Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography

脑血管疾病是全球死亡和残疾的主要原因。飞行时间磁共振 血管造影(TOF-MRA)是一种常见的无创性评估血管异常的技术。准确 TOF-MRA数据中脑血管的分割和特征提取是诊断和诊断的关键。 脑血管疾病的治疗。然而，人工对血管进行注释即使是一项耗时的任务 对于专家来说，自动分割方法可以显著加快任务的速度。尽管有几个 现有的基于机器学习的后处理方法仅限于血管分割和 需要大量手动分割的训练数据集。此外，图像分辨率对分割起着重要的作用 以及准确地提取特征(例如，直径、分支数量、曲折度)。 目的1：提出一种基于自动解剖先验的脑动脉机器学习框架 基于最小训练MR高分辨率数据和低分辨率特征提取的分割 来自现有数据库的MRA。 目的2：探讨脑部靶向血管特征与神经影像生物标志物的关系 小血管病(CSVD)。 目的3：评估HIV-CSVD患者靶血管特征与认知功能的关系 一群人。 为了实现这一目标，我们将实施我们最近提出的自动血管分割和特征 提取流水线“BayesTract”与超分辨率方法相结合。我们将使用体内实验进行验证 核磁共振扫描。然后，我们将检查血管特征及其与CSVD标记物和认知的关系 在现有数据集中的表现(101个HIV+和102个HIV-CSVD对照)。我们假设这是 与现有方法相比，该方法将提供更准确和更省时的措施。我们也 假设血管特征及其与CSVD标记物和认知的关联将显著 CSVD组与非CSVD组的差异有统计学意义。 这项研究有助于推动脑血管分割和非脑血管特征提取的研究进展 侵入性TOF-MRA，可以加速血管相关生物标记物的临床应用。这 将是评估有希望的干预措施的关键，并最终导致改善患者结局和 生活质量。

Cerebrovascular disease is a major cause of death and disability globally. Time-of-flight magnetic resonance angiography (TOF-MRA) is a common noninvasive technique to evaluate vascular abnormalities. Accurate segmentation and feature extraction of cerebral vessels from TOF-MRA data is crucial for diagnosis and treatment of cerebrovascular diseases. However, manual annotation of vessels is a time-consuming task even for experts, and an automatic segmentation method can speed-up the task significantly. Although several machine learning-based post-processing approaches exist, these are limited to vessel segmentation only and require large manually segmented training datasets. Further, image resolution plays an important role to segment the vessels as well as extracting features (e.g., diameters, number of branches, tortuosity) accurately. Aim 1: To develop an automated anatomical prior based machine learning framework for brain artery segmentation with minimal training MR high resolution data followed by feature extractions on low resolution MRA from an existing database. Aim 2: To assess the relationship between targeted vessel features and neuroimaging biomarkers of cerebral small vessel disease (CSVD). Aim 3: To assess the relationship between targeted vessel features and cognitive performance in HIV CSVD cohort. To achieve this goal, we will implement our recently proposed automated vessel segmentation and feature extraction pipeline “BayesTract” in combination with a super-resolution approach. We will validate it using in-vivo MRI scans. We will then examine the vascular features and their associations with CSVD markers and cognitive performance in an existing dataset (101 HIV+ and 102 HIV- controls with CSVD). We hypothesize that this approach will provide more accurate and time-efficient measures compared to existing approaches. We also hypothesize that vascular features and their associations with CSVD markers and cognition will be significantly different between those with and without CSVD. This study helps advance the state-of-the-art in brain vessel segmentation and feature extractions from non- invasive TOF-MRA, which could hasten the translation of vessel related biomarkers into the clinical setting. This will be essential in evaluating promising interventions and ultimately, lead to ameliorating patient outcome and quality of life.