ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2023
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- Integrating Anatomical Priors Into a Causal Diffusion Model.IEEE transactions on medical imaging · 2026Article
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- Latent Causal Modeling for 3D Brain MRI Counterfactuals.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- Evaluation of 3D Counterfactual Brain MRI Generation.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model.IEEE transactions on medical imaging · 2025Article
- Survey on sampling conditioned brain images and imaging measures with generative models.Biomedical engineering letters · 2025Review
- Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Synthetic Diffusion Tensor Imaging Maps Generated by 2D and 3D Probabilistic Diffusion Models: Evaluation and Applications.bioRxiv : the preprint server for biology · 2025Article
- Mitigating Data Scarcity in the Classification of Glioma Molecular Subtypes: The Power of Generative Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs.Medical image analysis · 2024Article
- Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis.NPJ digital medicine · 2024Article
- MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images.IEEE transactions on medical imaging · 2024Article
- Evaluating the Quality of Brain MRI Generators.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Advanced image generation for cancer using diffusion models.Biology methods & protocols · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational resources, we propose a conditional DPM (cDPM) with a memory-efficient process that generates realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an MRI subvolume conditioned on another subset of slices from the same MRI. By generating slices using arbitrary combinations between condition and target slices, the model only requires limited computational resources to learn interdependencies between slices even if they are spatially far apart. After having learned these dependencies via an attention network, a new anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM. Our experiments demonstrate that our method can generate high-quality 3D MRIs that share a similar distribution to real MRIs while still diversifying the training set. The code is available at https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as part of MONAI, at https://github.com/Project-MONAI/GenerativeModels.
Identifiers
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Registered trials
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.