Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
404-03-009 ISMRM Abstract

Measuring atrophy in people with Multiple Sclerosis from real-world MRI data

Primary: Neuro - Multiple Sclerosis
Secondary: Analysis Methods - Segmentation and Detection
404-03-009 · Multiscale MRI in Neuroinflammation · Tuesday, 12 May, 1:40 PM–3:30 PM · Ballroom East
Keywords: Atrophy
Accepted
Christine Farrugia 1,2,3, Agniete Kampaite1,2,3, Rozanna Meijboom1,2,3, Elizabeth N York1,2,3, Michael J Thrippleton1,3, Dawn Lyle2, Judith Watt2, Niall J. J. McDougall2,4, Siddharthan Chandran1,2, David P. Hunt1,2, Adam D. D Waldman1,2,3
1Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, United Kingdom
2Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, United Kingdom
3Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
4Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Presenting Author: Christine Farrugia

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. Sastre-Garriga J, Pareto D, Battaglini M, et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat. Rev. Neurol. 2020;16(3):171-182. Doi: 10.1038/s41582-020-0314-x [doi]
2. Narayanan S, Nakamura K, Fonov VS, et al. Brain volume loss in individuals over time: Source of variance and limits of detectability. NeuroImage. 2020;214:116737. Doi: 10.1016/J.NEUROIMAGE.2020.116737 [doi]
3. Biberacher V, Schmidt P, Keshavan A, et al. Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis. NeuroImage. 2016;142:188-197. Doi: 10.1016/J.NEUROIMAGE.2016.07.035 [doi]
4. De Stefano N, Stromillo ML, Giorgio A, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry. 2016;87(1): 93-99. Doi: 10.1136/JNNP-2014-309903 [doi]
5. Nakamura K, Brown RA, Araujo D, et al. Correlation between brain volume change and T2 relaxation time induced by dehydration and rehydration: Implications for monitoring atrophy in clinical studies. NeuroImage: Clin. 2014;6:166-170. Doi: 10.1016/J.NICL.2014.08.014 [doi]
6. Nakamura K, Brown RA, Narayanan S, et al. Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations. NeuroImage. 2015;118:126-132. Doi: 10.1016/J.NEUROIMAGE.2015.05.077 [doi]
7. Lee H, Nakamura K, Narayanan S, et al. Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements. NeuroImage. 2019;184:555-565. Doi: 10.1016/J.NEUROIMAGE.2018.09.062 [doi]
8. Smith SM, De Stefano N, Jenkinson M, et al. Normalized Accurate Measurement of Longitudinal Brain Change. J. Comput. Assist. Tomogr. 2001;25(3):466-475. DOI: 10.1097/00004728-200105000-00022 [doi]
9. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis. NeuroImage. 2002;17(1):479-489. Doi: 10.1006/NIMG.2002.1040 [doi]
10. Billot B, Greve DN, Puonti O, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med. Image Anal. 2023;86:102789. Doi: 10.1016/J.MEDIA.2023.102789 [doi]
11. Billot B, Magdamo C, Cheng Y, et al. Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proc. Natl. Acad. Sci. USA. 2023;120(9):e2216399120. DOI: 10.1073/pnas.2216399120 [doi]
12. Meijboom R, Wiseman SJ, York EN, et al. Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland [version 1; peer review: 2 approved]. Wellcome Open Res. 2022;7. Doi: 10.12688/WELLCOMEOPENRES.17731.1 [doi]
13. York EN, Meijboom R, Thrippleton MJ, et al. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. NeuroImage: Clin. 2022;36:103228. Doi: 10.1016/J.NICL.2022.103228 [doi]
14. Kearns PKA, Martin SJ, Chang J, et al. FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis. BMJ Open. 2022;12(6):e058506. Doi: 10.1136/BMJOPEN-2021-058506 [doi]
15. Smith SM. Fast robust automated brain extraction. Hum. Brain Mapp. 2002;17(3):143-155. Doi: 10.1002/HBM.10062 [doi]
16. Jenkinson M, Pechaud M, Smith S. BET2: MR-based estimation of brain, skull and scalp surfaces. 11th Annu. Meet. Organ. Hum. Brain. Mapp. 2005;17:167.
17. Thielscher A, Antunes A, Saturnino GB. Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS? 2015 37th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC). 2015;222-225. Doi: 10.1109/EMBC.2015.7318340 [doi]
18. Puonti O, van Leemput K, Saturnino GB, et al. Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling. NeuroImage. 2020;219:117044. Doi: 10.1016/J.NEUROIMAGE.2020.117044 [doi]
19. Cerri S, Puonti O, Meier DS, et al. A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. NeuroImage. 2021;225:117471. Doi: 10.1016/J.NEUROIMAGE.2020.117471 [doi]
20. Cerri S, Greve DN, Hoopes A, et al. An open-source tool for longitudinal whole-brain and white matter lesion segmentation. NeuroImage: Clin. 2023;38:103354. Doi: 10.1016/J.NICL.2023.103354 [doi]
21. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging. 1998;17(1):87-97. Doi: 10.1109/42.668698 [doi]
22. Tustison NJ, Avants BB, Cook PA, et al. N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging. 2010;29(6):1310-1320. Doi: 10.1109/TMI.2010.2046908 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/404-03-009