解决稀疏视角下基于点云的CT重建中因姿态误差导致的条纹伪影问题
Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction
- 通过可微投影算子联合优化几何参数，提升重建对姿态误差的鲁棒性
- 在真实稀疏非均匀视角条件下，显著减少条纹和条带伪影，重建质量优于传统方法
- 轻量级设计，可无缝集成到现有重建流程，适合工业实际应用
X射线计算机断层扫描（CT）通过穿透目标物体的投影图像重建三维体数据。近年来基于点云的CT方法将体积表示为3D高斯分布，在锥束稀疏视角CT中表现出高质量重建与快速收敛。然而在真实CT系统中，由于视图数量有限且分布不均，我们观察到显著的条纹与条带伪影，远超传统重建方法。经详细分析发现，这些伪影主要源于采集几何姿态的不准确，而非视图稀疏本身。本文重新审视了点阵渲染中的姿态敏感性，推导出一种基于梯度的稳定框架，可在重建过程中联合优化几何参数。研究揭示了姿态扰动如何通过可微投影算子传播，并解释了为何基于点云的CT对几何错位尤为敏感。新方法保持轻量化，易于集成到现有流程，在真实稀疏视角条件下显著提升重建保真度。
原文摘要 · Abstract (English)
X-ray computed tomography (CT) reconstructs volumetric representations of objects from projection images obtained by transmitting X-rays through a target. Recent splat-based tomography, which represents a volume as a continuous distribution of 3D Gaussians, has demonstrated both high reconstruction quality and fast convergence in cone-beam sparse-view CT. However, when deployed in real CT systems with limited and non-uniform view distributions, we observe distinctive streak and strip artifacts that are far more pronounced than in conventional reconstruction methods. Through detailed analysis, we show that these artifacts primarily originate from pose inaccuracies in the acquisition geometry rather than from view sparsity itself. We revisit pose sensitivity in the splatting formulation and derive a stable gradient-based framework that jointly refines geometric parameters during reconstruction. Our study not only identifies how pose perturbations propagate through the differentiable projection operator but also reveals why splat-based CT is particularly vulnerable to geometric misalignment. The resulting formulation remains lightweight and easily integrable into existing pipelines while substantially improving reconstruction fidelity under real-world sparse-view conditions.
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