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

基本信息

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
项目类别：
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

项目摘要

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)、弥散加权 MRI (DWI) 和高分辨率血管壁成像 (VWI) 与患者一起预测颅内脑卒中患者复发性卒中风险的信息（人口统计学和临床因素）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、斑块稳定性和中风病变模式以及将患者信息转化为预测模型。 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 血流 MRI 将提供中风循环的宏观血流动力学威利斯（CoW）。我们将重点关注以下创新发展： 4D 流 MRI：为了允许具有高动态速度范围的 4D 流 MRI 扫描（需要测量同时慢速和快速），我们最近开发了双 venc 4D 流 MRI。然而，这种方法遭受已经很长的采集的延长扫描时间的困扰。因此，我们的目标是尽量减少扫描时间双 venc 4D 流 MRI 扫描，同时使用所需的空间分辨率和体积覆盖范围，目标为 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来实现这一目标压缩传感加速度。将在模型实验中对序列进行严格的测试，如下所示以及健康的重测对照研究。数据分析和结果预测：目前，MRI可以获取的多模态信息尚未合并并用于 ICAD 中复发性卒中风险的综合预测。信息可以从不同 MRI 模式获得的信息可能对于表征 ICAD 患者状态至关重要。我们将开发一种新的分析工具，将所有数据组合成一个网络图。所有成像数据将报道与使用感兴趣的血管区域供应奶牛的颅内动脉有关分析。这将允许收集网络图中的所有成像参数。在一项横断面患者研究中，我们将使用组合数据来查看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面临风险经历复发性中风。为了实现这一目标，我们将招募来自两个国家的总共 150 名 ICAD 患者。机构（西北纪念医院和旧金山总医院）。综合数据来自四种不同的 MR 模式和所有其他患者信息将用于仅识别有区别的特征。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事件的风险相关。 SVM 将使用来自的信息进行训练和测试患者的临床随访作为结果衡量标准。结果（缺血事件或死亡是/否））将使开发 SVM 分类器来预测结果。