Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Application of Quantitative Susceptibility Mapping in the Assessment of Iron Content in Brain Regions of Normal Children

Author(s): Shilong Tang, Guanping Zhang, Xianfan Liu, Zhuo Chen and Ling He*

Volume 18, Issue 9, 2022

Published on: 23 May, 2022

Article ID: e250322202616 Pages: 10

DOI: 10.2174/1573405618666220325090655

Price: $65

conference banner
Abstract

Purpose: We evaluated brain iron content in a healthy pediatric population using quantitative susceptibility mapping (QSM).

Methods: From June 2018 to December 2019, healthy subjects aged 2-18 years old (200 males, 200 females) with no anatomical abnormalities were assessed. All of the children underwent 3D T1 anatomical MRIs in addition to the sequence scans of enhanced T2 star-weighted angiography (ESWAN). The ESWAN sequence images were obtained with software to attain quantitative susceptibility mapping of the entire brain. The magnetic susceptibility values in the same brain region were compared across different age groups. The magnetic susceptibility values expressed in the same age group were compared across sexes, brain sides, and brain regions.

Results: The magnetic susceptibility value of each brain region increased with age, and the magnetic susceptibility value expressed by each brain region demonstrated a positive correlation with the children’s age (r=0.63, P<0.05). No dramatic difference in magnetic susceptibility was observed between the brain’s left side and right side in the children within the age range ≥2-<6; however, among the children within the age range ≥6-<18, the magnetic susceptibility values expressed by the left putamen nucleus, globus pallidus, and substantia nigra were higher than those expressed by the same regions on the right side (P<0.05).

Conclusion: Quantitative susceptibility mapping can be used to evaluate the content of iron in each brain region of normal children.

Keywords: Children, brain, iron content, neurotransmitter, quantitative susceptibility mapping, MRI.

