Non-invasive Evaluation of Intracranial Atherosclerotic Disease Using Hemodynamic Biomarkers

The proposed study will be based on new quantitative measures to predict the risk of recurrent stroke of patients with intracranial atherosclerotic disease (ICAD) stenosis by integrating the hemodynamic impact, plaque stability and stroke lesion pattern together with patient demographics and clinical factors into a prediction model. We will use perfusion MRI to evaluate tissue perfusion, vessel wall imaging to evaluate plaque stability, diffusion-weighted imaging to assess the stroke lesion patterns as well as 4D flow MRI to look at macroscopic blood flow of the circle of Willis (CoW). We aim to bring all our previously developed methods together and tailor these specifically to the application in patients with ICAD stenosis. 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: Information that can be acquired from different MRI modalities may be critical in characterizing ICAD patient status. Our state-of-the-art multi-modal MR protocol will be used to assess cerebral hypoperfusion, plaque vulnerability and DWI lesion location, in combination with standard risk factors (e.g. race, age, gender, body mass index (BMI), hypertension, diabetes, cholesterol). 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. 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 approach. This will allow gathering all imaging parameters/features in a network graph, an extended version of the flow distribution network graph (FDNG), which we previously have developed. In a cross-sectional patient study, we will use combined data to detect differences between healthy subjects, as well as moderate and severe ICAD patients. Patient Study: In Aim 3, we will develop a machine-learning algorithm to predict, which 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 measures from the patient’s chart 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. A supervised learning classifier (such as a Support Vector Machine) will be trained and tested on the baseline imaging data, clinical factors as well as demographic data using information from the patient’s clinical follow-up as outcome measure. These outcome measures (acquired from clinical follow-up visits within 1-year afte