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Understanding population-wide variability of the human heart is crucial to detect abnormalities and improve the assessment of both cardiac anatomy and function. While many computational modeling approaches have been developed to capture this variability separately for either cardiac anatomy or physiology, their complex interconnections have rarely been explored together. In this work, we propose a novel multi-modal variational autoencoder (VAE) capable of processing combined physiology and bitemporal anatomy information in the form of electrocardiograms (ECG) and 3D biventricular point clouds. Our method achieves high reconstruction accuracy on a UK Biobank dataset with Chamfer distances between predicted and input anatomies below the underlying image resolution and the ECG reconstructions outperforming a state-of-the-art benchmark approach specialized in ECG generation. We also evaluate its generative ability and find comparable populations of generated and gold standard anatomies, ECGs, and combined anatomy-ECG data in terms of common clinical metrics and maximum mean discrepancies.

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