用图结构优化3D高斯点云，减少低视角CT重建的针状伪影。
GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction
- 基于图结构计算梯度，提升密度表示精度。
- 初始化去噪+图感知梯度，使重建更准确。
- 适合低视角扫描场景，对临床精准成像有帮助。
3D高斯点云（3DGS）在CT重建中展现出潜力，但现有方法依赖视图内点的平均梯度幅度，导致稀疏视角下产生严重针状伪影。为此，我们提出GR-Gaussian，一种基于图结构的3DGS框架，有效抑制针状伪影并提升稀疏视角下的重建精度。该框架包含两项创新：(1) 去噪点云初始化策略，降低初始化误差并加速收敛；(2) 像素-图感知梯度策略，利用图结构中的密度差异优化梯度计算，提升分裂精度与密度表达能力。在X-3D和真实数据集上的实验表明，该方法分别实现0.67 dB和0.92 dB的PSNR提升，以及0.011和0.021的SSIM增益。结果验证了GR-Gaussian在复杂稀疏视角条件下进行高精度CT重建的可行性。
原文摘要 · Abstract (English)
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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