Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-02-004 ISMRM Abstract

BrainDFMAE: A Unified Foundation Model for Aging-Brain sMRI via Deformation-Aware Pretraining

Primary: Neuro - Alzheimer's Disease
Secondary: Analysis Methods - Foundation Models
401-02-004 · Foundation Models · Tuesday, 12 May, 8:20 AM–10:10 AM · Hall 1A
Keywords: Neuroscience Alzheimer's Disease Foundation model Self-supervised learning
Accepted
Xinmei Qiu1,2, Kehan Li2,3, Yuzhu He1,2, Zehua Ren 2,3, Fan Wang2,3, Jianhua Ma2,3,4, Chunfeng Lian1,2,4
1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
2Research Center for Intelligent Medical Equipment and Devices (IMED), Xi'an Jiaotong University, Xi'an, China
3Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
4Pazhou Lab (Huangpu), Guangzhou, China
Presenting Author: Zehua Ren

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References

1. Jack Jr, Clifford R., et al. "Overview of ADNI MRI." Alzheimer's & Dementia 20.10 (2024): 7350-7360. https://doi.org/10.1002/alz.14166 [doi]
2. Su, Juntao, Syed Muhammad Anwar, and Fang Jin. "Explainsegnet: Interpretable Segmentation for Alzheimer's Diagnosis." 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/ISBI60581.2025.10981081 [doi]
3. Liu, Peirong, et al. "A modality-agnostic multi-task foundation model for human brain imaging." arXiv preprint arXiv:2509.00549 (2025). https://doi.org/10.48550/arXiv.2509.00549 [doi]
4. Van Essen, David C., et al. "The Human Connectome Project: a data acquisition perspective." Neuroimage 62.4 (2012): 2222-2231. https://doi.org/10.1016/j.neuroimage.2021.118543 [doi]
5. Jack Jr, Clifford R., et al. "The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods." Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 27.4 (2008): 685-691. https://doi.org/10.1002/jmri.21049 [doi]
6. LaMontagne, Pamela J., et al. "OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease." medrxiv (2019): 2019-12. https://doi.org/10.1101/2019.12.13.19014902 [doi]
7. Balakrishnan, Guha, et al. "Voxelmorph: a learning framework for deformable medical image registration." IEEE transactions on medical imaging 38.8 (2019): 1788-1800. https://doi.org/10.1109/TMI.2019.2897538 [doi]
8. He, Kaiming, et al. "Masked autoencoders are scalable vision learners." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. https://doi.org/10.48550/arXiv.2111.06377 [doi]
9. Hatamizadeh, Ali, et al. "Unetr: Transformers for 3d medical image segmentation." Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2022. https://doi.org/10.48550/arXiv.2103.10504 [doi]

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http://echo.ismrm.org/p/ISMRM2026/401-02-004