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.

翻译：三维高斯泼溅（3DGS）以其卓越的效率和渲染质量，彻底改变了三维场景的表征方式。尽管近期其在计算机断层扫描（CT）领域的应用展现出潜力，但这些方法在处理高度稀疏视角投影和动态运动时，仍面临严重伪影的挑战。为解决这些问题，我们提出了断层扫描几何场（TG-Field），这是一个专为静态与动态CT重建设计的几何感知高斯形变框架。我们采用多分辨率哈希编码器来捕获局部空间先验，从而在超稀疏设置下对高斯基元参数进行正则化。我们进一步将该框架扩展至动态重建，通过引入时间条件表征和一个时空注意力模块来自适应地聚合特征，从而解决时空模糊性问题并增强时间一致性。此外，一个运动流网络被用于建模细粒度的呼吸运动，以追踪局部解剖结构的形变。在合成数据集和真实世界数据集上进行的大量实验表明，TG-Field始终优于现有方法，在高度稀疏视角条件下实现了最先进的重建精度。