# Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions Source: [Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions]() Collection index: [llms.txt]() Data updated: 2026-06-19T05:00:05.274Z Authors: Jiaqi Guo, Santiago L\\'opez-Tapia Published: 2025-07-08T03:58:52.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Reconstruction on ChromSTET 2025](): Image Reconstruction; ChromSTET 2025; PSNR: 20.43. - [Sinogram Reconstruction on ChromSTET 2025](): Sinogram Reconstruction; ChromSTET 2025; PSNR: 35.91. ## Original sources and implementation [Original paper]() [PDF]()