用扩散模型生成逼真心脏3D网格，助力医学模拟与数据增强
3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
- 基于网格的潜空间扩散模型MeshLDM，直接生成心脏3D网格
- 生成结果与真实数据平均差异仅2.4%，还原收缩与舒张状态
- 适合心血管仿真、虚拟临床试验及医学影像数据增强
扩散模型因其生成数据的高质量和多样性而备受关注，但在3D医学影像，尤其是心脏病学中的应用仍较少。生成多样且真实的心脏解剖结构对计算机仿真、体外试验以及机器学习模型的数据增强至关重要。本文研究了潜空间扩散模型（LDM）在生成人类心脏解剖3D网格中的应用，提出一种新型架构MeshLDM。该模型在急性心肌梗死患者左心室3D网格数据集上进行训练，并通过定性和定量临床及3D网格重建指标评估性能。MeshLDM成功捕捉了心脏在舒张末期和收缩末期的形态特征，生成网格与金标准之间的群体均值差异仅为2.4%。
原文摘要 · Abstract (English)
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.
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