ArticleIEEE transactions on medical imaging2024
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: anFrontiers in cardiovascular medicine · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins.NPJ digital medicine · 2024Article
- TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.Medical image analysis · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data. The code and the trained generative model are available at https://github.com/MengyunQ/CHeart.
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Registered trials
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