Investigating Optimal Echo Times for Quantitative Susceptibility Mapping of Basal Ganglia Nuclei in the Healthy Brain

Author(s): Wenping Fan, Xue Wang, Xingwen Zhang, Mengqi Liu, Qinglin Meng, Zhiye Chen*

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 8 , 2020

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Graphical Abstract:


Background: Quantitative susceptibility mapping (QSM) technique had been used to measure the magnetic susceptibility of brain tissue in clinical practice. However, QSM presented echo-time (TE) dependence, and an appropriate number of echo-times (nTEs) for QSM became more important to obtain the reliable susceptibility value.

Objective: The aim of the study was to explore the optimal nTEs for quantitative susceptibility mapping (QSM) measurements of basal ganglia nuclei in the healthy brain.

Methods: 3D multi-echo enhanced gradient recalled echo T2 star weighted angiography (ESWAN) sequence was acquired on a 3.0T MR scanner for QSM analysis. Regions of interests (ROIs) were drawn along the margin of the head of the caudate nucleus (HCN), putamen (Pu) and globus pallidus (GP). The mean susceptibility value and standard deviation of the ROIs were derived from the pixels within each region.

Results: CV analysis demonstrated that TE6, TE8 and TE14 ESWAN sequences presented consistent lower CV value (< 1) for QSM measure of HCN, Pu and GP. ANOVA identified that susceptibility value showed no significant difference between TE6 and TE8 in HCN, Pu and GP (P > 0.05). ICC analysis demonstrated that the susceptibility value of TE6-TE8 had the highest ICC value as compared with TE6-TE14 and TE8-TE14 in HCN, Pu and GP. Combined with the timeefficiency of MRI scanning, TE6 sequence could not only provide the reliable QSM measurement but also short imaging time.

Conclusion: The current study identified that the optimal nTEs of ESWAN were 6 TEs (2.9ms ~ 80.9ms) for QSM measurement of basal ganglia nuclei in the healthy brain.

Keywords: Basal ganglia, magnetic resonance imaging, quantitative susceptibility mapping, brain, echo-time, regions of interest.

