mlsanityData to 7 Oct 2026

Diffusion Models for Computational Neuroimaging: A Survey

arXiv 2502.06552 ↗First posted 10 Feb 2025cs.CVcited by 5

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models have shown stability and high-quality generation in natural images, there is increasing interest in adapting them to analyze brain data for various neurological tasks such as data enhancement, disease diagnosis and brain decoding. This survey provides an overview of recent efforts to integrate diffusion models into computational neuroimaging. We begin by introducing the common neuroimaging data modalities, follow with the diffusion formulations and conditioning mechanisms. Then we discuss how the variations of the denoising starting point, condition input and generation target of diffusion models are developed and enhance specific neuroimaging tasks. For a comprehensive overview of the ongoing research, we provide a publicly available repository at https://github.com/JoeZhao527/dm4neuro.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 31 of its 45 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

31 of 45 references.

Closest ancestors · 12

Same direction, newest first

  1. 2024Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution7 Apr
  2. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study1 Nov
  3. 2023InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model23 Aug
  4. 2023Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction20 Aug
  5. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis24 Mar
  6. 2023DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution24 Mar
  7. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model15 Dec
  8. 2022Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding13 Nov
  9. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
  10. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep
  11. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  12. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Scalable Diffusion Models with Transformerscited by 5,931
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2022Dual Diffusion Implicit Bridges for Image-to-Image Translationcited by 212
  10. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 179
  11. 2022Diffusion Visual Counterfactual Explanationscited by 96
  12. 2023Diffusion Model as Representation Learnercited by 95
7 more
  1. 2024Deconstructing Denoising Diffusion Models for Self-Supervised Learningcited by 89
  2. 2022Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Datacited by 39
  3. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  4. 2023Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Modelscited by 29
  5. 2023FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clientscited by 23
  6. 2024A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learningcited by 9
  7. 2024Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRIcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimulicited by 6
  2. 2025DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Imagescited by 4
  3. 2025ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentationcited by 3
  4. 2025T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Modelscited by 3
  5. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  2. 2025Deep generative priors for 3D brain analysis
  3. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  4. 2024A Comprehensive Survey on Diffusion Models and Their Applications
  5. 2022Diffusion Models in Vision: A Survey
  6. 2023Neural Diffusion Models
  7. 2022Efficient Diffusion Models for Vision: A Survey
  8. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization