# Non-invasive Evaluation of Intracranial Atherosclerotic Disease Using Hemodynamic Biomarkers

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2021 · $490,343

## Abstract

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-...

## Key facts

- **NIH application ID:** 10248545
- **Project number:** 5R01HL149787-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Sameer A Ansari
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $490,343
- **Award type:** 5
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10248545

## Citation

> US National Institutes of Health, RePORTER application 10248545, Non-invasive Evaluation of Intracranial Atherosclerotic Disease Using Hemodynamic Biomarkers (5R01HL149787-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10248545. Licensed CC0.

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