用傅里叶运动建模生成可控制的心脏4D解剖序列。
Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis
- 用截断傅里叶级数参数化运动，构建结构化潜在空间。
- 在5000名英国生物银行受试者上实现近零循环闭合误差。
- 适合需要可控心脏解剖生成的虚拟临床试验场景。
医学器械的虚拟临床试验需生成解剖虚拟人群。心血管应用中，虚拟解剖通常以3D+t网格形式从生成模型采样。然而，现有网格生成器多聚焦静态解剖，序列模型常缺乏显式周期性。为此，我们提出4D F-MeshLDM，一种包含卷积网格变分自编码器（VAE）用于网格编码、基于截断傅里叶级数的结构化潜在空间参数化运动，以及学习傅里叶系数标记潜分布的扩散先验的条件生成框架。通过仿射调制对临床协变量进行条件控制，实现可调控合成。采样标记并执行逆傅里叶合成，生成循环一致的潜在轨迹，可解码为3D+t心脏网格序列。在5000名英国生物银行受试者上的实验表明，4D F-MeshLDM在解剖保真度上优于现有最优基线，并实现近零循环闭合误差。生成队列准确保留临床功能指标，凸显该框架在可靠虚拟心脏试验中的潜力。
原文摘要 · Abstract (English)
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.
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