arXiv：cs.LG· Honglei Brinkmann, Carola-Bibiane Sch\"onlieb, Ander Biguri·· 4 小时前AI 评分30
几何感知扩散近似后验采样用于稀疏视角与有限角度 CT
Geometry-Aware Diffusion Approximate Posterior Sampling for Sparse-View and Limited-Angle CT
AI 导读
研究者提出一种几何感知的扩散引导随机重建框架，用于稀疏视角与有限角度 CT，核心是用 CT 前向算子与测量噪声协方差构建正则化噪声加权拉回度量，按方向测量敏感度自适应调节测量感知更新与随机探索。
正文
Abstract:Sparse-view computed tomography (CT) reduces radiation dose and acquisition time and may mitigate motion artifacts. However, angular undersampling provides insufficient information to determine the image uniquely and stably. Limited-angle CT, arising from restricted angular coverage, produces strongly directional information loss associated with the missing angular range. In both settings, image directions may be strongly observed, weakly constrained, or unobservable, leading to severe ill-posedness and reconstruction ambiguity. Existing diffusion-based approaches incorporate measurement information through likelihood guidance, data-consistency operations, or range-null-space corrections. However, they do not generally use the continuously varying measurement sensitivity of the acquisition to jointly shape both reconstruction updates and stochastic exploration.
We propose a geometry-aware diffusion-guided stochastic reconstruction framework for sparse-view and limited-angle CT. Its central component is a regularized noise-weighted pullback metric constructed from the CT forward operator and measurement-noise covariance. This metric continuously adapts both the measurement-aware update and stochastic exploration according to directional measurement sensitivity, suppressing changes along strongly constrained directions while permitting greater exploration along weakly constrained and unobservable directions. We complement this geometry-aware update with a regularized data-consistency correction and approximately null-space-restricted stochastic perturbations, implemented matrix-free using forward and backprojection operations together with conjugate-gradient solves. Experiments on sparse-view, noisy, and limited-angle CT demonstrate competitive reconstruction quality, strong measurement consistency, and spatially resolved empirical uncertainty estimates.
| Subjects: | Image and Video Processing (eess.IV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.08866 [eess.IV] |
| (or arXiv:2610.08866v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08866 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Honglei Brinkmann [view email]
[v1]
Mon, 5 Oct 2026 23:02:07 UTC (40,985 KB)
来源：arXiv：cs.LG · arxiv.org