Cardiac Magnetic Resonance (CMR) imaging is widely used for heart model reconstruction and digital twin computational analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. In this work, we introduce MorphiNet, a novel network that reproduces heart anatomy learned from high-resolution Computed Tomography (CT) images, unpaired with CMR images. MorphiNet encodes the anatomical structure as gradient fields, deforming template meshes into patient-specific geometries. A multilayer graph subdivision network refines these geometries while maintaining a dense point correspondence, suitable for computational analysis. MorphiNet achieved state-of-the-art bi-ventricular myocardium reconstruction on CMR patients with tetralogy of Fallot with 0.3 higher Dice score and 2.6 lower Hausdorff distance compared to the best existing template-based methods. While matching the anatomical fidelity of comparable neural implicit function methods, MorphiNet delivered 50$\times$ faster inference. Cross-dataset validation on the Automated Cardiac Diagnosis Challenge confirmed robust generalization, achieving a 0.7 Dice score with 30\% improvement over previous template-based approaches. We validate our anatomical learning approach through the successful restoration of missing cardiac structures and demonstrate significant improvement over standard Loop subdivision. Motion tracking experiments further confirm MorphiNet's capability for cardiac function analysis, including accurate ejection fraction calculation that correctly identifies myocardial dysfunction in tetralogy of Fallot patients.

翻译：心脏磁共振成像因其能够可视化软组织并捕捉动态功能，被广泛用于心脏模型重建和数字孪生计算分析。然而，心脏磁共振图像具有各向异性的特点，表现为较大的层间距离以及心脏运动导致的错位。这些限制导致数据丢失和测量不准确，阻碍了对详细解剖结构的捕捉。在本工作中，我们提出了MorphiNet，这是一种新颖的网络，能够复现从高分辨率计算机断层扫描图像中学习到的心脏解剖结构，且无需与心脏磁共振图像配对。MorphiNet将解剖结构编码为梯度场，将模板网格变形为患者特定的几何形状。多层图细分网络在保持密集点对应关系的同时细化这些几何形状，适用于计算分析。在法洛四联症患者的心脏磁共振数据上，MorphiNet实现了最先进的双心室心肌重建，与现有最佳的基于模板的方法相比，Dice分数高出0.3，Hausdorff距离低2.6。在达到可比神经隐式函数方法解剖保真度的同时，MorphiNet的推理速度快50倍。在自动化心脏诊断挑战数据集上的跨数据集验证证实了其强大的泛化能力，获得了0.7的Dice分数，较之前的基于模板的方法提升了30%。我们通过成功恢复缺失的心脏结构验证了我们的解剖学习方法，并证明了其相对于标准Loop细分的显著改进。运动追踪实验进一步证实了MorphiNet用于心脏功能分析的能力，包括准确计算射血分数，从而正确识别法洛四联症患者的心肌功能障碍。