306-02-003 ISMRM Abstract

Propagating uncertainty from diffusion MRI signal to fiber orientations, model parameters and tractography

Primary: Diffusion - Microstructure

Secondary: Diffusion - Diffusion Modeling

306-02-003 · From Theory to Clinic: Advances in Diffusion MRI Modeling and Analysis · Monday, 11 May, 1:50 PM–3:40 PM · Auditorium 2

Keywords: Tractography Uncertainty quantification Diffusion MRI Inverse Problem Implicit Neural Representation

Accepted

Jiezhang Cao¹, William Consagra², Lipeng Ning^1,3, Lauren J O’Donnell^3,4, Yogesh Rathi ^1,5

¹Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, United States of America

²Medical University of South Carolina, Charleston, United States of America

³Brigham and Women's Hospital and Harvard Medical School, Boston, United States of America

⁴Harvard-MIT Health Sciences and Technology, Cambridge, United States of America

⁵Brigham and Women’s Hospital, Brigham and Women's Hospital and Harvard Medical School, Boston, United States of America

Presenting Author: Yogesh Rathi

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References

1. Consagra W, Ning L, Rathi Y. A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI. Medical Image Analysis. 102 (2025). https://doi.org/10.1016/j.media.2025.10353 [doi]

2. Hendriks T, Vilanova A, Chamberland M. Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI. Imaging Neuroscience. 3 (2025). https://doi.org/10.1162/imag_a_00501 [doi]

3. Shen J, Agudo A, Moreno-Noguer F, Ruiz A. Conditional-flow nerf: Accurate 3d modelling with reliable uncertainty quantification. European Conference on Computer Vision. 2022 (pp. 540-557). https://doi.org/10.1007/978-3-031-20062-5_31 [doi]

4. Consagra W, Ning L, Rathi Y. Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI. Medical Image Analysis. 93 (2024). https://doi.org/10.1016/j.media.2024.103105 [doi]

5. Jelescu IO, Veraart J, Fieremans E, Novikov DS. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR Biomed. 2016;29(1):33-47. doi: 10.1002/nbm.3450. [doi]

6. Aydogan D.B., Shi Y. Parallel transport tractography. IEEE Transactions on Medical Imaging, vol. 40, no. 2, pp. 635-647, 2021, doi: 10.1109/TMI.2020.3034038. [doi]

7. Aydogan D.B., Shi Y. A novel fiber tracking algorithm using parallel transport frames. Proceedings of the 27th Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM) 2019.

8. Jacobs GR, Coleman MJ, Lewandowski KE, Pasternak O, Cetin-Karayumak S, Mesholam-Gately RI, Wojcik J, Kennedy L, Knyazhanskaya E, Reid B, Swago S, Lyons MG, Rizzoni E, John O, Carrington H, Kim N, Kotler E, Veale S, Haidar A, Prunier N, Haaf M, Levitt JJ, Seitz-Holland J, Rathi Y, Kubicki M, Keshavan MS, Holt DJ, Seidman LJ, Öngür D, Breier A, Bouix S, Shenton ME. An Introduction to the Human Connectome Project for Early Psychosis. Schizophr Bull. 2025 May 8;51(3):658-671. doi: 10.1093/schbul/sbae123. PMID: 39036958; PMCID: PMC12061660. [doi] [pmid]

9. Yang G, Huang X, Hao Z, Liu MY, Belongie S, Hariharan B. Pointflow: 3d point cloud generation with continuous normalizing flows. Proceedings of the IEEE International Conference on Computer Vision 2019 (pp. 4541-4550). doi: 10.1109/ICCV.2019.00464 [doi]

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http://echo.ismrm.org/p/ISMRM2026/306-02-003