发表机构
Delft University of Technology; Hospital of the University of Pennsylvania; Ghent University; University of Pennsylvania（代尔夫特理工大学; 宾夕法尼亚大学医院; 根特大学; 宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出CAN-FLOW框架，基于归一化流生成元数据条件化的双心室解剖结构，在英国生物银行数据集上验证其生成的解剖结构更贴合临床表型分布，可用于构建虚拟队列与计算机模拟临床试验。
AI 中文摘要
心脏数字孪生研究正从针对特定个体的解剖结构复制品向代表临床相关人群亚组的虚拟队列发展，但队列规模有限、亚组稀疏性及数据共享约束导致难以获取具有代表性的成像衍生解剖数据集。条件生成模型可填补这一缺口，但虚拟队列需保留符合真实临床表型且依赖元数据的解剖变异性才具实用价值。现有心脏解剖结构生成器主要依赖条件变分自编码器（cVAE），其通过共享正则化潜在先验耦合表示学习与元数据条件化。本文提出CAN-FLOW，一种基于归一化流的两步条件解剖结构生成框架：首先学习微分同胚心脏形状动量的纯几何潜在表示，再用条件归一化流建模其性别、年龄、体重指数依赖的分布。我们在2208名英国生物银行健康受试者上训练CAN-FLOW，并与不同正则化强度的cVAE对比，结果显示CAN-FLOW生成的合理随机双心室解剖结构能更好复现临床表型分布、元数据依赖趋势、亚组变异性、点云覆盖及高维形状变异性。综上，这些结果证实CAN-FLOW是一种可共享的框架，可生成真实、随机变化、元数据条件化的双心室解剖结构，用于虚拟队列构建与计算机模拟临床试验工作流。
英文摘要
Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population subgroups. Yet access to representative imaging-derived anatomy datasets remains limited by cohort size, subgroup sparsity, and data-sharing constraints. Conditional generative models could help address this gap, but virtual cohorts are useful only if they preserve realistic, metadata-dependent anatomical variability. Existing cardiac anatomy generators largely rely on conditional variational autoencoders (cVAEs), which couple representation learning and metadata conditioning through a shared regularized latent prior. We introduce CAN-FLOW, a two-step Conditional ANatomy generation framework based on normalizing FLOWs that first learns geometry-only latent representations of diffeomorphic cardiac shape momenta and then models their sex-, age-, and body-mass-index-dependent distribution with a conditional normalizing flow. We trained CAN-FLOW on 2,208 healthy UK Biobank subjects and compared it with cVAEs across regularization strengths. CAN-FLOW generated plausible stochastic biventricular anatomies that better reproduced clinical phenotype distributions, metadata-dependent trends, subgroup variability, point-cloud coverage, and high-dimensional shape variability. Together, these results establish CAN-FLOW as a shareable framework for generating realistic, stochastically varying, metadata-conditioned biventricular anatomies for virtual cohort construction and in silico clinical trial workflows.