[1]
Yan F, He N, Lin H, Li R. Iron deposition quantification: Applications in the brain and liver. J Magn Reson Imaging 2018; 48(2): 301-17.
[http://dx.doi.org/10.1002/jmri.26161] [PMID: 29897645]
[2]
Pivina L, Semenova Y. Doşa MD, Dauletyarova M, Bjørklund G. Iron deficiency, cognitive functions, and neurobehavioral disorders in children. J Mol Neurosci 2019; 68(1): 1-10.
[http://dx.doi.org/10.1007/s12031-019-01276-1] [PMID: 30778834]
[3]
Vallée L. Iron and neurodevelopment Arch Pediatr 2017. 24(5S): 5S18-22.
[4]
Tiepolt S, Schäfer A, Rullmann M, et al. Quantitative susceptibility mapping of amyloid-β aggregates in alzheimer’s disease with 7T MR. J Alzheimers Dis 2018; 64(2): 393-404.
[http://dx.doi.org/10.3233/JAD-180118] [PMID: 29865069]
[5]
Cheng Z, Zhang J, He N, et al. Radiomic features of the nigrosome-1 region of the substantia Nigra: Using quantitative susceptibility map-ping to assist the diagnosis of idiopathic parkinson’s disease. Front Aging Neurosci 2019; 11(11): 167-78.
[http://dx.doi.org/10.3389/fnagi.2019.00167] [PMID: 31379555]
[6]
Zhang S, Nguyen TD, Hurtado Rúa SM, et al. Quantitative susceptibility mapping of time-dependent susceptibility changes in multiple scle-rosis lesions. AJNR Am J Neuroradiol 2019; 40(6): 987-93.
[PMID: 31097429]
[7]
Tan H, Zhang L, Mikati AG, et al. Quantitative susceptibility mapping in cerebral cavernous malformations: Clinical correlations. AJNR Am J Neuroradiol 2016; 37(7): 1209-15.
[http://dx.doi.org/10.3174/ajnr.A4724] [PMID: 26965464]
[8]
Arabi M, Saberi Kakhki A, Sohrabi M, Soltani Kouhbanani S, Jabbari Nooghabi M. Is visuomotor training an effective intervention for children with autism spectrum disorders? Neuropsychiatr Dis Treat 2019; 15(15): 3089-102.
[http://dx.doi.org/10.2147/NDT.S214991] [PMID: 31814721]
[9]
Gong NJ, Dibb R, Bulk M, van der Weerd L, Liu C. Imaging beta amyloid aggregation and iron accumulation in Alzheimer’s disease using quantitative susceptibility mapping MRI. Neuroimage 2019; 191(191): 176-85.
[http://dx.doi.org/10.1016/j.neuroimage.2019.02.019] [PMID: 30739060]
[10]
Li DTH, Hui ES, Chan Q, et al. Quantitative susceptibility mapping as an indicator of subcortical and limbic iron abnormality in Parkin-son’s disease with dementia. Neuroimage Clin 2018; 20(20): 365-73.
[http://dx.doi.org/10.1016/j.nicl.2018.07.028] [PMID: 30128274]
[11]
Sun H, Ma Y, MacDonald ME, Pike GB. Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method. Neuroimage 2018; 179(179): 166-75.
[http://dx.doi.org/10.1016/j.neuroimage.2018.06.036] [PMID: 29906634]
[12]
Yoon J, Gong E, Chatnuntawech I, et al. Quantitative susceptibility mapping using deep neural network: QSMnet. Neuroimage 2018; 179(179): 199-206.
[http://dx.doi.org/10.1016/j.neuroimage.2018.06.030] [PMID: 29894829]
[13]
Uchida Y, Kan H, Sakurai K, et al. Voxel-based quantitative susceptibility mapping in Parkinson’s disease with mild cognitive impairment. Mov Disord 2019; 34(8): 1164-73.
[http://dx.doi.org/10.1002/mds.27717] [PMID: 31091347]
[14]
Fang J, Bao L, Li X, van Zijl PCM, Chen Z. Background field removal for susceptibility mapping of human brain with large susceptibility variations. Magn Reson Med 2019; 81(3): 2025-37.
[http://dx.doi.org/10.1002/mrm.27492] [PMID: 30298547]
[15]
Liu C, Li W, Tong KA, Yeom KW, Kuzminski S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 2015; 42(1): 23-41.
[http://dx.doi.org/10.1002/jmri.24768] [PMID: 25270052]
[16]
Lancione M, Tosetti M, Donatelli G, Cosottini M, Costagli M. The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping. NMR Biomed 2017; 30(11): e3798.
[http://dx.doi.org/10.1002/nbm.3798] [PMID: 28902421]
[17]
Zhang Y, Rauscher A, Kames C, Weber AM. Quantitative analysis of punctate white matter lesions in neonates using quantitative suscepti-bility mapping and R2* Relaxation. AJNR Am J Neuroradiol 2019; 40(7): 1221-6.
[http://dx.doi.org/10.3174/ajnr.A6114] [PMID: 31221632]
[18]
Li SJ, Ren YD, Li J, et al. The role of iron in Parkinson’s disease monkeys assessed by susceptibility weighted imaging and inductively coupled plasma mass spectrometry. Life Sci 2020; 240(240): 117091.
[http://dx.doi.org/10.1016/j.lfs.2019.117091] [PMID: 31760102]
[19]
Azuma M, Hirai T, Nakaura T, et al. Combining quantitative susceptibility mapping to the morphometric index in differentiating between progressive supranuclear palsy and Parkinson’s disease. J Neurol Sci 2019; 406(406): 116443.
[http://dx.doi.org/10.1016/j.jns.2019.116443] [PMID: 31634718]
[20]
Bener A, Khattab AO, Bhugra D, Hoffmann GF. Iron and vitamin D levels among autism spectrum disorders children. Ann Afr Med 2017; 16(4): 186-91.
[http://dx.doi.org/10.4103/aam.aam_17_17] [PMID: 29063903]
[21]
Kor D, Birkl C, Ropele S, et al. The role of iron and myelin in orientation dependent R2* of white matter. NMR Biomed 2019; 32(7): e4092.
[http://dx.doi.org/10.1002/nbm.4092] [PMID: 31038240]
[22]
Thamburaj K, Soni A, Frasier LD, Tun KN, Weber SR, Dias MS. Susceptibility-weighted imaging of retinal hemorrhages in abusive head trauma. Pediatr Radiol 2019; 49(2): 210-6.
[http://dx.doi.org/10.1007/s00247-018-4292-8] [PMID: 30392163]
[23]
Kaunzner UW, Kang Y, Zhang S, et al. Quantitative susceptibility mapping identifies inflammation in a subset of chronic multiple sclerosis lesions. Brain 2019; 142(1): 133-45.
[http://dx.doi.org/10.1093/brain/awy296] [PMID: 30561514]
[24]
Johnson CP, Wang L, Tóth F, et al. Quantitative susceptibility mapping detects neovascularization of the epiphyseal cartilage after ischemic injury in a piglet model of legg-calvé-perthes disease. J Magn Reson Imaging 2019; 50(1): 106-13.
[http://dx.doi.org/10.1002/jmri.26552] [PMID: 30556613]
[25]
Mattern H, Sciarra A, Lüsebrink F, Acosta-Cabronero J, Speck O. Prospective motion correction improves high-resolution quantitative sus-ceptibility mapping at 7T. Magn Reson Med 2019; 81(3): 1605-19.
[http://dx.doi.org/10.1002/mrm.27509] [PMID: 30298692]
[26]
Du L, Zhao Z, Cui A, et al. Increased iron deposition on brain quantitative susceptibility mapping correlates with decreased cognitive func-tion in alzheimer’s disease. ACS Chem Neurosci 2018; 9(7): 1849-57.
[http://dx.doi.org/10.1021/acschemneuro.8b00194] [PMID: 29722955]
[27]
Liu J, Christiansen SD, Drangova M. Single multi-echo GRE acquisition with short and long echo spacing for simultaneous quantitative mapping of fat fraction, B0 inhomogeneity, and susceptibility. Neuroimage 2018; 172(172): 703-17.
[http://dx.doi.org/10.1016/j.neuroimage.2018.02.012] [PMID: 29448076]
[28]
Drayer B, Burger P, Darwin R, Riederer S, Herfkens R, Johnson GA. MRI of brain iron. AJR Am J Roentgenol 1986; 147(1): 103-10.
[http://dx.doi.org/10.2214/ajr.147.1.103] [PMID: 3487201]

Rights & Permissions Print Cite
Article Metrics
16
1
© 2024 Bentham Science Publishers | Privacy Policy