A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics
<|reference_start|>A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics: Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, that are influenced by demographic, anthropometric and disease factors. Unravelling the normal patterns of shape and motion, as well as understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalised treatment strategies. To this end, we developed a novel conditional generative model, MeshHeart, to learn the distribution of cardiac shape and motion patterns. MeshHeart is capable of generating 3D+t cardiac mesh sequences, taking into account clinical factors such as age, sex, weight and height. To model the high-dimensional and complex spatio-temporal mesh data, MeshHeart employs a geometric encoder to represent cardiac meshes in a latent space, followed by a temporal Transformer to model the motion dynamics of latent representations. Based on MeshHeart, we investigate the latent space of 3D+t cardiac mesh sequences and propose a novel distance metric termed latent delta, which quantifies the deviation of a real heart from its personalised normative pattern in the latent space. In experiments using a large dataset of 38,309 subjects, MeshHeart demonstrates a high performance in cardiac mesh sequence reconstruction and generation. Features defined in the latent space are highly discriminative for cardiac disease classification, whereas the latent delta exhibits strong correlation with clinical phenotypes in phenome-wide association studies. The codes and models of this study will be released to benefit further research on digital heart modelling.<|reference_end|>
@article{qiao2024a,
    title={A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart
  Dynamics},
    author={Mengyun Qiao, Kathryn A McGurk, Shuo Wang, Paul M. Matthews, Declan P
  O Regan, Wenjia Bai},
    journal={arXiv preprint arXiv:2409.13825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13825},
    primaryClass={cs.AI}
}