Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
667-01-003 ISMRM Abstract

Bridging ultra-strong and clinical gradients for deep characterization of cortical brain microstructure

Primary: Diffusion - Microstructure
Secondary: Diffusion - Diffusion Modeling
667-01-003 · Innovations in Diffusion MRI: Systems, Sequences, and Protocol Design · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row H
Keywords: Diffusion Acquisition Diffusion Modeling Diffusion-weighted MRI Water exchange Microstructural mapping
Accepted
Kadir Şimşek 1, Muhamed Barakovic2, Stefano Magon2, Jens Wuerfel3, Derek K Jones, Marco Palombo1
1School of Computer Science and Informatics, Cardiff, United Kingdom, Cardiff University, Cardiff, United Kingdom
2Pharma Research and Early Development, Neuroscience and Rare Diseases, Basel, Switzerland, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
3Hoffmann-La Roche AG, Basel, Switzerland
Presenting Author: Kadir Şimşek

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. 1. Palombo M, Ianus A, Guerreri M, et al. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage. 2020;215:116835. doi:10.1016/J.NEUROIMAGE.2020.116835 [doi]
2. 2. Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR Biomed. 2019;32(4):e3998. doi:10.1002/NBM.3998 [doi]
3. 3. Jelescu IO, de Skowronski A, Geffroy F, Palombo M, Novikov DS. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. Neuroimage. 2022;256:119277. doi:10.1016/J.NEUROIMAGE.2022.119277 [doi]
4. 4. Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Diffusion time dependence, power-law scaling, and exchange in gray matter. Neuroimage. 2022;251:118976. doi:10.1016/J.NEUROIMAGE.2022.118976 [doi]
5. 5. Nilsson Markus. Biophysical Modelling in Diffusion MRI: The Role of Tissue Microstructure and Water Exchange. Lund University; 2011. Accessed May 11, 2025. https://lup.lub.lu.se/record/2166371
6. 6. Uhl Q, Pavan T, Feiweier T, Piredda GF, Jelescu I. Human gray matter microstructure mapped using Neurite Exchange Imaging (NEXI) on a clinical scanner. Imaging Neuroscience. Published online May 19, 2025. doi:10.1162/IMAG.A.32 [doi]
7. 7. Schiavi S, Palombo M, Zacà D, et al. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp. 2023;44(13):4792-4811. doi:10.1002/HBM.26416 [doi]
8. 8. Genc Sila and Chamberland M and KK and TCMW and ZH and PM and JDK. Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter. In: Gyori Noemi and Hutter J and NV and PM and PM and ZF, ed. Computational Diffusion MRI. Springer International Publishing; 2021:135-145.
9. 9. Uhl Q, Pavan T, Molendowska M, Jones DK, Palombo M, Jelescu IO. Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients. Imaging Neuroscience. 2024;2:1-19. doi:10.1162/IMAG_A_00104 [doi]
10. 10. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:48-58. https://ac.els-cdn.com/S1053811905002259/1-s2.0-S1053811905002259-main.pdf?_tid=f6a3700b-8053-4e94-b55f-a58f1c7ba9e0&acdnat=1539105849_c8068108b819f4bd5a964fb72cd69c6e
11. 11. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010;49(2):1271-1281. doi:10.1016/j.neuroimage.2009.10.002 [doi]
12. 12. Kashyap S. srikash/presurfer: ondu. doi:10.5281/ZENODO.4626841 [doi]
13. 13. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-781. doi:10.1016/J.NEUROIMAGE.2012.01.021 [doi]
14. 14. Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394-406. doi:10.1016/J.NEUROIMAGE.2016.08.016 [doi]
15. 15. Sairanen V, Leemans A, Tax CMW. Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data. Neuroimage. 2018;181:331-346. doi:10.1016/J.NEUROIMAGE.2018.07.003 [doi]
16. 16. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20(2):870-888. doi:10.1016/S1053-8119(03)00336-7 [doi]
17. 17. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(SUPPL. 1):S208-S219. doi:10.1016/J.NEUROIMAGE.2004.07.051 [doi]
18. 18. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063-1078. doi:10.1016/J.NEUROIMAGE.2015.10.019 [doi]
19. 19. Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016;76(5):1574-1581. doi:10.1002/MRM.26054 [doi]
20. 20. Isensee F, Schell M, Pflueger I, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp. 2019;40(17):4952-4964. doi:10.1002/HBM.24750 [doi]
21. 21. SANDI MATLAB Toolbox. https://github.com/palombom/SANDI-Matlab-Toolbox-Latest-Release.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/667-01-003