Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
465-04-008 ISMRM Abstract

Predicting brain atrophy in Alzheimer’s disease using 3D conditional rectified flow model

Primary: Neuro - Alzheimer's Disease
Secondary: Analysis Methods - Classification and Prediction
465-04-008 · Image Analysis for Alzheimer's and Parkinson's · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row F
Keywords: Diagnosis/Prediction Neurodegeneration Alzheimer's Disease Generative diffusion model
Accepted
Jeongbeen Lee 1,2, Juhyung Park1, Rokgi Hong1, Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
2Department of Brain and Cognitive Sciences, EWHA Womans University, Seoul, Korea, Republic of
Presenting Author: Jeongbeen Lee

Synopsis

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References

1. van Oostveen, Wieke M., and Elizabeth CM de Lange. "Imaging techniques in Alzheimer’s disease: a review of applications in early diagnosis and longitudinal monitoring." International journal of molecular sciences 22.4 (2021): 2110. https://doi.org/10.3390/ijms22042110 [doi]
2. Komarova, Natalia L., and Craig J. Thalhauser. "High degree of heterogeneity in Alzheimer's disease progression patterns." PLoS computational biology 7.11 (2011): e1002251. https://doi.org/10.1371/journal.pcbi.1002251 [doi]
3. He, Rosemary, et al. "Individualized multi-horizon mri trajectory prediction for Alzheimer’s disease." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-84525-3_3 [doi]
4. Zhao, Yan, et al. "Prediction of Alzheimer's disease progression with multi-information generative adversarial network." IEEE Journal of Biomedical and Health Informatics 25.3 (2020): 711-719. doi: 10.1109/JBHI.2020.3006925 [doi]
5. Puglisi, Lemuel, Daniel C. Alexander, and Daniele Ravì. "Brain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion." arXiv preprint arXiv:2502.08560 (2025). https://doi.org/10.48550/arXiv.2502.08560 [doi]
6. Liu, Xingchao, Chengyue Gong, and Qiang Liu. "Flow straight and fast: Learning to generate and transfer data with rectified flow." arXiv preprint arXiv:2209.03003 (2022). https://doi.org/10.48550/arXiv.2209.03003 [doi]
7. R. C. Petersen, et al., “Alzheimer’s disease neuroimaging initiative (ADNI): Clinical characterization,” Neurology, vol. 74, no. 3, pp. 201–209, 2010. https://doi.org/10.1212/WNL.0b013e3181cb3e25 [doi]
8. Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.
9. Perez, Ethan, et al. "Film: Visual reasoning with a general conditioning layer." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018. https://doi.org/10.1609/aaai.v32i1.11671 [doi]
10. J. Park, et al., “Predicting Before Reconstruction: A generative prior framework for MRI acceleration,” arXiv preprint, arXiv:2510.19472, 2025. https://doi.org/10.48550/arXiv.2510.19472 [doi]

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http://echo.ismrm.org/p/ISMRM2026/465-04-008