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

基于自动机器学习的脑动脉分割、解剖先验标记和 MR 血管造影特征提取

• 批准号: 10759721
• 负责人: Md Nasir Uddin
• 金额: $ 15.4万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10759721
• 关键词: 3-Dimensional; Address; Affect; Anatomy; Angiography; Arteries; Attention; Biological Markers; Blood - brain barrier anatomy; Blood Vessels; Blood capillaries; Brain; Case Study; Cause of Death; Cerebral small vessel disease; Cerebrovascular Disorders; Cerebrum; Classification; Clinical; Clinical Data; Cognition; Cohort Studies; Collaborations; Consumption; Contrast Media; Data; Data Analytics; Data Engineering; Data Set; Databases; Diagnosis; Diameter; Disease Marker; Distal; Effectiveness; Functional disorder; Goals; HIV; HIV Infections; High Prevalence; Image; Individual; Intervention; Label; Lead; MRI Scans; Machine Learning; Magnetic Resonance Angiography; Magnetic Resonance Imaging; Manuals; Measures; Methods; Monitor; Morphology; Neurocognitive; Participant; Pathogenesis; Patient-Focused Outcomes; Penetration; Persons; Play; Protocols documentation; Quality of life; Reading; Resolution; Risk; Role; Speed; Subcortical Infarctions; Techniques; Time; Training; Translations; Vascular Cognitive Impairment; Vascular Dementia; Vascular Diseases; Visual; White Matter Hyperintensity; antiretroviral therapy; arteriole; automated segmentation; biomarker performance; brain based; cerebral atrophy; cerebral microbleeds; cerebrovascular; clinical diagnosis; clinical examination; cognitive function; cognitive performance; cohort; deep learning; disability; executive function; feature extraction; imaging biomarker; improved; in vivo; machine learning framework; neuroimaging; neuroimaging marker; neurovascular unit; novel strategies; processing speed; prospective; reconstruction; ultra high resolution; vascular abnormality; vascular risk factor; venule

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.

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）是评估血管异常的常见无创技术。准确的 从TOF-MRA数据中的细分和特征提取大脑视频对于诊断和 脑血管疾病的治疗。但是，船只的手动注释甚至是一项耗时的任务 对于专家而言，自动分割方法可以显着加速任务。虽然有几个 存在基于机器学习的后处理方法，这些方法仅限于船只分割， 需要大量的手动分割培训数据集。此外，图像分辨率在细分方面起着重要作用 血管以及提取特征（例如，直径，分支数量，曲折的数量）。 目标1：开发一个自动解剖学先验的机器学习框架 通过最小训练的分割MR高分辨率数据，然后是低分辨率提取的特征提取 来自现有数据库的MRA。 目标2：评估靶向血管特征与脑神经影像学之间的关系 小血管疾病（CSVD）。 目标3：评估靶向血管特征与HIV CSVD中的认知表现之间的关系 队列。 为了实现这一目标，我们将实施我们最近提出的自动化船只细分和功能 提取管道“ Bayestract”与超分辨率方法结合使用。我们将使用体内验证它 MRI扫描。然后，我们将检查血管特征及其与CSVD标记和认知的关联 现有数据集（使用CSVD的101 HIV+和102 HIV控制）中的性能。我们假设这是 与现有方法相比，方法将提供更准确和及时的措施。我们也是 假设血管特征及其与CSVD标记和认知的关联将显着 有和没有CSVD的人不同。 这项研究有助于提高脑血管分割的最新技术，并从非 - 侵入性TOF-MRA，这可能是将相关生物标志物转化为临床环境的转化。这 评估承诺的干预措施至关重要，最终导致患者的结果和 生活质量。