mlsanityData to 7 Oct 2026

Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT

arXiv 2310.06949 ↗First posted 10 Oct 2023eess.IV cs.LG physics.med-phcited by 8

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior. Drawing on the exceptional imaging prowess of the denoising diffusion probabilistic model (DDPM), we merge it with a reconstruction procedure that prioritizes data fidelity. This fusion capitalizes on the merits of both techniques, delivering exceptional reconstruction results in an unsupervised framework. To further enhance the efficiency of the reconstruction process, we incorporate the Nesterov momentum acceleration technique. This enhancement facilitates superior diffusion sampling in fewer steps. As demonstrated in our experiments, our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 25 of its 55 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

25 of 55 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction18 Nov
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  6. 2017Improved Training of Wasserstein GANscited by 2,223
  7. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  8. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  9. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  10. 2017Learned Primal-dual Reconstructioncited by 251
  11. 2017Image reconstruction by domain transform manifold learningcited by 231
  12. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
11 more
  1. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
  2. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  3. 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 75
  4. 2020FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imagingcited by 74
  5. 2018Radon Inversion via Deep Learningcited by 35
  6. 2016Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysiscited by 22
  7. 2020MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstructioncited by 19
  8. 2019Convolutional Sparse Coding for Compressed Sensing CT Reconstructioncited by 17
  9. 2016A Perspective on Deep Imagingcited by 16
  10. 2022Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A surveycited by 14
  11. 2017Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionarycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 2.

8 papers.

  1. 2023CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Modelcited by 6
  2. 2025Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Enginecited by 3
  3. 2024Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Modelcited by 2
  4. 2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Modelscited by 1
  5. 2024Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flowscited by 0
  6. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Datacited by 0
  7. 2026Foundation Models for Medical Imaging: Status, Challenges, and Directionscited by 0
  8. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
  2. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
  3. 2025AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models
  4. 2025Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
  5. 2025Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction
  6. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
  7. 2023DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding
  8. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT