arXiv Computer Vision By Song Ni, Haijun Yu, Haodong Li, Changsheng Fang, Shuyi Fan, Yixing Huang, Hengyong Yu

SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction

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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 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.

By Luis Barba, Johannes Kirschner, Benjamin Bejar
arXiv AI
Aug 18

CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction

arXiv:2608. 15246v1 Announce Type: cross Abstract: Sparse-view computed tomography (CT) reduces radiation dose by acquiring fewer projection views, but the resulting inverse problem is highly ill-posed and often produces severe streak artifacts.

By Tran Xuan Hieu Le, Doanh C. Bui, Vu Trung Duong Le, Hoai Luan Pham, Khang Nguyen, Mai K. Nguyen, Tu Bao Ho, Yasuhiko Nakashima
arXiv AI
Jun 16

LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction

arXiv:2606. 16212v1 Announce Type: cross Abstract: Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details.

By Jigang Duan, Jiayi Wang, Heran Wang, Ping Yang, Genwei Ma, Xing Zhao
arXiv Computer Vision
Sep 4

$K$-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs

The paper introduces $K$-NeAS, a scalable neural architecture for multi-material CT reconstruction that replaces separate material networks with a shared latent backbone and a differentiable $K$-material soft selector. It automates attenuation bounds using a Gaussian Mixture Model and adds a scheduled auxiliary floater loss to reduce geometric hallucinations in sparse-view settings. Evaluated on four clinical CBCT datasets, $K$-NeAS achieves higher 3D volumetric fidelity—up to a 1.88 dB PSNR gain over a single-material baseline—and shows improved robustness under extreme sparsity, outperforming baselines by up to 1.17 dB.

By Daksh K. Shah, Emmanouil Nikolakakis, Razvan Marinescu