TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
This addresses CT reconstruction challenges for medical imaging, but it is incremental as it builds on existing 3D Gaussian Splatting adaptations.
The paper tackled the problem of severe artifacts in computed tomography (CT) reconstruction under highly sparse-view projections and dynamic motions by proposing TG-Field, a geometry-aware Gaussian deformation framework, which achieved state-of-the-art reconstruction accuracy in experiments on synthetic and real-world datasets.
3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.