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Sparse-view computed tomography (CT) reduces radiation exposure by acquiringfewer projections, making it a valuable tool in clinical scenarios wherelow-dose radiation is essential. However, this often results in increased noiseand artifacts due to limited data. In this paper we propose a novel 3D Gaussianrepresentation (3DGR) based method for sparse-view CT reconstruction. Inspiredby recent success in novel view synthesis driven by 3D Gaussian splatting, weleverage the efficiency and expressiveness of 3D Gaussian representation as analternative to implicit neural representation. To unleash the potential of 3DGRfor CT imaging scenario, we propose two key innovations: (i) FBP-image-guidedGuassian initialization and (ii) efficient integration with a differentiable CTprojector. Extensive experiments and ablations on diverse datasets demonstratethe proposed 3DGR-CT consistently outperforms state-of-the-art counterpartmethods, achieving higher reconstruction accuracy with faster convergence.Furthermore, we showcase the potential of 3DGR-CT for real-time physicalsimulation, which holds important clinical applications while challenging forimplicit neural representations.

3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation