Non-invasive Evaluation of Intracranial Atherosclerotic Disease Using Hemodynamic Biomarkers

使用血流动力学生物标志物对颅内动脉粥样硬化疾病进行无创评估

• 批准号: 10248545
• 负责人: Sameer A Ansari
• 金额: $ 49.03万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248545
• 关键词: 3-Dimensional; 4D MRI; Acceleration; Acute; Age; Angioplasty; Biological Markers; Blood Vessels; Blood flow; Brain Infarction; Cerebrovascular Circulation; Cerebrovascular system; Cessation of life; Characteristics; Circle of Willis; Clinical; Clinical Protocols; Control Groups; Core-Binding Factor; Coupled; Cross-Sectional Studies; Data; Data Analyses; Death Rate; Demographic Factors; Development; Diffusion Magnetic Resonance Imaging; Disease; Distal; Drops; Enrollment; Evaluation; Event; Failure; Flowmeters; Gender; General Hospitals; Graph; Hospitals; Image; Impairment; In Vitro; Institution; Intervention; Intracranial Arterial Stenosis; Intracranial Atherosclerotic Disease; Investigation; Ischemic Stroke; Lesion; Location; MRI Scans; Magnetic Resonance Imaging; Measures; Medical; Medical center; Methods; Modality; Outcome; Outcome Measure; Patients; Pattern; Perfusion; Perfusion Weighted MRI; Pilot Projects; Prognosis; Protocols documentation; Recurrence; Refractory; Reporting; Reproducibility; Resolution; Risk; Risk Factors; San Francisco; Scanning; Scheme; Severities; Staging; Stenosis; Stents; Stroke; Subgroup; Testing; Therapeutic; Time; Tissues; Training; Work; base; cerebrovascular; comorbidity; demographics; disorder subtype; experience; experimental study; follow-up; hemodynamics; hypoperfusion; image processing; imaging biomarker; improved; innovation; insight; interest; intracranial artery; learning classifier; machine learning algorithm; machine learning method; magnetic resonance imaging biomarker; multimodality; novel; outcome prediction; predictive modeling; pressure; pressure sensor; prognostic significance; risk stratification; stroke patient; stroke risk; supervised learning; support vector machine; tool

The proposed study will be based on a multimodal approach using 4D flow MRI, perfusion-weighted MRI (PWI), diffusion-weighted MRI (DWI) and high-resolution vessel wall imaging (VWI) together with patient information (demographics and clinical factors) to predict the risk of recurrent stroke of patients with intracranial atherosclerotic disease (ICAD) stenosis. This will allow integrating the vulnerability of the stenosis as well as the patient by assessing the hemodynamic impact, plaque stability, and stroke lesion pattern together with patient information into a prediction model. PWI will provide tissue perfusion, VWI will provide plaque stability, DWI will provide stroke lesion pattern and 4D flow MRI will provide macroscopic hemodynamics of the circle of Willis (CoW). We will concentrate on the following innovative developments: 4D flow MRI: In order to allow 4D flow MRI scanning with a high dynamic velocity range (necessary to measure slow and fast velocities simultaneously), we recently developed dual-venc 4D flow MRI. However, this method suffers from extended scan tome of an already long acquisition. We, therefore, aim to minimize scan time for dual-venc 4D flow MRI scan while using the required spatial resolution and volume coverage, targeting 5-10 minutes so that this sequence can be added to clinical protocols. We aim to achieve this by integrating compressed sensing acceleration. Rigorous testing of the sequence will be done in phantom experiments as well as in a healthy test-retest control study. Data Analysis and Outcome Prediction: Currently, the multi-modal information that can be acquired with MRI has not been combined and used for comprehensive prediction of recurrent stroke risk in ICAD. Information that can be acquired from different MRI modalities may be critical in characterizing ICAD patient status. We will develop a new analysis tool that combines all data into a single network graph. All imaging data will be reported relative to supplying the intracranial artery of the CoW by using the vascular territory region of interest analysis. This will allow gathering all imaging parameters in a network graph. In a cross-sectional patient study, we will use combined data to see if it enables differentiation between healthy subjects, ICAD subgroups. Patient Study: In Aim 3, we will develop a machine-learning algorithm to predict which of the patients are at risk of experiencing a recurrent stroke. In order to achieve this, we will enroll a total of 150 ICAD patients from two institutions (Northwestern Memorial Hospital and San Francisco General Hospital). The combined data from the four different MR modalities and all other patient information will be used to identify only the discriminative features. This will be realized by using support vector machine recursive feature elimination to rank features associated with the risk of an ischemic event. The SVM will be trained and tested using information from the patient's clinical follow-up as outcome measure. The outcome (ischemic event or death yes/no)) will enable the development of the SVM classifier to predict outcome.

拟定研究将基于多模式方法，使用4D血流MRI、灌注加权MRI (PWI)、弥散加权MRI（DWI）和高分辨率血管壁成像（VWI）， 信息（人口统计学和临床因素），以预测颅内动脉瘤患者的复发性卒中风险 动脉粥样硬化性疾病（ICAD）狭窄。这将允许整合狭窄的脆弱性以及 通过评估血流动力学影响、斑块稳定性和卒中病变模式， 患者信息转化为预测模型。PWI提供组织灌注，VWI提供斑块稳定性， DWI将提供卒中病变模式，而4D flow MRI将提供环的宏观血流动力学。 威利斯（CoW）我们将专注于以下创新发展： 4D flow MRI：为了允许4D flow MRI扫描具有高动态速度范围（测量所需） 慢和快速度同时），我们最近开发了双静脉4D流动MRI。但这种方法 遭受已经很长的采集的延长扫描托姆。因此，我们的目标是尽量减少扫描时间， 双腔4D流动MRI扫描，同时使用所需的空间分辨率和体积覆盖范围，靶向5-10 分钟，以便将该序列添加到临床方案中。我们的目标是通过整合 压缩感知加速度将在体模实验中对序列进行严格测试， 以及健康的重测对照研究。 数据分析和结果预测：目前，MRI可以获得的多模态信息 尚未联合用于ICAD患者复发性卒中风险的综合预测。信息 可以从不同的MRI模态中获取的信息在表征ICAD患者状态中可能是至关重要的。我们将 开发一种新的分析工具，将所有数据合并到一个网络图中。所有成像数据将 通过使用感兴趣的血管区域，报告了与CoW颅内动脉供血相关的情况 分析.这将允许在网络图中收集所有成像参数。在一项横断面患者研究中， 我们将使用组合数据来观察它是否能够区分健康受试者、ICAD亚组。 患者研究：在目标3中，我们将开发一种机器学习算法来预测哪些患者有风险 中风复发的风险为了实现这一目标，我们将从两个国家共招募150名ICAD患者， 西北纪念医院和旧金山弗朗西斯科。的合并数据 四种不同的MR模态和所有其他患者信息将仅用于识别 功能.这将通过使用支持向量机递归特征消除对特征进行排序来实现 与缺血性事件的风险相关。SVM将使用来自 患者的临床随访作为结局指标。结局（缺血性事件或死亡是/否）将使 开发SVM分类器来预测结果。