3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation in both efficiency and quality. Recent adaptations of Gaussian splatting specifically tailored for computed tomography (CT) have shown promising results but still suffer from severe artifacts under highly sparse-view X-ray conditions and lack robustness in dynamic scenarios. To tackle these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored specifically for sparse-view and dynamic CT reconstruction. A hash encoder is introduced to explicitly capture spatial geometric relationships among Gaussian primitives, significantly regularizing their spatial distribution under ultra-sparse conditions. We further extend this framework to dynamic reconstruction by introducing time-conditioned representations. To alleviate hash collisions and temporal inconsistencies caused by joint spatiotemporal encoding, a spatiotemporal attention module is proposed, which adaptively recalibrates and optimizes Gaussian features across frames. Moreover, we incorporate a motion-flow network to model fine-grained respiratory motion, enabling accurate tracking of 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. We will release source codes.