GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction

• URL: http://arxiv.org/abs/2508.02408v2
• Date: Wed, 06 Aug 2025 15:26:47 GMT
• Title: GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction
• Authors: Yikuang Yuluo, Yue Ma, Kuan Shen, Tongtong Jin, Wang Liao, Yangpu Ma, Fuquan Wang,
• Abstract summary: We propose GR-Gaussian, a graph-based 3D Gaussian Splatting framework for CT reconstruction.<n> GR-Gaussian suppresses needle-like artifacts and improves reconstruction accuracy under sparse-view conditions.<n>Experiments on X-3D and real-world datasets validate the effectiveness of GR-Gaussian.
• Score: 1.2506009236700528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising approach for CT reconstruction. However, existing methods rely on the average gradient magnitude of points within the view, often leading to severe needle-like artifacts under sparse-view conditions. To address this challenge, we propose GR-Gaussian, a graph-based 3D Gaussian Splatting framework that suppresses needle-like artifacts and improves reconstruction accuracy under sparse-view conditions. Our framework introduces two key innovations: (1) a Denoised Point Cloud Initialization Strategy that reduces initialization errors and accelerates convergence; and (2) a Pixel-Graph-Aware Gradient Strategy that refines gradient computation using graph-based density differences, improving splitting accuracy and density representation. Experiments on X-3D and real-world datasets validate the effectiveness of GR-Gaussian, achieving PSNR improvements of 0.67 dB and 0.92 dB, and SSIM gains of 0.011 and 0.021. These results highlight the applicability of GR-Gaussian for accurate CT reconstruction under challenging sparse-view conditions.

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