标题： 基于扩散的噪声条件下有限角度CT重建 显示英文标题

标题： Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions

摘要： 有限角度计算机断层扫描（LACT）是一个具有挑战性的逆问题，其中缺失的角投影会导致正弦图不完整，并在重建图像中产生严重的伪影。 尽管基于学习的方法已显示出有效性，但大多数方法假设理想、无噪声的测量，并未能解决测量噪声的影响。 为了克服这一限制，我们将LACT视为一个正弦图修复任务，并提出了一种基于扩散的框架，该框架使用均值回归随机微分方程（MR-SDE）公式来完成缺失的角视图。 为了在现实噪声下提高鲁棒性，我们提出了RNSD$^+$，一种新颖的噪声感知校正机制，该机制显式建模了推理时的不确定性，从而实现了可靠和鲁棒的重建。 大量实验表明，我们的方法在数据一致性和感知质量方面始终优于基线模型，并且在不同噪声强度和采集场景中具有良好的泛化能力。 显示英文摘要

摘要： 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.