mlsanityData to 14 Oct 2026

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques

arXiv 2411.12181 ↗First posted 19 Nov 2024eess.IV cs.AI cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of generative models, enable single-step sampling of high quality data without the need for adversarial training. In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. This is combined with a sinusoidal curriculum that enhances the learning of the trajectory between the noise distribution and the posterior distribution of interest, allowing High Noise Improved Consistency Training (HN-iCT) to be trained in a supervised fashion. Additionally, High Noise Improved Consistency Training with Image Condition (HN-iCT-CN) architecture is introduced, enables to take Low Dose images as a condition for extracting significant features by Weighted Attention Gates (WAG).Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1 on the CIFAR10 and CelebA datasets. Moreover, our image-conditioned model demonstrates exceptional performance in enhancing low-dose (LD) CT scans.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 12 of its 20 references that are in the map. First posted on arXiv 19 Nov 2024.

Builds on

12 of 20 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising25 May

Other references · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  7. 2023Consistency Modelscited by 1,822
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  9. 2022Diffusion Models in Vision: A Surveycited by 1,015
  10. 2023Improved Techniques for Training Consistency Modelscited by 439
  11. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Poisson Flow Consistency Trainingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024PFCM: Poisson flow consistency models for low-dose CT image denoising
  2. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
  3. 2021Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism
  4. 2024CTS: A Consistency-Based Medical Image Segmentation Model
  5. 2026TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising
  6. 2026LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
  7. 2025AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models
  8. 2026DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling