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arXiv AI By David Br\"uggemann, Ekaterina Krymova, Firat \"Ozdemir, Jochen von Spiczak, Sebastian Kozerke, Samia Mora, Robert Manka, Mathieu Salzmann, Olga V. Demler

A latent ODE approach to full-cycle biventricular cardiac motion modeling from cine MRI

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The paper introduces a latent dynamical model that captures full‑cycle biventricular cardiac motion from cine MRI using a heart‑rate‑aware neural ODE and a graph‑based mesh autoencoder. It encodes ventricular anatomy and motion as a continuous latent trajectory, with a covariate‑conditioned prior for the end‑diastolic state and a Cox model linking deviations to incident heart failure. In a large UK Biobank cohort, adding the latent score to existing risk equations improved predictive performance, outperforming several established cardiac markers and alternative modeling approaches.

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