A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incid...
| Authors | David Brüggemann, Ekaterina Krymova, Firat Özdemir et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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