Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-06-209 ISMRM Abstract

Transformer-Based Structural-to-Diffusion MRI Synthesis with Dual-Pruned Attention Boosts Dementia Classification Performance

Primary: Diffusion - DWI/DTI/DKI
Secondary: Analysis Methods - Image Synthesis and Translation
570-06-209 · Vision and Language Models in MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Traditional Posters | Exhibition Hall
Keywords: Deep learning Image translation Image synthesis Diffusion Tensor Imaging (DTI) Vision Transformer model
Accepted
Tamoghna Chattopadhyay1, Dhruv Kudalkar1, Sophia Thomopoulos1, Jose-Luis Ambite2, Greg Ver Steeg3, Paul M Thompson1
1USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
2Information Sciences Institute, University of Southern California, Marina del Rey, Los Angeles, United States of America
3University of California, Berkeley, United States of America
Presenting Author: Julio E Villalon Reina

Synopsis

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References

1. P.J. Basser, et al., (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259-267.
2. J. E. Villalón-Reina, et al. (2024). Lifespan Normative Modeling of Brain Microstructure. bioRxiv, 2024-12.
3. A. Zavaliangos-Petropulu, et al. (2019). Diffusion MRI indices and their relation to cognitive impairment in brain aging: the updated multi-protocol approach in ADNI3. Frontiers in Neuroinformatics, 13, 2.
4. C. Owens-Walton, et al. (2024). A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease. npj Parkinson's Disease, 10(1), 151.
5. J.Y. Zhu, et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2223–2232.
6. M. Mirza, et al. (2014). Conditional generative adversarial nets. arXiv preprint, arXiv:1411.1784.
7. X. Gu, et al. (2019). Generating diffusion MRI scalar maps from T1 weighted images using generative adversarial networks. In Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings 21 (pp. 489-498). Springer International Publishing.
8. T. Chattopadhyay, et al. (2025, April). Evaluating synthetic diffusion MRI maps created with diffusion denoising probabilistic models. In Medical Imaging 2025: Imaging Informatics (Vol. 13411, pp. 148-159). SPIE.
9. S. Zhang, et al. (2025). Diffusion Bridge Models for 3D Medical Image Translation. In 2025 47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) IEEE.
10. T. Chattopadhyay, et al. (2025). Synthetic Diffusion Tensor Imaging Maps Generated by 2D and 3D Probabilistic Diffusion Models: Evaluation and Applications. bioRxiv, 2025-02.
11. K. Pan, et al. Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images. Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:5062-5065.
12. W. Zheng, et al.(2022). ITTR: Unpaired image-to-image translation with transformers. arXiv preprint arXiv:2203.16015.
13. T. M. Nir, et al. (2013). Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging. NeuroImage: Clinical, 3, 180–195.
14. S.I. Thomopoulos, et al. (2021, December). Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches. In the 17th International Symposium on Medical Information Processing and Analysis (SIPAIM, Vol. 12088, pp. 166-179). SPIE.
15. C. R. Jack Jr, et al. (2008). ‘The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.’ J. Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685-691.
16. P. Lam, et al., “3-D Grid-Attention Networks for Interpretable Age and Alzheimer’s Disease Prediction from Structural MRI,” arXiv (2020).
17. M. W., Woolrich, et al. “Bayesian analysis of neuroimaging data in FSL,” NeuroImage 45(1 Suppl), S173-86 (2009).
18. M. Jenkinson, et al. “FSL,” NeuroImage 62(2), 782–790 (2012).
19. N. Jahanshad, et al. (2013). Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group. NeuroImage, 81, 455–469.
20. T. Chattopadhyay, et al. (2023). Predicting dementia severity by merging anatomical and diffusion MRI with deep 3D convolutional neural networks. In the 18th International Symposium on Medical Information Processing and Analysis (SIPAIM; Vol. 12567, pp. 90-99). SPIE.
21. A. Gretton, et al., “A kernel two-sample test,” J. Mach. Learn. Res., vol. 13, pp. 723–773, 2012.
22. M. Torso, et al., Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes. Sci Rep 10, 11237 (2020).
23. Q. Song, et al.. (2024). Predicting Alzheimer's progression in MCI: a DTI-based white matter network model. BMC Medical Imaging, 24(1), 103.
24. D. Agostinho, et al. (2022). Combined structural MR and diffusion tensor imaging classify the presence of Alzheimer’s disease with the same performance as MR combined with amyloid positron emission tomography: A data integration approach. Frontiers in Neuroscience, 15, 638175.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/570-06-209