mlsanityData to 7 Oct 2026

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

arXiv 2211.12340 ↗First posted 22 Nov 2022eess.IV cs.CVcited by 73

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 52 of its 103 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

52 of 103 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction5 Mar
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  3. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 49

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  10. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  11. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  12. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
37 more
  1. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  2. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  3. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  4. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  5. 2021Variational Diffusion Modelscited by 1,145
  6. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  7. 2022Denoising Diffusion Restoration Modelscited by 951
  8. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  9. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  10. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  11. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  12. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  13. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  14. 2017Learned Primal-dual Reconstructioncited by 251
  15. 2021Deblurring via Stochastic Refinementcited by 248
  16. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matchingcited by 182
  17. 2017Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CTcited by 87
  18. 2015Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolationcited by 78
  19. 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 75
  20. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56
  21. 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
  22. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
  23. 2019Block Coordinate Regularization by Denoisingcited by 33
  24. 2017Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completioncited by 31
  25. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  26. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  27. 2020TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problemscited by 20
  28. 2022UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomographycited by 19
  29. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  30. 2020Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomographycited by 15
  31. 2022NeAT: Neural Adaptive Tomographycited by 11
  32. 2020Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstructioncited by 9
  33. 2017Scale-Space Anisotropic Total Variation for Limited Angle Tomographycited by 7
  34. 2020Joint Reconstruction and Calibration using Regularization by Denoisingcited by 5
  35. 2021Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approachescited by 5
  36. 2022LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstructioncited by 3
  37. 2019Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibriumcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 25; 2025: 19; 2026: 17.

73 papers.

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By area:

  1. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 93
  2. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  3. 2024R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstructioncited by 65
  4. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  5. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  6. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  7. 2024Conditional Image Synthesis with Diffusion Models: A Surveycited by 29
  8. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  9. 2023Differentiable Forward Projector for X-ray Computed Tomographycited by 14
  10. 2023Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstructioncited by 12
  11. 2024Invertible Diffusion Models for Compressed Sensingcited by 12
  12. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
38 more
  1. 2024DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rayscited by 9
  2. 2023Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Modelscited by 8
  3. 2024DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rayscited by 8
  4. 2024SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Modelscited by 8
  5. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translationcited by 8
  6. 2023Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contextcited by 7
  7. 2024Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generationcited by 7
  8. 2023Model-corrected learned primal-dual models for fast limited-view photoacoustic tomographycited by 6
  9. 2023CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Modelcited by 6
  10. 2024Random Walks with Tweedie: A Unified View of Score-Based Diffusion Modelscited by 6
  11. 2023FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restorationcited by 5
  12. 2024Plug-and-Play Priors as a Score-Based Methodcited by 5
  13. 2025Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completioncited by 5
  14. 2025GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstructioncited by 5
  15. 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
  16. 2023Cooperative Hardware-Prompt Learning for Snapshot Compressive Imagingcited by 3
  17. 2023INDigo: An INN-Guided Probabilistic Diffusion Algorithm for Inverse Problemscited by 3
  18. 2024RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equationscited by 3
  19. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
  20. 2025INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restorationcited by 3
  21. 2025DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rayscited by 3
  22. 2025FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generationcited by 3
  23. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstructioncited by 3
  24. 2023Learned Regularization for Inverse Problems: Insights from a Spectral Modelcited by 2
  25. 2024Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CTcited by 2
  26. 2025A Self-supervised Diffusion Bridge for MRI Reconstructioncited by 2
  27. 2025Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction enginecited by 2
  28. 2025Physics informed guided diffusion for accelerated multi-parametric MRI reconstructioncited by 2
  29. 2026NullFlow: One-Step Generative Reconstructioncited by 2
  30. 2024Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstructioncited by 1
  31. 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Ratescited by 1
  32. 2024FCDM: A Physics-Guided Bidirectional Frequency Aware Convolution and Diffusion-Based Model for Sinogram Inpaintingcited by 1
  33. 2024Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditionscited by 1
  34. 2024Guided MRI Reconstruction via Schr"odinger Bridgecited by 1
  35. 2025Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Modelscited by 1
  36. 2025PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CTcited by 1
  37. 2026Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstructioncited by 1
  38. 2023Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstructioncited by 0

50 most cited of 73 All 73 (JSON)

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  2. 2026A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
  3. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
  4. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction
  5. 2025When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
  6. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
  7. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
  8. 2025Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction