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: 10471925
• 负责人: Sameer A Ansari
• 金额: $ 45.77万
• 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/10471925
• 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; 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; 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; disease prognosis; 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; 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; 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; 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; disease prognosis; 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.

拟议的研究将基于使用4D流MRI，灌注加权MRI的多模式方法 （PWI），扩散加权MRI（DWI）和高分辨率容器壁成像（VWI）以及患者 信息（人口统计学和临床​​因素），以预测颅内患者复发性中风的风险 动脉粥样硬化疾病（ICAD）狭窄。这将允许整合狭窄的脆弱性以及 通过评估血液动力学影响，斑块稳定性和中风病变模式以及 患者信息进入预测模型。 PWI将提供组织灌注，VWI将提供斑块稳定性， DWI将提供中风病变模式，4D流MRI将为圆圈提供宏观的血流动力学 威利斯（牛）。我们将专注于以下创新的发展： 4D流MRI：为了允许具有高动态速度范围的4D流MRI扫描 同时缓慢且快速的速度），我们最近开发了双元素4D流MRI。但是，此方法 遭受了已经长期收购的延长扫描tome。因此，我们的目标是最大程度地减少扫描时间 使用所需的空间分辨率和体积覆盖范围，双向4D流MRI扫描目标为5-10 分钟，以便可以将此序列添加到临床方案中。我们旨在通过整合来实现这一目标 压缩传感加速度。序列的严格测试将在幻影实验中进行 以及在健康的重测控制研究中。 数据分析和结果预测：目前，可以使用MRI获取的多模式信息 尚未合并并用于全面预测ICAD中的中风风险。信息 可以从不同的MRI模式中获取，这对于表征ICAD患者身份的表征至关重要。我们将 开发一个新的分析工具，将所有数据结合到单个网络图中。所有成像数据将是 报告的相对于使用感兴趣的血管区域提供的牛的颅内动脉 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 我们将使用合并的数据来查看它是否可以区分健康受试者，ICAD子组。 患者研究：在AIM 3中，我们将开发一种机器学习算法，以预测哪些患者有风险 经历了反复的中风。为了实现这一目标，我们将总共招收来自两个的ICAD患者 机构（西北纪念医院和旧金山综合医院）。来自 四种不同的MR模式和所有其他患者信息将仅用于识别歧视性 特征。这将通过使用支持向量机递归功能消除来进行排名特征来实现这一点 与缺血事件的风险相关。 SVM将使用来自 患者的临床随访作为结果度量。结果（缺血事件或死亡是/否））将使 SVM分类器的开发以预测结果。