SAGA: Structure-Aware Gaussian Allocation for Ultra-Sparse-View CT Reconstruction

Ultra-sparse-view computed tomography (CT) reconstructs a 3D attenuation field from only a few X-ray projections, where severe angular undersampling makes projection consistency insufficient for reliable volumetric recovery. Explicit Gaussian representations provide adaptive volumetric modeling, but their topology evolution is commonly driven by optimization gradients or heuristic criteria that cannot reliably distinguish anatomical structures from sparse-view artifacts and depth-ambiguous responses. We present SAGA, a stage-stabilized structure-aware Gaussian allocation framework for sparse-view CT reconstruction without paired volumetric supervision. SAGA combines a geometry-frequency constrained objective for stabilizing attenuation geometry and preserving structural details with a G+P structure field that integrates volumetric-gradient saliency and residual-backprojection evidence. The resulting structural evidence is converted into per-Gaussian importance scores to guide cloning, splitting, and pruning. A two-stage optimization schedule keeps Gaussian evolution conservative during early geometry formation and progressively activates structure-guided allocation as the reconstruction becomes more reliable. Experiments on five anatomies under 6, 8, and 10-view settings show that SAGA achieves the best average volumetric PSNR and SSIM across all three sampling regimes. Under the challenging 6-view setting, SAGA improves over the explicit Gaussian baseline by 1.44 dB PSNR and 0.0827 SSIM. Ablation studies further validate the contributions of the reconstruction objective, structure-aware allocation, and stage stabilization.