451-02-013 / 451-02-013 ISMRM Abstract

Foundations of susceptibility source separation for studying brain aging: the age dependence of the relaxometric constant

Primary: Contrast Mechanisms - Susceptibility/QSM

Secondary: Neuro - Aging

451-02-013 · Advances in Contrast Mechanisms · Tuesday, 12 May, 1:40 PM–3:16 PM · Power Pitch Theatre 1

Keywords: Analysis/Processing Brain Contrast Mechanisms Neuroscience Quantitative Susceptibility Mapping (QSM)

Accepted

Niklas Kuegler ^1,2, Taechang Kim³, Shir Filo¹, Agnieszka Z Burzynska^1,4, Kerrin J Pine¹, Mikhail Zubkov⁵, Puneet Talwar⁵, Gilles Vandewalle⁵, Felix Büttner^1,2, Valerij G Kiselev^1,6, Jongho Lee³, Nikolaus Weiskopf^1,7,8, Evgeniya Kirilina¹

¹Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

²International Max Planck Research School (IMPRS) for Cognitive Neuroimaging, Germany

³Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of

⁴SHARE Berlin Institute, Berlin, Germany

⁵GIGA Institute, CRC Human Imaging Unit, University of Liège, Liège, Belgium

⁶Department of Radiology, Medical Physics, Medical Center – University of Freiburg, Freiburg, Germany

⁷Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Leipzig, Germany

⁸Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

Presenting Author: Niklas Kuegler

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.

References

1. Shin HG, Lee J, Yun YH, et al. χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. NeuroImage. 2021;240:118371. doi:10.1016/j.neuroimage.2021.118371 [doi]

2. Chen J, Gong NJ, Chaim KT, Otaduy MCG, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. NeuroImage. 2021;242:118477. doi:10.1016/j.neuroimage.2021.118477 [doi]

3. Dimov AV, Nguyen TD, Gillen KM, et al. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging. 2022;32(5):852-859. doi:10.1111/jon.13014 [doi]

4. Li Z, Feng R, Liu Q, et al. APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. NeuroImage. 2023;274:120148. doi:10.1016/j.neuroimage.2023.120148 [doi]

5. Kan H, Uchida Y, Kawaguchi S, Kasai H, Hiwatashi A, Ueki Y. Quantitative susceptibility mapping for susceptibility source separation with adaptive relaxometric constant estimation (QSM-ARCS) from solely gradient-echo data. NeuroImage. 2024;296:120676. doi:10.1016/j.neuroimage.2024.120676 [doi]

6. Şişman M, Nguyen TD, Roberts AG, et al. Microstructure‐Informed Myelin Mapping (MIMM) from routine multi‐echo gradient echo data using multiscale physics modeling of iron and myelin effects and QSM. Magn Reson Med. 2025;93(4):1499-1515. doi:10.1002/mrm.30369 [doi]

7. Yoon J, Gong E, Chatnuntawech I, et al. Quantitative susceptibility mapping using deep neural network: QSMnet. NeuroImage. 2018;179:199-206. doi:10.1016/j.neuroimage.2018.06.030 [doi]

8. Ji S, Park J, Shin HG, Yoon J, Kim M, Lee J. Successful generalization for data with higher or lower resolution than training data resolution in deep learning powered QSM reconstruction. In: Proceedings of the 31th Annual ISMRM Meeting, Toronto, Canada. ; 2023

9. Kim M, Ji S, Kim J, et al. χ‐sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation. Hum Brain Mapp. 2025;46(2). doi:10.1002/hbm.70136 [doi]

10. Kim J, Kim M, Ji S, et al. In-vivo high-resolution χ-separation at 7T. NeuroImage. 2025;308:121060. doi:10.1016/j.neuroimage.2025.121060 [doi]

11. Min K, Sohn B, Kim WJ, et al. A human brain atlas of χ‐separation for normative iron and myelin distributions. NMR Biomed. 2024:e5226. doi:10.1002/nbm.5226 [doi]

12. Bazin PL, Groot JM, Miletic S, Groenewegen L, Trutti AC, Mulder MJ, Forstmann BU, Alkemade A; Automated parcellation and atlasing of the human subcortex with ultra-high resolution quantitative MRI. Imaging Neuroscience 2025; doi: https://doi.org/10.1162/imag_a_00560 [doi]

13. Gräßle T, Crockford C, Eichner C, Girard-Buttoz C, Jäger C, Kirilina E, Lipp I, Düx A, Edwards L, Jauch A, Kopp KS, Paquette M, Pine K, Haun DBM, McElreath R, Anwander A, Gunz P, Morawski M, Friederici AD … (2023). Sourcing high tissue quality brains from deceased wild primates with known socio-ecology. Methods in Ecology and Evolution, 14, 1906–1924. https://doi.org/10.1111/2041-210X.14039 [doi]

14. Rotkopf LT, Buschle LR, Schlemmer HP, Ziener CH; Influence of diffusion on transverse relaxation rates and phases of an ensemble of magnetic spheres. Journal of Magnetic Resonance 2022; doi: https://doi.org/10.1016/j.jmr.2022.107259 [doi]

15. Hallgren B, Sourander P; The effect of age on the non-haemin iron in the human brain. Journal of Neurochemistry 1958;3(1):41-51. doi: 10.1111/j.1471-4159.1958.tb12607.x [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/451-02-013