Functional Cardiovascular 4D MRI in Congenital Heart Disease

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DESCRIPTION (provided by applicant): Congenital heart disease (CHD) represents the most common birth defect and affects approximately 1.2% of all live births and is the leading cause of birth defect-related deaths. CHD patients require frequent diagnostic testing in order to plan surgical repair, assess preoperative risk, and/or survey for important long-term complications. Bicuspid aortic valve (BAV), one of the most common forms of CHD, and single ventricle physiology (SVP), one of the most severe, are two patient cohorts which will require such longitudinal surveillance. Standard diagnostic tools, however, often involve invasive catheter-based procedures, ionizing radiation, and/or lengthy 60-90 minute Magnetic Resonance Imaging (MRI) exams, necessitating sedation or general anesthesia in pediatric patients. Recent studies suggest that general anesthesia could adversely affect neurologic, cognitive, and social development of neonates and young children. In addition, common outcome measures for CHD diseases such as BAV or SVP are coarse and rely on simplified parameters such as the diameter of the aortic root or the post-operative clinical status which do not reflect the underlyig mechanisms of disease progression. Further knowledge of which BAV and SVP patients are at risk for aortopathy or failing Fontan physiology would improve patient management and therapy planning by defining regular follow-up intervals and generating precise criteria for referral to surgical correction. Our goal is to develop and a new comprehensive 20-minute cardiovascular functional 4D MR exam that can replace the long standard MR imaging protocol and reduce or eliminate exposure to general anesthesia. Patient-specific post-hoc analysis will allow retrospective quantification of cardiac function and flow without limitation to predefined 2D scan planes. In addition, new hemodynamic biomarkers will be derived and evaluated for their potential as prognostic markers for improved outcome prediction in patients with BAV and SVP.