Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
462-02-010 ISMRM Abstract

Measuring g-Ratio in the Intraorbital Optic Nerve

Primary: Neuro - White Matter
Secondary: Diffusion - Microstructure
462-02-010 · Diffusion MRI Neuroscience · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row C
Keywords: Multiple Sclerosis T1 Mapping Diffusion-weighted MRI Optic nerves G-ratio
Accepted
Ameen Qadi1,2, Hugo Albert Plante 3,4, Agah Karakuzu3, Bas Rokers1,2, Nikola Stikov1,2,3
1Center for Brain and Health, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
2Psychology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates
3NeuroPoly Lab, Department of Electrical Engineering, Polytechnique Montréal, Montréal, Canada
4Department of Electrical Engineering, Polytechnique Montréal, Montréal, Canada
Presenting Author: Hugo Albert Plante

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References

1. Giacci, M. K. et al. Three dimensional electron microscopy reveals changing axonal and myelin morphology along normal and partially injured optic nerves. Sci. Rep. 8, 3979 (2018). doi.org/10.1038/s41598-018-22361-2. [doi]
2. Trip, S. A. et al. Optic nerve diffusion tensor imaging in optic neuritis. Neuroimage 30, 498–505 (2006). doi.org/10.1016/j.neuroimage.2005.09.024. [doi]
3. Wheeler-Kingshott, C. A. M. et al. ADC mapping of the human optic nerve: increased resolution, coverage, and reliability with CSF-suppressed ZOOM-EPI. Magn. Reson. Med. 47, 24–31 (2002). doi.org/10.1002/mrm.10016. [doi]
4. Tian, Y. et al. Comparison of field-of-view optimized and constrained undistorted single-shot diffusion-weighted imaging and conventional diffusion-weighted imaging of optic nerve and chiasma at 3T. Neuroradiology 60, 903–912 (2018). doi.org/10.1007/s00234-018-2058-5. [doi]
5. Zhou, F. et al. Reproducibility and feasibility of optic nerve diffusion MRI techniques: single-shot echo-planar imaging (EPI), readout-segmented EPI, and reduced field-of-view diffusion-weighted imaging. BMC Med. Imaging 22, 96 (2022). doi.org/10.1186/s12880-022-00814-5. [doi]
6. Kraker, J. A. & Chen, J. J. An update on optic neuritis. J. Neurol. 270, 5113–5126 (2023). doi.org/10.1007/s00415-023-11920-x. [doi]
7. Toosy, A. T., Mason, D. F. & Miller, D. H. Optic neuritis. Lancet Neurol. 13, 83–99 (2014). doi.org/10.1016/S1474-4422(13)70259-X. [doi]
8. Friese, M. A., Schattling, B. & Fugger, L. Mechanisms of neurodegeneration and axonal dysfunction in multiple sclerosis. Nat. Rev. Neurol. 10, 225–238 (2014). doi.org/10.1038/nrneurol.2014.37. [doi]
9. Modvig, S. et al. Relationship between cerebrospinal fluid biomarkers for inflammation, demyelination and neurodegeneration in acute optic neuritis. PLoS One 8, e77163 (2013). doi.org/10.1371/journal.pone.0077163. [doi]
10. Bsteh, G. et al. Diagnostic performance of adding the optic nerve region assessed by optical coherence tomography to the diagnostic criteria for multiple sclerosis. Neurology 101, e784–e793 (2023). doi.org/10.1212/WNL.0000000000207507. [doi]
11. Stikov, N. et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 118, 397–405 (2015). doi.org/10.1016/j.neuroimage.2015.05.023. [doi]
12. Stikov, N. et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage 54, 1112–1121 (2011). doi.org/10.1016/j.neuroimage.2010.08.068. [doi]
13. Campbell, J. S. W. et al. Promise and pitfalls of g-ratio estimation with MRI. Neuroimage 182, 80–96 (2018). doi.org/10.1016/j.neuroimage.2017.08.038. [doi]
14. Mezer, A. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19, 1667–1672 (2013). http://doi.org/10.1038/nm.3390. [doi]
15. Theaud, G. et al. TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity. Neuroimage 218, 116889 (2020). doi.org/10.1016/j.neuroimage.2020.116889. [doi]
16. Karakuzu, A. et al. qMRLab: Quantitative MRI analysis, under one umbrella. J. Open Source Softw. 5, 2343 (2020). doi.org/10.21105/joss.02343. doi.org/10.21105/joss.02343. [doi]
17. Miller, N., Liu, Y., Krivochenitser, R. & Rokers, B. Linking neural and clinical measures of glaucoma with diffusion magnetic resonance imaging (dMRI). PLoS One 14, e0217011 (2019). doi.org/10.1371/journal.pone.0217011. [doi]
18. Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281 (2010). doi.org/10.1016/j.neuroimage.2009.10.002. [doi]

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http://echo.ismrm.org/p/ISMRM2026/462-02-010