mlsanityData to 7 Oct 2026

A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography

arXiv 2602.10722 ↗First posted 11 Feb 2026cs.CV cs.AIcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of diffusion-based generative models is combined with an iterative optimization algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 28 of its 51 references that are in the map. First posted on arXiv 11 Feb 2026.

Builds on

28 of 51 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction9 May
  2. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  3. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  4. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  6. 2022Denoising Diffusion Restoration Modelscited by 951
  7. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  8. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  9. 2017Compressed Sensing using Generative Modelscited by 337
  10. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  11. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  12. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
12 more
  1. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  2. 2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
  3. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
  4. 2024DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Modelscited by 60
  5. 2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidancecited by 43
  6. 2018Physics-Based Generative Adversarial Models for Image Restoration and Beyondcited by 37
  7. 2018Modeling Sparse Deviations for Compressed Sensing using Generative Modelscited by 33
  8. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  9. 2021Regularising Inverse Problems with Generative Machine Learning Modelscited by 22
  10. 2022To be or not to be stable, that is the question: understanding neural networks for inverse problemscited by 8
  11. 2022Image Embedding for Denoising Generative Modelscited by 7
  12. 2023Constrained Regularization by Denoising with Automatic Parameter Selectioncited by 6

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  1. 2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models
  2. 2023Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstruction
  3. 2026Towards reconstructing experimental sparse-view X-ray CT data with diffusion models
  4. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
  5. 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
  6. 2025Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction
  7. 2023Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
  8. 2023Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction