arXiv 2412.109852026-01-25eess.IVcs.CV

MorphiNet：一种用于自适应双心室表面重建的图细分网络

MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction

School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）

Yu Deng, Yiyang Xu, Linglong Qian, Charlène Mauger, Anastasia Nasopoulou, Steven Williams, Michelle Williams, Steven Niederer, David Newby, Andrew McCulloch, Je… 展开作者

Yu Deng, Yiyang Xu, Linglong Qian, Charlène Mauger, Anastasia Nasopoulou, Steven Williams, Michelle Williams, Steven Niederer, David Newby, Andrew McCulloch, Jeff Omens, Kuberan Pushprajah, Alistair Young

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AI总结：

MorphiNet通过高分辨率CT图像学习，实现更精确、更快的双心室心肌重建，适用于心脏功能分析和数字双计算。

摘要：

心脏磁共振（CMR）成像因其能够可视化软组织和捕捉动态功能而被广泛用于心脏模型重建和数字双计算分析。然而，CMR图像具有各向异性特性，表现为切片间距离大且因心脏运动导致的对齐偏差。这些限制导致数据丢失和测量不准确，阻碍了详细解剖结构的捕捉。在本工作中，我们引入了MorphiNet，一种新的网络，该网络通过高分辨率计算机断层扫描（CT）图像学习来重建心脏解剖结构，且不与CMR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结构。一个多层图细分网络进一步细化这些几何结构，同时保持密集的点对应关系，适用于计算分析。MorphiNet在具有法洛四联症的CMR患者上实现了最先进的双心室心肌重建，其Dice评分比现有最佳模板方法高0.3，Hausdorff距离低2.6。尽管在解剖保真度上与可比的神经隐式函数方法相当，MorphiNet的推理速度比现有方法快50倍。在自动化心脏诊断挑战赛上的跨数据集验证证实了其强大的泛化能力，实现了0.7的Dice评分，比先前的模板方法提高了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，并展示了与标准循环细分相比的显著改进。运动跟踪实验进一步证实了MorphiNet在心脏功能分析中的能力，包括准确的射血分数计算，正确识别法洛四联症患者的 myocardial 功能障碍。

英文摘要：

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.

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