Abstract
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.
Abstract (translated)
限制角度计算机断层扫描（LACT）是一个具有挑战性的逆向问题，其中缺少的角度投影会导致不完整的弦图，并在重建图像中产生严重的伪影。虽然最近基于学习的方法已经证明了其有效性，但大多数方法假设理想且无噪声的测量值，并未能解决测量噪声的影响。为了克服这一限制，我们将LACT视为一个弦图修复任务，并提出了一种基于扩散框架的方法，使用均值复归随机微分方程（MR-SDE）来完成缺失的角度视图。为提高在实际噪声下的鲁棒性，我们提出了RNSD$^+$，这是一种新颖的噪声感知校正机制，它明确地建模了推理时间的不确定性，从而能够实现可靠和稳健的重建。广泛的实验表明，我们的方法在数据一致性以及感知质量方面始终优于基线模型，并且能够在不同的噪声强度和获取场景下良好泛化。
URL
https://arxiv.org/abs/2507.05647
