用扩散模型修复噪声下的缺失角度CT图像，重建更准更稳。
Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions
- 将缺角CT重建视为 sinogram 填补问题，采用均值回归随机微分方程建模。
- 在不同噪声强度下均优于基线模型，数据一致性和视觉质量显著提升。
- 适合医学成像中低剂量、不完整投影场景的重建任务。
有限角度计算机断层扫描（LACT）是一个具有挑战性的反演问题，缺失的角度投影会导致正弦图不完整并引发严重伪影。尽管基于学习的方法已显示出有效性，但多数假设测量无噪声，无法应对实际噪声影响。为此，本文将 LACT 视为 sinogram 填补任务，提出一种基于扩散模型的框架，利用均值回归随机微分方程（MR-SDE）完成缺失角度视图的补全。为增强真实噪声环境下的鲁棒性，设计了 RNSD$^+$，一种新型噪声感知修正机制，显式建模推理时的不确定性，实现可靠且稳健的重建。大量实验表明，该方法在数据一致性与感知质量上持续优于基线模型，并在不同噪声强度和采集条件下具有良好泛化能力。
原文摘要 · Abstract (English)
Limited-Angle Computed Tomography (LACT) is a challenging inverse problem where missing angular projections lead to incomplete sinograms and severe artifacts in the reconstructed images. While recent learning-based methods have demonstrated effectiveness, most of them assume ideal, noise-free measurements and fail to address the impact of measurement noise. To overcome this limitation, we treat LACT as a sinogram inpainting task and propose a diffusion-based framework that completes missing angular views using a Mean-Reverting Stochastic Differential Equation (MR-SDE) formulation. To improve robustness under realistic noise, we propose RNSD$^+$, a novel noise-aware rectification mechanism that explicitly models inference-time uncertainty, enabling reliable and robust reconstruction. Extensive experiments demonstrate that our method consistently surpasses baseline models in data consistency and perceptual quality, and generalizes well across varying noise intensity and acquisition scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。