| Title: | Towards cardiac MRI foundation models: Comprehensive visual-tabular representations for whole-heart assessment and beyond |
| Journal: | Medical Image Analysis |
| Published: | 11 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40819587/ |
| DOI: | https://doi.org/10.1016/j.media.2025.103756 |
Publication 19130
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Abstract
Cardiac magnetic resonance (CMR) imaging is the gold standard for non-invasive cardiac assessment, offering rich spatio-temporal views of the heart's anatomy and physiology. Patient-level health factors, such as demographics, metabolic, and lifestyle, are known to substantially influence cardiovascular health and disease risk, yet remain uncaptured by CMR alone. To holistically understand cardiac health and to enable the best possible interpretation of an individual's disease risk, CMR and patient-level factors must be jointly exploited within an integrated framework. Recent multi-modal approaches have begun to bridge this gap, yet they often rely on limited spatio-temporal data and focus on isolated clinical tasks, thereby hindering the development of a comprehensive representation for cardiac/health evaluation. To overcome these limitations, we introduce ViTa, a step toward foundation models that delivers a comprehensive representation of the heart and a precise interpretation of individual disease risk. Leveraging data from 42,000 UK Biobank participants, ViTa integrates 3D+T cine stacks from short-axis and long-axis views, enabling a complete capture of the cardiac cycle. These imaging data are then fused with detailed tabular patient-level factors, enabling context-aware insights. This multi-modal paradigm supports a wide spectrum of downstream tasks, including cardiac phenotype and physiological feature prediction, segmentation, and classification of cardiac/metabolic diseases within a single unified framework. By learning a shared latent representation that bridges rich imaging features and patient context, ViTa moves beyond traditional, task-specific models toward a universal, patient-specific understanding of cardiac health, highlighting its potential to advance clinical utility and scalability in cardiac analysis. 2.</p>
7 Authors
- Yundi Zhang
- Paul Hager
- Che Liu
- Suprosanna Shit
- Chen Chen
- Daniel Rueckert
- Jiazhen Pan
1 Application
| Application ID | Title |
|---|---|
| 87802 | Assessment of frailty and biological age using multi-modal deep learning |
Enabling scientific discoveries that improve human health