# Functional Cardiovascular 4D MRI in Congenital Heart Disease

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2021 · $709,759

## Abstract

SUMMARY / ABSTRACT
Congenital heart disease (CHD) is the most common birth defect, affecting 1.2% of all live births. Imaging
plays a major role in managing CHD but remains challenging for evaluating complex cardiac and vascular
abnormalities across a wide range of age and habitus. To address these limitations, the PIs have developed
cardiovascular 4D flow MRI which can measure complex 3D blood flow in-vivo, a task difficult or impossible to
obtain with other imaging strategies. Recent efforts have focused on two forms of CHD: 1) bicuspid aortic valve
(BAV) which is the most common form of CHD, and 2) single ventricle physiology (SVP), one of the most
severe forms of CHD. Our 4D flow MRI studies have successfully identified new hemodynamic biomarkers to
better characterize CHD. We were the first to establish a physiologic link between aberrant 3D blood flow,
elevated wall shear stress (WSS), aortopathy phenotype, and aortic wall tissue degeneration on histopathology
in patients with BAV. In patients with SVP, our findings demonstrated relationships between surgical correction
strategies and flow distribution to the lungs, a known factor implicated in SVP outcome. We have achieved
successful clinical translation at Northwestern, where 4D flow MRI is now used as a clinical tool in diagnostic
MRI exams for patients with CHD and aortic disease. Over the past four years, the PIs have assembled one of
the largest 4D MRI databases with over 2500 patient exams.
For this renewal application, we identified a need to increase the dynamic range of 4D MRI flow sensitivity to
account for data complexity (3D + time) and the wide age range in CHD by a combination of dual-venc flow
encoding, compressed sensing, and SSFP imaging. Second, three is a need for longitudinal studies to identify
predictors of BAV and SVP outcome. Third, making these unique but complex 4D MRI data sets and analysis
tools more widely available to the greater research community is challenging. In addition, no automated
methods currently exist for advanced processing such as atlas based analysis across large cohorts. Analysis is
thus time consuming and requires manual interactions (e.g. 3D vessel segmentation) which limits
reproducibility and translation. To address this need, an established Northwestern data archival and pipeline
processing resource based on remote high-performance computing clusters (NUNDA) will be utilized for
standardized data archival, sharing, and pipeline processing of 4D MRI data. This platform will provide the
unique opportunity to utilize annotated data available in the 4D MRI database (>1300 BAV, SVP, and control
4D MRI data analyzed in the initial funding cycle) for application of machine learning concepts to establish
(semi-)automated 4D MRI analysis workflows in NUNDA. Thus, the renewal application for this study aims to
1) develop a rapid (15 min) non-contrast 4D MRI for clinical translation, 2) leverage the existing large 4D MRI
database to identify 4D MRI m...

## Key facts

- **NIH application ID:** 10117035
- **Project number:** 5R01HL115828-09
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Michael Markl
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $709,759
- **Award type:** 5
- **Project period:** 2012-09-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10117035

## Citation

> US National Institutes of Health, RePORTER application 10117035, Functional Cardiovascular 4D MRI in Congenital Heart Disease (5R01HL115828-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10117035. Licensed CC0.

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