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Abstract
We report a comprehensive range of structural and functional phenotypes for the heart and aorta, quantified from cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine learning-based analysis pipeline. We explore the variations of these phenotypes with sex, age, major cardiovascular risk factors and other non-imaging phenotypes across 26,893 participants. Our study illustrates how population-based cardiac and aortic imaging phenotypes could be used to better define cardiovascular risks and heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.