Open Access

ARTICLE

A Novel Atlas-Based Strategy for Understanding Cardiac Dysfunction in Patients with Congenital Heart Disease

by Sara Salehyar, Nickolas Forsch, Kathleen Gilbert, Alistair A. Young, James C. Perry, Sanjeet Hegde, Jeffrey H. Omens, Andrew D. McCulloch

1 Department of Bioengineering, University of California San Diego, La Jolla, USA.
2 Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
3 Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
4 Department of Biomedical Engineering, King’s College London, United Kingdom.
5 Department of Pediatrics, University of California San Diego, La Jolla, USA.
6 Department of Medicine, University of California San Diego, La Jolla, USA.
† These authors contributed equally to this work.

* Corresponding Author: Nickolas Forsch. Email: .

Molecular & Cellular Biomechanics 2019, 16(3), 179-183. https://doi.org/10.32604/mcb.2019.07384

Abstract

Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease. Infants diagnosed with TOF require surgical interventions to survive into adulthood. However, as a result of postoperative structural malformations and long-term ventricular remodeling, further interventions are often required later in life. To help identify those at risk of disease progression, serial cardiac magnetic resonance (CMR) imaging is used to monitor these patients. However, most of the detailed information on cardiac shape and biomechanics contained in these large four-dimensional (4D) data sets goes unused in clinical practice for lack of efficient and comprehensive quantitative analysis tools. While current global metrics of cardiac size and function, such as indexed ventricular mass and volumes, can identify patients at risk of further complications, they are not adequate to explain the underlying mechanisms causing the postoperative malfunctions, and help cardiologists plan optimal personalized treatments. We are proposing a novel approach that uses 4D ventricular shape models derived from CMR imaging exams to generate statistical atlases of ventricular shape and finite-element models of ventricular biomechanics to identify specific features of cardiac shape and biomechanical properties that explain variations in ventricular function. This study has the potential to discover novel biomarkers that precede adverse ventricular remodeling and dysfunction.

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