发表机构
KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种集成多色X射线投影模型等的Gaussian Splatting框架，无需手动金属掩码，联合优化参数与光谱，发布相关数据集，在CBCT金属伪影减少上性能优于现有最优方法。
AI 中文摘要
锥束计算机断层扫描（CBCT）可从X射线投影实现体积重建，但在成像金属等高衰减材料时会出现严重伪影，尤其是束硬化伪影。这些伪影源于X射线的多色特性，传统单色重建算法无法妥善解决。尽管近期基于神经表示的方法能提升重建质量，但计算成本高昂，部署时常不实用。我们提出一种新颖的、受物理启发的自校准金属伪影减少方法，可高效重建3D CBCT体积并校正束硬化伪影。该方法将多色X射线投影模型、材料相关衰减曲线及系统响应建模集成到Gaussian Splatting框架中。与现有工作不同，我们无需手动金属掩码或强先验假设，且在训练过程中联合优化重建参数与X射线光谱特性。我们还引入了经Monte-Carlo X射线模拟工具箱验证的高保真合成CBCT数据集生成流程，并发布带有严重金属诱导伪影的新数据集以支持相关研究社区。这是首个用于减少CBCT中束硬化伪影的基于Splat的方法。在合成与真实数据集上的大量实验表明，我们的方法在伪影抑制和重建精度方面均优于当前最优方法。
英文摘要
Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly addressed by conventional monochromatic reconstruction algorithms. While recent neural representation-based methods offer improved reconstruction quality, they are computationally expensive and often impractical for deployment. We propose a novel physics-inspired, self-calibrating metal artifact reduction method that efficiently reconstructs 3D CBCT volumes while correcting beam hardening artifacts. Our method integrates a polychromatic X-ray projection model, material-dependent attenuation profiles, and system response modeling into a Gaussian Splatting framework. Unlike prior work, we eliminate the need for manual metal masks or strong prior assumptions, and we optimize both reconstruction parameters and X-ray spectral characteristics jointly during training. We further introduce a high-fidelity synthetic CBCT dataset generation pipeline validated on Monte-Carlo x-ray simulation toolbox and release new datasets with severe metal-induced artifacts to support the community. This is the first splat-based method for reducing beam hardening in CBCT. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in artifact suppression and reconstruction accuracy.
Journal refComputer Graphics forum, Volume 45 (2026), Number 2
