Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis, which conveniently captures divergent anatomical variations across different types and represents meaningful intermediate CHD states. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.

翻译：先天性心脏病（CHD）涵盖了一系列心血管结构异常，通常需要为患者个体制定定制化治疗方案。对这些独特心脏解剖结构的计算建模与分析可提升诊断与治疗规划水平，并可能最终改善预后。深度学习方法已展现出自动化正常心脏解剖结构患者的心脏分割与网格构建以支持高效治疗规划的潜力。然而，CHD病例往往较为罕见，获取足够大规模的患者队列来训练此类深度学习模型存在挑战。心脏解剖结构的生成式建模有望通过生成虚拟队列填补这一空白；然而，现有方法主要针对正常解剖结构设计，难以直接捕捉CHD患者中显著的拓扑变异。为此，我们提出一种适用于捕捉不同CHD类型中多种心脏解剖结构的类型-形状解耦生成方法，能合成保留特定CHD类型独特拓扑结构的差异化形状心脏解剖结构。本深度学习模型基于CHD类型诊断隐式采用有符号距离场表示CHD类型特异性异常的通用全心脏解剖结构，可便捷地捕捉不同类型间的解剖变异差异，并表征有意义的中间CHD状态。为捕捉形状特异性变异，我们进一步学习可逆形变以对学习到的CHD类型特异性解剖结构进行变形，重建患者特异性形状。该方法有望为罕见CHD类型的心脏分割任务增广图像-分割掩膜对，并生成用于计算模拟的CHD心脏网格队列。