mlsanityData to 7 Oct 2026

Brain Imaging Generation with Latent Diffusion Models

arXiv 2209.07162 ↗First posted 15 Sep 2022eess.IV cs.CV q-bio.QMcited by 267

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N=31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariables, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community.

Key facts. Cited by 267 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 23 of its 35 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

23 of 35 references.

References · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  8. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  9. 2022Classifier-Free Diffusion Guidancecited by 5,717
  10. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  11. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
  12. 2016Least Squares Generative Adversarial Networkscited by 1,000
11 more
  1. 2017Generative Adversarial Networks: An Overviewcited by 588
  2. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  3. 2017Deep Learning for Medical Image Analysiscited by 399
  4. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 282
  5. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  6. 2016Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkercited by 72
  7. 2022Synthetic Data -- what, why and how?cited by 66
  8. 2019Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networkscited by 65
  9. 2020Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANcited by 60
  10. 2020Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methodscited by 13
  11. 2019A Tool for Super-Resolving Multimodal Clinical MRIcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 14; 2023: 64; 2024: 70; 2025: 73; 2026: 46.

267 papers.

Sort:

By area:

  1. 2023Extracting Training Data from Diffusion Modelscited by 723
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2023Self-Consuming Generative Models Go MADcited by 239
  4. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 156
  5. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  6. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Modelscited by 130
  7. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  8. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domainscited by 85
  9. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  10. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  11. 2022Diffusion Probabilistic Models beat GANs on Medical Imagescited by 77
  12. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
38 more
  1. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Modelscited by 69
  2. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  3. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Studycited by 64
  4. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  5. 2022SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generationcited by 56
  6. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  7. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generationcited by 54
  8. 2022Efficient Diffusion Models for Vision: A Surveycited by 50
  9. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  10. 2023Membership Inference of Diffusion Modelscited by 46
  11. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  12. 2024Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptionscited by 45
  13. 2023Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesiscited by 43
  14. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
  15. 2024Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledgecited by 35
  16. 2023Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationcited by 33
  17. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Modelcited by 28
  18. 2023InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Modelcited by 28
  19. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Modelscited by 27
  20. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 26
  21. 2022Improving dermatology classifiers across populations using images generated by large diffusion modelscited by 25
  22. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray imagescited by 23
  23. 2023Score-Based Generative Models for PET Image Reconstructioncited by 22
  24. 2023Medical diffusion on a budget: Textual Inversion for medical image generationcited by 20
  25. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Imagescited by 19
  26. 2023Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesiscited by 19
  27. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  28. 2023Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Modelscited by 18
  29. 2023Synthetic Augmentation with Large-scale Unconditional Pre-trainingcited by 18
  30. 2024Differentially Private Fine-Tuning of Diffusion Modelscited by 18
  31. 2024Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Riskcited by 17
  32. 2025Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusioncited by 17
  33. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  34. 2023Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedbackcited by 14
  35. 2023Synthesising Rare Cataract Surgery Samples with Guided Diffusion Modelscited by 14
  36. 2024Generative Enhancement for 3D Medical Imagescited by 14
  37. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
  38. 2023Data Augmentation for Seizure Prediction with Generative Diffusion Modelcited by 13

50 most cited of 267 All 267 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024Diffusion-Based Approaches in Medical Image Generation and Analysis
  2. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
  3. 2024Guided Synthesis of Labeled Brain MRI Data Using Latent Diffusion Models for Segmentation of Enlarged Ventricles
  4. 2025Diffusion Models for conditional MRI generation
  5. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  6. 2023Natural scene reconstruction from fMRI signals using generative latent diffusion
  7. 2024Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
  8. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms