Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
531-03-009 / 531-03-009 ISMRM Abstract

Iron Does Not Respect Anatomical Boundaries in Deep Gray Matter: Implications for Iron-Sensitive MRI Contrast

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Neuro - Gray Matter
531-03-009 · Exploring Brain Microstructure Across White Matter, Gray Matter, and in TBI · Wednesday, 13 May, 4:00 PM–5:36 PM · Roof Terrace
531-03-009 · Exploring Brain Microstructure Across White Matter, Gray Matter, and in TBI · Wednesday, 13 May, 4:00 PM–5:36 PM · Roof Terrace
Keywords: 3D T1-weighted imaging Quantitative Susceptibility Mapping (QSM) Basal ganglia nuclei Iron quantification Multi-contrast MRI
Accepted
Fahad Salman1,2, Christian Riedl3, Kaylea Horacek2,4, Kevin P Thomas2,5, Thomas Jochmann2,6, Guenther Grabner7, Niels P Bergsland2, Michael G Dwyer2,8, Bianca Weinstock-Guttman9, Robert Zivadinov2,8, Simon Hametner3, Ferdinand Schweser 2,8
1Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, United States of America
2Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, United States of America
3Medical University of Vienna, Vienna, Austria
4University of New England College of Osteopathic Medicine, Portland, United States of America
5Kiran C Patel College of Osteopathic Medicine, NOVA Southeastern University, Fort Lauderdale, United States of America
6Insitute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany
7Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
8Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, United States of America
9Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, United States of America
Presenting Author: Ferdinand Schweser

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. Sandrone S, Aiello M, Cavaliere C, et al. Mapping myelin in white matter with T1-weighted/T2-weighted maps: discrepancy with histology and other myelin MRI measures. Brain Struct Funct. 2023;228(2):525-535. doi:10.1007/s00429-022-02600-z [doi]
2. Lyman C, Lee D, Ferrari H, et al. MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes. J Neuroimaging. 2022;32(2):245-252. doi:10.1111/jon.12947 [doi]
3. Burggraaff J, Liu Y, Prieto JC, et al. Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study. NeuroImage Clin. 2021;29:102549. doi:10.1016/j.nicl.2020.102549 [doi]
4. Salman F, Thomas K, Adegbemigun A, et al. Bi-parametric Joint Label Fusion A Comprehensive Segmentation Tool for Deep Gray Matter in QSM. EMTP QMR. Published online 2024.
5. Zhang Y, Wei H, Cronin MJ, He N, Yan F, Liu C. Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping. NeuroImage. 2018;171:176-189. doi:10.1016/j.neuroimage.2018.01.008 [doi]
6. Li X, Chen L, Kutten K, et al. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility. NeuroImage. 2019;191:337-349. doi:10.1016/j.neuroimage.2019.02.016 [doi]
7. Feng X, Deistung A, Dwyer MG, et al. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magn Reson Imaging. 2017;39:110-122. doi:10.1016/j.mri.2017.02.002 [doi]
8. Treit S, Naji N, Seres P, et al. R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years. Hum Brain Mapp. 2021;42(14):4597-4610. doi:10.1002/hbm.25569 [doi]
9. Naji N, Sun H, Wilman AH. On the value of QSM from MPRAGE for segmenting and quantifying iron‐rich deep gray matter. Magn Reson Med. 2020;84(3):1486-1500. doi:10.1002/mrm.28226 [doi]
10. Hagemeier J, Zivadinov R, Dwyer MG, et al. Changes of deep gray matter magnetic susceptibility over 2 years in multiple sclerosis and healthy control brain. NeuroImage Clin. 2018;18:1007-1016. doi:10.1016/j.nicl.2017.04.008 [doi]
11. Sun H, Wilman AH. Background field removal using spherical mean value filtering and Tikhonov regularization. Magn Reson Med. 2014;71(3):1151-1157. doi:10.1002/mrm.24765 [doi]
12. Schweser F, Sommer K, Deistung A, Reichenbach JR. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. NeuroImage. 2012;62(3):2083-2100. doi:10.1016/j.neuroimage.2012.05.067 [doi]
13. Hanspach J, Dwyer MG, Bergsland NP, et al. Methods for the computation of templates from quantitative magnetic susceptibility maps (QSM) – toward improved atlas- and voxel-based analyses (VBA). J Magn Reson Imaging JMRI. 2017;46(5):1474-1484. doi:10.1002/jmri.25671 [doi]
14. Salman F, Ramesh A, Jochmann T, Prayer M, Adegbemigun A, Reeves JA, Wilding GE, Cho J, Jakimovski D, Bergsland N, Dwyer MG, Zivadinov R, Schweser F. Sensitivity of Quantitative Susceptibility Mapping for Clinical Research in Deep Gray Matter. Hum Brain Mapp. 2025 Apr 15;46(6):e70187. doi: 10.1002/hbm.70187. [doi]
15. Stüber C, Morawski M, Schäfer A, et al. Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. NeuroImage. 2014;93:95-106. doi:10.1016/j.neuroimage.2014.02.026 [doi]
16. Bankhead P, Loughrey MB, Fernández JA, et al. QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017;7(1):16878. doi:10.1038/s41598-017-17204-5 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/531-03-009