mlsanityData to 14 Oct 2026

Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction

arXiv 2211.13926 ↗First posted 25 Nov 2022eess.IV cs.CVcited by 27

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The radiation dose in computed tomography (CT) examinations is harmful for patients but can be significantly reduced by intuitively decreasing the number of projection views. Reducing projection views usually leads to severe aliasing artifacts in reconstructed images. Previous deep learning (DL) techniques with sparse-view data require sparse-view/full-view CT image pairs to train the network with supervised manners. When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs. To relieve this limitation, we present a fully unsupervised score-based generative model in sinogram domain for sparse-view CT reconstruction. Specifically, we first train a score-based generative model on full-view sinogram data and use multi-channel strategy to form highdimensional tensor as the network input to capture their prior distribution. Then, at the inference stage, the stochastic differential equation (SDE) solver and data-consistency step were performed iteratively to achieve fullview projection. Filtered back-projection (FBP) algorithm was used to achieve the final image reconstruction. Qualitative and quantitative studies were implemented to evaluate the presented method with several CT data. Experimental results demonstrated that our method achieved comparable or better performance than the supervised learning counterparts.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 26 of its 57 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

26 of 57 references.

References · 26

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  5. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  6. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
  7. 2015Generating Sentences from a Continuous Spacecited by 612
  8. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  9. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 508
  10. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  11. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  12. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
14 more
  1. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 162
  2. 2019Invertible generative models for inverse problems: mitigating representation error and dataset biascited by 94
  3. 2017Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CTcited by 88
  4. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  5. 2018Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?cited by 54
  6. 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
  7. 2017MR image reconstruction using deep density priorscited by 37
  8. 2016Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysiscited by 22
  9. 2020Homotopic Gradients of Generative Density Priors for MR Image Reconstructioncited by 14
  10. 2019Sinogram interpolation for sparse-view micro-CT with deep learning neural networkcited by 9
  11. 2017Low Dose CT Image Reconstruction With Learned Sparsifying Transformcited by 5
  12. 2020Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Modelcited by 4
  13. 2017LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CTcited by 2
  14. 2021A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstructioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 9; 2025: 10; 2026: 4.

27 papers.

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  1. 2023Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstructioncited by 18
  2. 2024Adaptive Compressed Sensing with Diffusion-Based Posterior Samplingcited by 13
  3. 2024DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rayscited by 8
  4. 2024GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesiscited by 6
  5. 2024Discretized Gaussian Representation for Tomographic Reconstructioncited by 6
  6. 2023Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problemscited by 5
  7. 2024MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstructioncited by 4
  8. 2023Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstructioncited by 3
  9. 2024Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstructioncited by 2
  10. 2024Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CTcited by 2
  11. 2025Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusioncited by 1
  12. 2025Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstructioncited by 1
15 more
  1. 2025PRO: Projection Domain Synthesis for CT Imagingcited by 1
  2. 2025Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priorscited by 1
  3. 2023Privacy-Preserving Encrypted Low-Dose CT Denoisingcited by 0
  4. 2024CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imagingcited by 0
  5. 2024Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Modelscited by 0
  6. 2025Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstructioncited by 0
  7. 2025Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstructioncited by 0
  8. 2025UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardizationcited by 0
  9. 2025Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstructioncited by 0
  10. 2025MORE: Multi-Organ Medical Image REconstruction Datasetcited by 0
  11. 2025Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Modelcited by 0
  12. 2026Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstructioncited by 0
  13. 2026Limited-Angle CT Reconstruction Using Multi-Volume Latent Consistency Modelcited by 0
  14. 2026Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstructioncited by 0
  15. 2026CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimationcited by 0

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Close in wording, no citation either way

  1. 2023Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstruction
  2. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
  3. 2026A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
  4. 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
  5. 2022One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
  6. 2026LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
  7. 2026Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
  8. 2025ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction