Diffusion Models for Computational Neuroimaging: A Survey
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912226550480896 |
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| author | Zhao, Haokai Lou, Haowei Yao, Lina Peng, Wei Adeli, Ehsan Pohl, Kilian M Zhang, Yu |
| author_facet | Zhao, Haokai Lou, Haowei Yao, Lina Peng, Wei Adeli, Ehsan Pohl, Kilian M Zhang, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Models for Computational Neuroimaging: A Survey Zhao, Haokai Lou, Haowei Yao, Lina Peng, Wei Adeli, Ehsan Pohl, Kilian M Zhang, Yu Computer Vision and Pattern Recognition 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. |
| title | Diffusion Models for Computational Neuroimaging: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.06552 |