arXiv:2608. 19738v1 Announce Type: cross Abstract: Full-cycle biventricular geometry is essential for characterizing cardiac function.
arXiv:2610.09185v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electroc...
arXiv:2606. 03827v1 Announce Type: cross Abstract: In-silico trials of medical devices require the generation of virtual populations of anatomies.
arXiv:2610.09397v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame a...
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.
arXiv:2608. 09460v1 Announce Type: new Abstract: Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population subgroups.
arXiv:2606. 26718v1 Announce Type: new Abstract: 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.
Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation.
arXiv:2607. 02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.
arXiv:2606. 26764v1 Announce Type: cross Abstract: Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions.
The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.
arXiv:2606. 14759v1 Announce Type: cross Abstract: Cine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models.
arXiv:2609.08627v1 Announce Type: cross Abstract: In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, c...