4-D Reconstruction of Fetal Left Ventricle From Echocardiography via 2.5-D Radial Segmentation and Graph-Fourier...

4D (3D over time) fetal heart reconstruction improves detection and functional assessment of congenital malformations compared with 2D methods, but remains challenging due to the lack of publicly available 4D echocardiography datasets, the burden of full 3D/4D annotations, and the computational cost of volumetric networks. To address these challenges, we introduce a 2.5D radial-slicing paradigm that converts 3D volumes into a set of angularly structured long-axis slices, inducing a consistent U-shaped anatomical appearance that facilitates manual annotation and introduces an acquisition-induced angular symmetry as an effective inductive bias. Based on this slicing, we construct FeEcho4D, the first public benchmark for 4D fetal echocardiography (52 subjects, 1845 annotated volumes). Building on this inductive bias, we propose SCOPE-Net, a symmetry-consistent, prompt-enhanced segmentation network that encodes radial symmetry via novel learnable Flip-Consistent Radial Attention and Symmetry-induced Self-distillation through inter-slice augmentation invariance, enabling label-free representation-level self-supervision. Sparse radial segmentations are subsequently reconstructed into temporally coherent 3D meshes using graph-harmonic deformation, providing a geometry-aware alternative to volumetric segmentation and voxel-based surface extraction without requiring dense 3D annotations. Extensive experiments on FeEcho4D and the public MITEA dataset show that radial segmentation outperforms standard short-, long-axis, and volumetric views, while SCOPE-Net yields anatomically plausible 3D meshes over time. Our framework achieves an 88.8% correlation in ejection fraction, surpassing 3D volumetric segmentation (81.9%) and conventional 2D approaches (64.4%). These results indicate that geometry-aware 2.5D learning can outperform fully volumetric models for 4D fetal cardiac analysis, enabling accurate, efficient, and high-fidelity functional assessment without the need for dense 3D annotations. The FeEcho4D dataset and associated resources are publicly available at https://feecho4d.github.io/Website/.