Smart Cardiac Magnetic Resonance Delivering One-Click and Comprehensive Assessment of Cardiovascular Diseases

Introduction

Cardiovascular disease (CVD) causes at least 1.8 million European deaths annually, exceeding fatalities from cancer, chronic respiratory disease, and diabetes. Consequently, the fight against CVD has become the main priority of the World Health Organization.

Current Challenges

In the pursuit of understanding and treating CVD, cardiac magnetic resonance imaging (CMR) has remained the only modality capable of providing a comprehensive assessment of the heart's function and structure without harmful radiation. Unfortunately, current CMR systems remain too slow, too complex, require highly trained specialists and, as such, have presented a barrier to a wider adoption of CMR.

Project Aim

The aim of my ERC project is to unleash the full potential of CMR to transform patient trajectories by introducing a fast, one-click, fully automated, and comprehensive imaging pipeline applicable to diagnosis, prognosis, and therapy selection in cardiology.

Methodology

This aim will be achieved by:

1. Creating a novel imaging technology that collects CMR data in a single continuous free-breathing scan, taking into account post-processing requirements at the very origin of CMR sequence design.
2. Exploiting the unique contrasts generated by this technology to automatically extract quantitative markers on cardiac anatomy, function, and tissue characteristics.
3. Translating this transformative technology from a pre-clinical to a clinical setting.

Expected Outcomes

This will be the first-ever integrated cardiac imaging pipeline in which CMR images are acquired in a single click, jointly represented in a single volume, and automatically analyzed. This will unlock obstacles for broader acceptance of CMR and unleash the full potential of CMR to maximize its impact on patient trajectories.

The results of this project will pave the way towards robust image-based strategies for personalized patient care, including diagnosis, risk stratification, therapy selection, monitoring, and image-guided interventions.