mlsanityData to 14 Oct 2026

Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models

arXiv 2403.17734 ↗First posted 26 Mar 2024eess.IV cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The rapid advancement of Artificial Intelligence (AI) in biomedical imaging and radiotherapy is hindered by the limited availability of large imaging data repositories. With recent research and improvements in denoising diffusion probabilistic models (DDPM), high quality synthetic medical scans are now possible. Despite this, there is currently no way of generating multiple related images, such as a corresponding ground truth which can be used to train models, so synthetic scans are often manually annotated before use. This research introduces a novel architecture that is able to generate multiple, related PET-CT-tumour mask pairs using paired networks and conditional encoders. Our approach includes innovative, time step-controlled mechanisms and a `noise-seeding' strategy to improve DDPM sampling consistency. While our model requires a modified perceptual loss function to ensure accurate feature alignment we show generation of clearly aligned synthetic images and improvement in segmentation accuracy with generated images.

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 7 of its 17 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

7 of 17 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models5 Jun
  2. 2023Denoising Diffusion Medical Models19 Apr
  3. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation7 Nov
  4. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov

Other references · 3

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  3. 2021Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attentioncited by 6

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesiscited by 7
  2. 2025Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesiscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
  2. 2024Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
  3. 2025Paired Image Generation with Diffusion-Guided Diffusion Models
  4. 2024Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
  5. 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
  6. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
  7. 2025Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
  8. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation