Non-invasive Evaluation of Intracranial Atherosclerotic Disease Using Hemodynamic Biomarkers

这项拟议的研究将基于一种多模式方法，使用4D Flow MRI、灌注加权MRI (PWI)、磁共振弥散加权成像(DWI)和高分辨率血管壁成像(VWI)与患者 预测颅内中风复发风险的信息(人口学和临床因素) 动脉粥样硬化病(ICAD)狭窄。这将允许集成狭窄的易损性以及 患者通过评估血流动力学影响、斑块稳定性和卒中病变类型 将患者信息转换为预测模型。PWI将提供组织灌流，VWI将提供斑块稳定性， DWI将提供卒中病变模式，4D Flow MRI将提供脑血管循环的宏观血流动力学。 威利斯(奶牛)。我们将重点抓好以下创新发展： 4D Flow MRI：为了实现高动态速度范围(测量所必需的)的4D Flow MRI扫描 我们最近开发了Dual Venc 4D Flow MRI。然而，这种方法 遭受了一笔已经很长时间的收购的延长扫描。因此，我们的目标是将扫描时间降至最低 Dual Venc 4D Flow MRI扫描，同时使用所需的空间分辨率和体积覆盖，目标为5-10 几分钟，这样这个序列就可以被添加到临床方案中。我们的目标是通过整合 压缩传感加速。序列的严格测试将在模体实验中进行，如 在一个健康的重测对照研究中也是如此。 数据分析和结果预测：目前，MRI可以获取的多模式信息 还没有被结合起来用于ICAD复发中风风险的综合预测。信息 可以从不同的MRI模式获得的信息可能是表征ICAD患者状态的关键。我们会 开发一种新的分析工具，将所有数据合并到单个网络图中。所有成像数据都将 关于利用血管区域感兴趣区域供应奶牛的颅内动脉的报道 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查看它是否能够区分健康受试者和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 有反复中风的危险。为了实现这一目标，我们将从两个国家招募150名ICAD患者 机构(西北纪念医院和旧金山总医院)。来自的组合数据 四种不同的MR模式和所有其他患者信息将仅用于识别有区别的患者 功能。这将通过使用支持向量机递归特征消除来对要素进行排序来实现 与缺血事件的风险相关。支持向量机将使用来自 以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 开发用于预测结果的支持向量机分类器。

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.