The Exciting Future of Cardiovascular Imaging.
Authors
Affiliations (4)
Affiliations (4)
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Department of Cardiology, Royal Brompton and Harefield Hospitals, Guys' and St Thomas' NHS Foundation Trust, London, England, United Kingdom; National Heart and Lung Institute, Imperial College London, London, England, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom.
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
- Department of Cardiology, Houston Methodist Hospital and The DeBakey Heart and Vascular Center, Houston, Texas, USA. Electronic address: [email protected].
Abstract
The field of cardiovascular imaging is entering an exciting era of accelerated innovation, fueled by advances in hardware, reconstruction algorithms, and artificial intelligence. Each imaging modality-echocardiography, computed tomography, cardiac magnetic resonance, and nuclear imaging-continues to evolve, with distinct strengths and constraints that complement one another. Collectively, these advances drive improved diagnostic accuracy, refine risk stratification, optimize therapy planning, and enable effective monitoring of treatment response. Artificial intelligence integration will increasingly support the entire imaging workflow, from acquisition planning and reconstruction to segmentation and quality control, facilitating faster, more reproducible, and quantitative imaging, and integrating imaging with multimodal clinical information to enable individualized management. Further, digital twins and physics-based simulation, powered by imaging-derived anatomy and tissue properties, provide mechanistic insight and a platform for scenario testing. This state-of-the-art review outlines key advances across all major modalities and explores how emerging technologies are driving the future of cardiovascular imaging toward data-driven predictive, personalized, and outcome-focused care.