This study is a joint NIH funded research study together with Northwestern University and University of California San Francisco. 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 time 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. 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. 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 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. The outcome (ischemic event or death yes/no)) will enable the development of the SVM classifier to predict outcome.

Primary contact: Susanne Schnell (PhD)
Investigators: Susanne Schnell (PhD), TBD
Collaborations: Northwestern University (Michael Markl, Sameer Ansari,Ann Ragin, Maria Aristova), UCSF (David Saloner, Jared Narvid)
Funding: NIH 1R01HL149787