mlsanityData to 14 Oct 2026

Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models

arXiv 2308.13002 ↗First posted 24 Aug 2023eess.IVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 4 of its 33 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

4 of 33 references.

References · 4

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 162

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesiscited by 6
  2. 2026Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
  2. 2026Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction
  3. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
  4. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
  5. 2025Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
  6. 2024Integrating Deep Unfolding with Direct Diffusion Bridges for Computed Tomography Reconstruction
  7. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
  8. 2026GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization