Towards Enhanced Sparse-View Tomographic Reconstruction Using 3D Gaussian Splatting

Aqsa Yousaf, Paul Agbaje, Afia Anjum, Arkajyoti Mitra, Habeeb Olufowobi

Keywords: Sparse-view CT reconstruction, 3D Gaussian splatting, Medical imaging

Abstract: Sparse-view tomographic reconstruction aims to recover 3D volumes from limited projection views, but often suffers from incomplete structures and volumetric artifacts. Gaussian splatting has recently emerged as an efficient representation for continuous volumetric modeling, reducing memory cost compared to voxel grids and training time compared to implicit methods. However, existing Gaussian splatting methods for CT reconstruction struggle with needle-like artifacts in sparse-view settings. To address this, we introduce two key contributions. First, we propose a structure-aware initialization strategy that uses gradient and density magnitude from preliminary reconstructions to intelligently place Gaussian primitives in high-contrast regions. Second, we adapt the well-established Beer-Lambert law from CT physics to stabilize Gaussian splatting optimization, transforming the exponential attenuation relationship into a linear domain that mitigates vanishing gradients, and stabilizes optimization. Together, these innovations lead to sharper and more stable reconstructions, achieving average improvements of 2.32% in PSNR and 2.41% in SSIM while using 6.47% fewer primitives across three standard CT datasets.

Submission Number: 376