Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
470-03-089 ISMRM Abstract

GenTract - Generative Models for Global Tractography

Primary: Analysis Methods - Generative Models
Secondary: Diffusion - Tractography
470-03-089 · Diffusion: Tractography Methods · Tuesday, 12 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Tractography Conditional Diffusion Generative diffusion model
Accepted
Alec Sargood1,2, Lemuel Puglisi3, Elinor Thompson1,2, Mirco Musolesi1,4, Daniel C Alexander1,2
1Department of Computer Science, University College London, London, United Kingdom
2Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
3Department of Computer Science, University of Catania, Italy
4Centre for Artificial Intelligence, Department of Computer Science, University College London, London, United Kingdom
Presenting Author: Barbara Brito Vega

Synopsis

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References

1. Jeurissen B, Descoteaux M, Mori S, Leemans A. Diffusion MRI fiber tractography of the brain. NMR in Biomedicine. 2019 Apr;32(4):e3785. https://doi.org/10.1002/nbm.3785 [doi]
2. Tournier JD, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. InProceedings of the international society for magnetic resonance in medicine 2010 May 1 (Vol. 1670, p. 2010).
3. Tournier, J.-D.; Calamante, F. & Connelly, A. MRtrix: Diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol., 2012, 22, 53-66. https://doi.org/10.1002/ima.22005 [doi]
4. Christiaens D, Reisert M, Dhollander T, Sunaert S, Suetens P, Maes F. Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model. Neuroimage. 2015 Dec 1;123:89-101. https://doi.org/10.1016/j.neuroimage.2015.08.008 [doi]
5. Théberge A, Descoteaux M, Jodoin PM. TractOracle: towards an anatomically-informed reward function for RL-based tractography. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2024 Oct 4 (pp. 476-486). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72069-7_45 [doi]
6. Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. 2013 Dec 20. https://doi.org/10.48550/arXiv.1312.6114 [doi]
7. Peebles W, Xie S. Scalable diffusion models with transformers. InProceedings of the IEEE/CVF international conference on computer vision 2023 (pp. 4195-4205). https://doi.org/10.48550/arXiv.2212.09748 [doi]
8. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Wu-Minn HCP Consortium. The WU-Minn human connectome project: an overview. Neuroimage. 2013 Oct 15;80:62-79. 10.1016/j.neuroimage.2013.05.041 [doi]
9. Kruper J, Hagen MP, Rheault F, Crane I, Gilmore A, Narayan M, Motwani K, Lila E, Rorden C, Yeatman JD, Rokem A. Tractometry of the Human Connectome Project: resources and insights. Frontiers in Neuroscience. 2024 Jun 12;18:1389680. https://doi.org/10.3389/fnins.2024.1389680 [doi]
10. Garyfallidis E, Côté MA, Rheault F, Sidhu J, Hau J, Petit L, Fortin D, Cunanne S, Descoteaux M. Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage. 2018 Apr 15;170:283-95. 10.1016/j.neuroimage.2017.07.015 [doi]

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http://echo.ismrm.org/p/ISMRM2026/470-03-089