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
本文针对锥形束CT的金属伪影问题，提出基于样条的框架，将物理多能前向模型融入连续高斯表示，实现高效联合优化，实验显示其收敛更快、抑伪影效果更优。
AI 中文摘要
X射线计算机断层扫描（CT）在存在高衰减物体（如牙填充物或骨科植入物）时会产生严重的金属伪影。这些伪影源于X射线的多能性，其衰减随光子能量和材料组成变化显著，打破了传统重建算法采用的单能假设。近期神经渲染方法尝试通过可微分多能投影模型解决这种不匹配，但扩展到大型锥形束CT时，仍存在平滑性偏差、精细结构丢失及计算成本过高的问题。本文提出一种基于样条的金属伪影消除框架，将基于物理的多能前向模型融入锥形束CT的连续高斯表示中。每个高斯通过紧凑材料参数化编码底层材料的能量依赖衰减，无需依赖金属掩码即可实现几何与材料属性的高效联合优化。这种紧凑衰减公式捕捉了生物组织和金属植入物的基本变化，使模型能够解释金属诱导的非线性，同时保留高频结构。对模拟和真实锥形束CT扫描的实验表明，与现有重建方法及神经场方法相比，本文方法收敛速度显著更快，且能更有效地抑制金属伪影。
英文摘要
X-ray computed tomography (CT) suffers from severe metal artifacts when high-attenuation objects such as dental fillings or orthopedic implants are present. These artifacts originate from the polychromatic nature of X-rays, where attenuation varies strongly with photon energy and material composition, breaking the monochromatic assumption used by conventional reconstruction algorithms. Recent neural rendering approaches attempt to address this mismatch through differentiable polychromatic projection models, but they still struggle with smoothness bias, loss of fine structures, and prohibitive computation when extended to large-scale cone-beam CT. We introduce a splat-based metal artifact reduction framework that incorporates a physically grounded polychromatic forward model into a continuous Gaussian representation for cone-beam CT. Each Gaussian encodes the energy-dependent attenuation of the underlying material using a compact material parameterization, which enables efficient joint optimization of geometric and material properties without relying on a metal mask. This compact attenuation formulation captures the essential variation across biological tissues and metallic implants, allowing our model to explain metal-induced nonlinearity while preserving high-frequency structure. Experiments on simulated and real cone-beam CT scans show that our method converges significantly faster and suppresses metal artifacts more effectively than existing reconstruction and neural field-based approaches.
Journal refProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
