要性。三维时间飞跃磁共振血管造影（3D TOF MRA）被广泛用于可视化大脑血管结构；然而，临床评价通常侧重于主要动脉异常的检查，而忽视了定量指标对理解细微血管变化的关键作用。无论是手动还是自动化的方法，在从MRA中提取结构、几何和形态学的动脉特征时都面临着挑战，包括用户依赖性变异、陡峭的学习曲线以及缺乏标准化的定量验证。 我们提出了一种名为ArteryX的新颖半监督动脉评估框架，这是一个基于MATLAB工具箱，能够以高准确性和效率量化血管特征。在0.5毫米分辨率下处理每个受试者的耗时约为10-15分钟，并且只需要最小限度的人工干预。ArteryX采用了一种基于融合血管的网络定位方法来可靠地追踪和管理轨迹，有效地解决了悬挂/断开连接的血管问题。 人体实验验证显示，对于患有脑小血管病的受试者，ArteryX对细微血管变化具有更高的敏感度，并且优于现有的半自动化方法。重要的是，ArteryX工具箱通过整合一种基于真实动脉模拟框架的方法进行定量特征验证，该框架利用融合血管图节点和预定义的真实基准特征来进行特定类型动脉的评估。 因此，ArteryX框架为基准测试功能提取工具箱以及无缝集成到临床工作流程中提供了希望。它能够早期检测脑血管病变，并对患者群体间进行标准化比较以增进我们对血管在大脑健康贡献的理解。
URL
https://arxiv.org/abs/2507.07920