Brass SD, Chen NK, Mulkern RV, Bakshi R. Magnetic resonance imaging of iron deposition in neurological disorders. Top Magn Reson Imaging 2006; 17(1): 31-40.
[ ] [PMID: 17179895]
Jack CR Jr, Marjanska M, Wengenack TM, et al. Magnetic resonance imaging of Alzheimer’s pa-thology in the brains of living transgenic mice: A new tool in Alzheimer’s disease research. Neuroscientist 2007; 13(1): 38-48.
[ ] [PMID: 17229974]
Bartzokis G, Tishler TA. MRI evaluation of basal ganglia ferritin iron and neurotoxicity in Alzheimer’s and Huntingon’s disease. Cell Mol Biol 2000; 46(4): 821-33.
[PMID: 10875443]
Chen JC, Hardy PA, Kucharczyk W, et al. MR of human postmortem brain tissue: Correla-tive study between T2 and assays of iron and ferritin in Park-inson and Huntington disease. AJNR Am J Neuroradiol 1993; 14(2): 275-81.
[PMID: 8456699]
Bagga D, Modi S, Poonia M, et al. T2 relaxation time alterations underlying neurocognitive deficits in alcohol-use disorders (AUD) in an Indian popula-tion: A combined conventional ROI and voxel-based re-laxometry analysis. Alcohol 2015; 49(7): 639-46.
[ ] [PMID: 26537482]
Haacke EM, Miao Y, Liu M, et al. Correlation of putative iron content as represented by changes in R2* and phase with age in deep gray matter of healthy adults. J Magn Reson Imaging 2010; 32(3): 561-76.
[ ] [PMID: 20815053]
Péran P, Hagberg G, Luccichenti G, et al. Voxel-based analysis of R2* maps in the healthy human brain. J Magn Reson Imaging 2007; 26(6): 1413-20.
[ ] [PMID: 18059009]
Haacke EM, Garbern J, Miao Y, Habib C, Liu M. Iron stores and cerebral veins in MS studied by susceptibility weighted imaging. Int Angiol 2010; 29(2): 149-57.
[PMID: 20351671]
Chen Z, Liu M, Liu M, et al. Effect of normal aging on the structure of marginal division of neostriatum as measured by MR phase imaging and diffusion tensor imag-ing. J Magn Reson Imaging 2017; 45(5): 1343-51.
[ ] [PMID: 27619422]
Langkammer C, Schweser F, Krebs N, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 2012; 62(3): 1593-9.
[ ] [PMID: 22634862]
Zhou D, Liu T, Spincemaille P, Wang Y. Background field re-moval by solving the Laplacian boundary value problem. NMR Biomed 2014; 27(3): 312-9.
[ ] [PMID: 24395595]
Liu MQ, Chen ZY, Bian XB, Liu MY, Yu SY, Ma L. MRI evalua-tion of lateral geniculate body in normal aging brain using quantita-tive susceptibility mapping. Chin Med Sci J 2015; 30(1): 34-6.
[ ] [PMID: 25837358]
Sun H, Klahr AC, Kate M, et al. Quantitative Susceptibility Mapping for Following In-tracranial Hemorrhage. Radiology 2018; 288(3): 830-9.
[ ] [PMID: 29916778]
Liu T, Surapaneni K, Lou M, Cheng L, Spincemaille P, Wang Y. Cerebral Microbleeds: Burden Assessment by Using Quantitative Susceptibility Mapping. Radiology 2011.
[PMID: 22056688]
Deistung A, Schweser F, Wiestler B, et al. Quantitative susceptibility mapping differenti-ates between blood depositions and calcifications in patients with glioblastoma. PLoS One 2013; 8(3)e57924
[ ] [PMID: 23555565]
Cronin MJ, Wang N, Decker KS, Wei H, Zhu WZ, Liu C. Explor-ing the origins of echo-time-dependent quantitative susceptibility mapping (QSM) measurements in healthy tissue and cerebral mi-crobleeds. Neuroimage 2017; 149: 98-113.
[ ] [PMID: 28126551]
Schofield MA, Zhu Y. Fast phase unwrapping algorithm for inter ferometric applications. Opt Lett 2003; 28(14): 1194-6.
[ ] [PMID: 12885018]
Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 2011; 54(4): 2789-807.
[ ] [PMID: 21040794]
Wei H, Dibb R, Zhou Y, et al. Streak-ing artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR Biomed 2015; 28(10): 1294-303.
[ ] [PMID: 26313885]
Shechtman O. The Coefficient of Variation as an Index of Meas-urement ReliabilityMethods of Clinical Epidemiology Springer Series on Epidemiology and Public Health Springer. Berlin: Hei-delberg 2013; pp. 39-49.
Mutsaerts HJ, Petr J, Václavů L, et al. The spatial coefficient of variation in arterial spin labeling cerebral blood flow images. J Cereb Blood Flow Metab 2017; 37(9): 3184-92.
[ ] [PMID: 28058975]
Roldan-Valadez E, Rios-Piedra E, Favila R, Alcauter S, Rios C. Diffusion tensor imaging-derived measures of fractional anisotropy across the pyramidal tract are influenced by the cerebral hemi-sphere but not by gender in young healthy volunteers: a split-plot factorial analysis of variance. Chin Med J (Engl) 2012; 125(12): 2180-7.
[PMID: 22884150]
Lopez-Mejia M, Roldan-Valadez E. Comparisons of apparent diffusion coefficient values in penumbra, infarct, and normal brain regions in acute ischemic stroke: Confirmatory data using bootstrap confidence intervals, analysis of variance, and analysis of means. J Stroke Cerebrovasc Dis 2016; 25(3): 515-22.
[ ] [PMID: 26654670]
Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005; 19(1): 231-40.
[PMID: 15705040]
Hakulinen U, Brander A, Ryymin P, et al. Repeatability and variation of region-of-interest methods using quantitative diffusion tensor MR imag-ing of the brain. BMC Med Imaging 2012; 12: 30.
[ ] [PMID: 23057584]
Aliu SO, Jones EF, Azziz A, et al. Repeatability of quantitative MRI measurements in normal breast tissue. Transl Oncol 2014; 7(1): 130-7.
[ ] [PMID: 24772216]

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Article Details

Year: 2020
Published on: 18 December, 2019
Page: [991 - 996]
Pages: 6
DOI: 10.2174/1573405615666191219102044

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