基于二维稀疏心脏磁共振成像的三维双心室心脏形状重建与建模的神经可变形模型
Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging
- Rutgers University（罗格斯大学）
- NVIDIA（英伟达）
- New York University School of Medicine（纽约大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出神经可变形模型（NDM），通过可学习的全局与局部变形从二维稀疏CMR数据重建三维双心室形状，实现点云稠密化、网格生成与形状配准，性能优于传统方法。
中文摘要 AI 辅助
我们提出了一种新颖的神经可变形模型（NDM），旨在从二维稀疏心脏磁共振（CMR）成像数据中重建和建模心脏的三维双心室形状。我们使用混合可变形超二次曲面来建模双心室形状，这些超二次曲面由一组几何参数函数参数化，并能够进行全局和局部变形。全局几何参数函数和变形从视觉数据中捕获整体形状特征，而局部变形（参数化为神经微分同胚点流）可以被学习以恢复详细的心脏形状。与常规可变形模型公式中使用的迭代优化方法不同，NDM可以被训练以从形状分布流形中学习这些几何参数函数、全局和局部变形。我们的NDM可以学习对任意尺度的稀疏心脏点云进行稠密化，并自动生成高质量的三角网格。它还使得在不同心脏形状实例之间隐式学习稠密对应关系成为可能，从而实现精确的心脏形状配准。此外，NDM的参数直观易懂，医生无需复杂的后处理即可使用。在大型CMR数据集上的实验结果表明，NDM相比传统方法具有更优的性能。
英文摘要
We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally. While global geometric parameter functions and deformations capture gross shape features from visual data, local deformations, parameterized as neural diffeomorphic point flows, can be learned to recover the detailed heart shape.Different from iterative optimization methods used in conventional deformable model formulations, NDMs can be trained to learn such geometric parameter functions, global and local deformations from a shape distribution manifold. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically. It also enables the implicit learning of dense correspondences among different heart shape instances for accurate cardiac shape registration. Furthermore, the parameters of NDM are intuitive, and can be used by a physician without sophisticated post-processing. Experimental results on a large CMR dataset demonstrate the improved performance of NDM over conventional methods.
