ArticleScientific reports2023
Denoising diffusion probabilistic models for 3D medical image generation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.Magnetic resonance in medicine · 2026Article
- POWDR: Pathology-Preserving Outpainting with Wavelet Diffusion for 3D MRI.Diagnostics (Basel, Switzerland) · 2026Article
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- The context of new media art for photography generation based on diffusion models.Scientific reports · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- AI-Powered Pipeline Transforms Neurosurgical Articles Into High-Quality Graphical Abstracts.Neurosurgery practice · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- 3DAD: Super-Resolution Image Synthesis from Anisotropic CT Images Using a Three-Dimensional Adversarial Diffusion Model.Bioengineering (Basel, Switzerland) · 2026Article
- Realistic PET image synthesis from MRI for automated inference of brain atrophy and Alzheimer's.iScience · 2026Article
- Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis.Scientific reports · 2026Article
- Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR.Sensors (Basel, Switzerland) · 2026Article
- Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.Scientific reports · 2026Article
- CT-to-PET Synthesis in the Head-Neck and Thoracic Region via Conditional 3D Latent Diffusion Modeling.Bioengineering (Basel, Switzerland) · 2026Article
- Structure-aware 3D diffusion generation for kidney MRI via mask-guided noise scheduling and topology-prior constraints.Scientific reports · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI.Scientific reports · 2026Article
- Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework.Sensors (Basel, Switzerland) · 2026Article
- 3D-StyleGAN2-ADA: Volumetric Synthesis of Realistic Prostate T2W MRI.Journal of imaging · 2026Article
40 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
Funding
Abstract
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding "realistic image appearance", "anatomical correctness", and "consistency between slices". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.