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PhyDiCT framework reconstructs 3D CT images using physics and diffusion priors

By PulseAugur Editorial · [3 sources] ·

Researchers have developed PhyDiCT, a novel framework for reconstructing 3D Computed Tomography (CT) images from limited X-ray projections. This training-free approach combines a physics-based differentiable forward model, rooted in the Beer-Lambert law, with a text-conditioned diffusion model acting as a strong prior. The system uses Split Gibbs sampling to optimize for projection fidelity and prior consistency, with an added test-time refinement step for enhanced realism. Evaluations show PhyDiCT outperforms existing plug-and-play diffusion and fully trained reconstruction methods, achieving a 7.5% improvement in SSIM. AI

IMPACT This method could improve medical imaging by enabling higher-quality CT scans from fewer X-ray inputs.

RANK_REASON The cluster describes a new research paper detailing a novel method for image reconstruction.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

PhyDiCT framework reconstructs 3D CT images using physics and diffusion priors

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors

    Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free framework that integrates a differentiable Physics-based forward model, grounded in …

  2. arXiv cs.CV TIER_1 English(EN) · Thomas Welsch, Min-Hsin Tu, David J. Chapman, Daniel E. Eakins ·

    OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation

    arXiv:2610.11547v1 Announce Type: new Abstract: NeRF and Gaussian splatting methods have been successfully applied on X-ray scenes where the views are too sparse for 3D reconstruction via classical methods. Ultra-sparse scenes with 10 or fewer views such as those with high-rate o…

  3. arXiv cs.CV TIER_1 English(EN) · Weicheng Dai, Shantanu Ghosh, Kayhan Batmanghelich ·

    PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors

    arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free framework that integrates a differen…

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