arXiv Computer Vision
SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Sep 12
SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction
Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learnin...
arXiv Machine Learning
Sep 30
TomoTransformer: Towards a Foundation Model for CT Reconstruction
arXiv:2609.37605v1 Announce Type: cross Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...
Hugging Face Trending Papers
Jun 21
Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography
Sparse-view computed tomography is a severely ill-posed inverse problem, where recent 3D Gaussian Splatting methods offer an efficient explicit representation for tomographic reconstruction. However, we find that projection-domain optimization can be misleading in this setting: the rendered projections may continue to improve while the reconstructed volume deteriorates.
arXiv AI
3d ago
Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics
The study explores task‑aware projection selection for predicting hemodynamics from sparse‑view CT angiography. Using synthetic CTA data and a Fourier neural operator, the authors compare fixed sampling schemes with three optimal selection methods—Gumbel‑Softmax, Soft top‑K, and an agentic AI selector—finding that the agentic and Soft top‑K approaches yield the lowest errors for spatially resolved wall shear stress, especially under extreme sparsity. The analysis shows that task‑optimal masks differ markedly from those optimal for image reconstruction, highlighting a new direction for designing sparse acquisitions around downstream physical quantities.
arXiv Machine Learning
Sep 3
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
The paper introduces Conditional Diffusion Posterior Alignment (CDPA), a method that scales diffusion-based sparse‑view CT reconstruction to large 3D volumes by conditioning a 2D U‑Net diffusion model on an initial 3D reconstruction and enforcing data‑consistency alignment. CDPA addresses high memory demands, limited 3D training data, and slice‑wise inconsistencies, achieving state‑of‑the‑art performance on synthetic and real Cone Beam CT data. The authors also demonstrate that the same approach improves fast denoising U‑Nets, delivering near‑diffusion quality at a fraction of the computational cost.
arXiv Machine Learning
Aug 7
A neural operator view on U-Nets for inverse imaging problems
arXiv:2608. 05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging.