mlsanityData to 7 Oct 2026

Diffusion Denoising for Low-Dose-CT Model

arXiv 2301.11482 ↗First posted 27 Jan 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Low-dose Computed Tomography (LDCT) reconstruction is an important task in medical image analysis. Recent years have seen many deep learning based methods, proved to be effective in this area. However, these methods mostly follow a supervised architecture, which needs paired CT image of full dose and quarter dose, and the solution is highly dependent on specific measurements. In this work, we introduce Denoising Diffusion LDCT Model, dubbed as DDLM, generating noise-free CT image using conditioned sampling. DDLM uses pretrained model, and need no training nor tuning process, thus our proposal is in unsupervised manner. Experiments on LDCT images have shown comparable performance of DDLM using less inference time, surpassing other state-of-the-art methods, proving both accurate and efficient. Implementation code will be set to public soon.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 16 references that are in the map. First posted on arXiv 27 Jan 2023.

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13 of 16 references.

References · 13

  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. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2022Denoising Diffusion Restoration Modelscited by 951
  6. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  7. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  8. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  9. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  10. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  11. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
  12. 2017Sharpness-aware Low dose CT denoising using conditional generative adversarial networkcited by 28
1 more
  1. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedupcited by 3

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  1. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
  2. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
  3. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
  4. 2026RDDM: A Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
  5. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
  6. 2024Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction
  7. 2024DenoMamba: A fused state-space model for low-dose CT denoising
  8. 2025FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction