Abstract
Background: The quantitative susceptibility mapping (QSM) technique can be used to quantitatively evaluate the cerebral iron deposition of the deep gray matter structure (DGM) in clinical practice. However, it could be significantly important to assess the reproducibility of the susceptibility values at different magnetic resonance (MR) scanners before the QSM technique can be widely used in clinical applications.
Objective: This study aimed to assess the reproducibility of susceptibility value of the deep gray matter structure (DGM) at two different MR systems with the same magnetic strength.
Methods: Raw data of 21 normal subjects (M/F = 7/14, median age 29 (21, 63) years) were acquired from a 3D multi-echo enhanced gradient recalled echo sequence at two different 3.0T MR systems, and STI software was used to reconstruct the magnetic susceptibility images. Brain structural images were used to be coregistered with magnitude images to generate normalized parameters and normalized susceptibility images. Voxel-based intraclass correlation coefficient (VB-ICC) was used to evaluate the reproducibility of susceptibility value of DGM at different 3.0T MR systems.
Results: DGM with ICC > 0.75 is located in the bilateral posterior putamen and globus pallidus, bilateral red nuclei, and left dental nucleus. DGM with 0.6 < ICC < 0.75 is mainly located in the bilateral anterior putamen and globus pallidus, the margin of the bilateral red nuclei, right dental nucleus, and the margin of the left dental nucleus. DGM with 0.4 < ICC < 0.6 is located in anterior parts of the bilateral putamen, bilateral globus pallidus and substantia nigra, the margin of the bilateral dental nuclei, and the inferior part of the right dental nucleus.
Conclusion: DGM presented regional dependent reproducibility of susceptibility value at two different 3.0T MR systems based on VB-ICC analysis.
Keywords: Quantitative Susceptibility Mapping (QSM), gray matter, magnetic resonance imaging, reproducibility of results, basal ganglia, voxel-based intraclass correlation coefficient.
Graphical Abstract
[http://dx.doi.org/10.1016/j.neuroimage.2011.08.077] [PMID: 21925274]
[http://dx.doi.org/10.1016/j.neuroimage.2012.05.049] [PMID: 22634862]
[http://dx.doi.org/10.1016/S1001-9294(15)30006-7] [PMID: 25837358]
[http://dx.doi.org/10.1371/journal.pone.0081093] [PMID: 24278382]
[http://dx.doi.org/10.1002/mds.27717] [PMID: 31091347]
[http://dx.doi.org/10.1016/j.neuroimage.2014.12.043] [PMID: 25536496]
[http://dx.doi.org/10.1007/s11682-008-9059-7] [PMID: 20161183]
[http://dx.doi.org/10.3233/JAD-170118] [PMID: 28671115]
[http://dx.doi.org/10.1016/j.neuroimage.2016.04.065] [PMID: 27132546]
[http://dx.doi.org/10.3233/JAD-170601] [PMID: 29376857]
[http://dx.doi.org/10.1002/jnr.24358] [PMID: 30489648]
[http://dx.doi.org/10.1111/j.1552-6569.2008.00296.x] [PMID: 19192042]
[http://dx.doi.org/10.1212/WNL.0000000000001679] [PMID: 25979698]
[http://dx.doi.org/10.5214/ans.0972.7531.11183012] [PMID: 25205940]
[http://dx.doi.org/10.3174/ajnr.A4137] [PMID: 25339652]
[PMID: 7315877]
[http://dx.doi.org/10.1007/s12021-016-9299-4] [PMID: 27075850]
[http://dx.doi.org/10.1016/j.neuroimage.2013.05.127] [PMID: 23769915]
[http://dx.doi.org/10.1037/0033-2909.86.2.420] [PMID: 18839484]
[http://dx.doi.org/10.1007/s10072-018-3557-1] [PMID: 30209700]
[http://dx.doi.org/10.1148/radiol.2019182934] [PMID: 31909699]
[http://dx.doi.org/10.1002/nbm.3383] [PMID: 26313885]
[http://dx.doi.org/10.1002/mrm.25358] [PMID: 25044035]
[http://dx.doi.org/10.1006/jmre.1999.1873] [PMID: 10497053]
[http://dx.doi.org/10.1002/mrm.24848] [PMID: 23801540]
[http://dx.doi.org/10.1148/rg.311105041] [PMID: 21257930]
[http://dx.doi.org/10.1093/brain/aww022] [PMID: 26962053]
[http://dx.doi.org/10.1016/j.neurobiolaging.2012.10.025] [PMID: 23177595]
[http://dx.doi.org/10.1111/j.1471-4159.1958.tb12607.x] [PMID: 13611557]
[http://dx.doi.org/10.1002/jmri.27464] [PMID: 33295658]