Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
362-05-002 ISMRM Abstract

Preliminary ¹H-MRS Study of the Glia–Energy Metabolic Axis After Shunting and Prognosis in iNPH

Primary: Neuro - Dementia
Secondary: Neuro - White Matter
362-05-002 · Spectroscopy and Spectroscopic Imaging: Neuro II · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row C
Keywords: Metabolism Biomarkers Dementia MR spectroscopy Idiopathic normal pressure hydrocephalus
Accepted
Yunjung Bae1,2, Hyeong Hun Lee 3, So Young Ji4
1Department of Radiology, Seoul National University Bundang Hospital, Seoul, Korea, Republic of
2Seoul National University College of Medicine, Seoul, Korea, Republic of
3R&D Center, METLiT Inc., Seoul, Korea, Republic of
4Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul, Korea, Republic of
Presenting Author: Hyeong Hun Lee

Synopsis

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References

1. Tarnaris A, Kitchen ND, Watkins LD. Hydrodynamics and biomarkers of idiopathic normal pressure hydrocephalus: a review. Fluids Barriers CNS. 2011;8:12. doi:10.1186/2045-8118-8-12 [doi]
2. Ringstad G, Valnes LM, Dale AM, Pripp AH, Vatnehol SAS, Emblem KE, et al. Glymphatic enhancement of metabolic clearance after CSF shunting in idiopathic normal pressure hydrocephalus. Brain. 2018;141(9):2699–2708. doi:10.1093/brain/awy214 [doi]
3. Lundin F, Sjöbeck M, Wikkelsø C, Leijon G, Larsson EM. Normal pressure hydrocephalus: MR spectroscopy before and after shunting. J Neurol Neurosurg Psychiatry. 2013;84(11):1228–1233. doi:10.1136/jnnp-2012-304347 [doi]
4. Marmarou A, Bergsneider M, Relkin N, Klinge P, Black PM. The pathophysiology of normal pressure hydrocephalus: the role of CSF circulation and brain metabolism. Acta Neurochir Suppl. 2005;95:331–336. doi:10.1007/3-211-32318-X_65 [doi]
5. Takaya N, Yamada S, Osaki M, Oka H, Mii K, Ueda S, et al. Cerebral metabolic recovery after shunt surgery in idiopathic normal pressure hydrocephalus evaluated by proton MR spectroscopy. AJNR Am J Neuroradiol. 2018;39(8):1508–1515. doi:10.3174/ajnr.A5689 [doi]
6. Lee HH, Kim H. Bayesian deep learning–based ¹H-MRS of the brain: metabolite quantification with uncertainty estimation using Monte Carlo dropout. Magn Reson Med. 2022;88(1):38–52. doi:10.1002/mrm.29214 [doi]
7. Lee HH, Kim H. Deep learning–based target metabolite isolation and big data–driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain. Magn Reson Med. 2020;84(4):1689–1706. doi:10.1002/mrm.28234 [doi]
8. Lee HH, Kim H. Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain. Magn Reson Med. 2019;82(1):33–48. doi:10.1002/mrm.27727 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/362-05-002