Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis
It addresses the need for realistic virtual populations of dynamic cardiac anatomies for in-silico medical device trials, offering controllable synthesis with preserved clinical indices.
The paper proposes 4D F-MeshLDM, a conditional generative framework for synthesizing 3D+t cardiac mesh sequences from clinical covariates, achieving near-zero cycle closure error and outperforming state-of-the-art baselines in anatomical fidelity on 5,000 UK Biobank subjects.
In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t mesh sampled from a generative model. However, most existing mesh generators focus on static anatomy, while sequence models often lack explicit periodicity. To this end, we propose 4D F-MeshLDM, a conditional generative framework comprising a convolutional mesh VAE to encode meshes, a structural latent space that parameterises motion using a truncated Fourier series, and a diffusion prior that learns the latent distribution over Fourier coefficient tokens. By conditioning the diffusion process on clinical covariates via affine modulation, we enable controllable synthesis. Sampling tokens and performing inverse Fourier synthesis yield cycle-consistent latent trajectories, which can be decoded into 3D+t cardiac mesh sequences. Experiments on 5,000 UK Biobank subjects demonstrate that 4D F-MeshLDM outperforms state-of-the-art baselines in anatomical fidelity and achieves near-zero cycle closure error. Furthermore, the generated cohorts accurately preserve clinical functional indices, highlighting the potential of our framework for reliable in-silico cardiac trials.