Functional Cardiovascular 4D MRI in Congenital Heart Disease

Project: Research project

Project Details


Congenital heart disease (CHD) is the most common birth defect, affecting approximately 1.2% of all live births. Imaging plays a major role in managing CHD patients but current diagnostic tests can be invasive, involve ionizing radiation, and require general anesthesia. 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. Studies have demonstrated that 4D flow MRI can be reliably performed in 10-15 minutes
and reduce exposure to general anesthesia in pediatric patients compared to standard cardiac MRI. In addition, 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. Based on these sustained efforts by the PIs during the initial funding period (2012-2017), successful clinical translation has been achieved. 4D flow MRI is now used as an IRB-approved clinical tool in diagnostic MRI exams at Northwestern for patients with CHD and aortic disease. Over the past four years, the PIs have assembled the largest 4D flow MRI database in the world with over 2200 patient exams and healthy controls across a wide range of ages.

However, 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 limit 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 (BAV and SVP 4D MRI data analyzed in the initial funding period) for application of machine learning concepts to establish (semi-)automated 4D MRI analysis workflows in NUNDA. In addition, existing methodological challenges include long scan times and limited blood-tissue contrast which often require Gadolinium (Gd)-contrast administration.

Thus, the renewal application for this study aims to 1) further develop improved non-contrast 4D MRI, 2) leverage the existing large 4D MRI database to identify 4D MRI metrics predictive of long-term (> 5 years) CHD patient outcome, and 3) establish a remote NUNDA platform for 4D MRI data sharing and automated analysis across large cohorts.
Effective start/end date4/1/183/31/22


  • National Heart, Lung, and Blood Institute (5R01HL115828-07)


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