arXiv Machine Learning By Yujia Wu, Zhaoqiang Liu

Tracing the Oracle: Improving Diffusion Timestep Scheduling for 3D CT Reconstruction

Read the original on arXiv Machine Learning →

arXiv:2606. 06236v1 Announce Type: new Abstract: Pretrained diffusion models demonstrate impressive potential in solving highly ill-posed 3D computed tomography (CT) inverse problems, while the inference process suffers from significant computational overhead.

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arXiv AI
Jul 7

Efficient Flow Matching for Sparse-View CT Reconstruction

arXiv:2603. 00205v2 Announce Type: replace-cross Abstract: Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems.

By Jiayang Shi, Lincen Yang, Zhong Li, Tristan van Leeuwen, Daniel M. Pelt, K. Joost Batenburg
arXiv Computer Vision
Aug 31

Physics-Guided Flow Matching for CT Image Reconstruction

The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.

By Davide Evangelista
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