We develop image analysis algorithms for whole-heart cardiac MRI in patients with severe congenital heart disease.

We develop computer vision and machine learning methods to segment and interpret cardiac magnetic resonance images (MRI) for patients with congenital heart disease (CHD). Our aim is to support surgical planning and simulation. Our research focuses on overcoming the challenges of dramatic anatomical variability in the hearts of CHD patients.

The resulting patient-specific heart models can, for example, be 3D-printed to visualize each patient's individual cardiac anatomy, or be used for dynamic functional analysis of the cardiac cycle (i.e., ejection fraction).