标题： 基于网格潜在扩散模型的三维心脏解剖生成 显示英文标题

标题： 3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models

摘要： 扩散模型最近因其生成能力而受到广泛关注，特别是合成数据的高质量和多样性。 然而，它们在三维医学成像中的应用实例仍然很少，尤其是在心脏病学领域。 生成多样且逼真的心脏解剖结构对于体外试验、电机械计算机模拟或机器学习模型的数据增强等应用至关重要。 在本工作中，我们研究了潜在扩散模型（LDMs）在生成人类心脏解剖三维网格中的应用。 为此，我们提出了一种新的LDM架构——MeshLDM。 我们将所提出的模型应用于来自急性心肌梗死患者左心室心脏解剖的三维网格数据集，并根据定性和定量的临床及三维网格重建指标评估其性能。 所提出的MeshLDM成功捕捉了心脏形状在舒张期（放松）和收缩期（收缩）的特点，生成的网格与金标准相比，群体均值差异为2.4%。 显示英文摘要

摘要： Diffusion models have recently gained immense interest for their generative capabilities, specifically the high quality and diversity of the synthesized data. However, examples of their applications in 3D medical imaging are still scarce, especially in cardiology. Generating diverse realistic cardiac anatomies is crucial for applications such as in silico trials, electromechanical computer simulations, or data augmentations for machine learning models. In this work, we investigate the application of Latent Diffusion Models (LDMs) for generating 3D meshes of human cardiac anatomies. To this end, we propose a novel LDM architecture -- MeshLDM. We apply the proposed model on a dataset of 3D meshes of left ventricular cardiac anatomies from patients with acute myocardial infarction and evaluate its performance in terms of both qualitative and quantitative clinical and 3D mesh reconstruction metrics. The proposed MeshLDM successfully captures characteristics of the cardiac shapes at end-diastolic (relaxation) and end-systolic (contraction) cardiac phases, generating meshes with a 2.4% difference in population mean compared to the gold standard.