Ning Ning1, Peng Wu2, Xianjun Li3, Yajie Hu3, Weishan Zhang1, Lei Zhang1, Sung-Min Gho4, Dong-Hyun Kim4, Hua Guo2, and Jian Yang1,3
1Department of Diagnostic Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China, People's Republic of, 2Department of Biomedical Engineering, Tsinghua University, Beijing, China, People's Republic of, 3Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China, People's Republic of, 4Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
Synopsis
To observe the age-related susceptibility changes
in the deep gray nuclei and assess the superiority of the quantitative
susceptibility mapping(QSM) and effective transverse relaxation rate(R2*) for quantifying
the iron deposits in children. 87 subjects(1M-6Y) were enrolled. The susceptibility in QSM and R2*
values exhibited positive correlations with age and the
reference iron concentrations calculated using an empirical equation. The
correlation of the susceptibility with the iron is higher than the R2* with it.
QSM may provide a more promising and reliable tool for assessment of iron
content in children’s deep gray nuclei, even in the regions with lower iron
content.Purpose
Iron plays an
important role in brain development and metabolism, so estimating the iron deposition may be important to assess neural development and diseases for children
1. The
age-related susceptibility changes may provide valuable information regarding
the iron deposition state of the human brain. The quantitative susceptibility
mapping (QSM) and effective transverse relaxation rate (R2*) are recognized as
effective quantitative methods to measure the iron deposition in deep gray
nuclei in adults
2. The purpose of
this study is to observe the age-related susceptibility changes in the deep
gray nuclei and assess the superiority of the two methods for quantifying the brain
iron deposits in children.
Methods
87 young children (1M-6Y) without abnormalities in brain MR
images were enrolled in this study and examined with informed consent from parents
according to local ethics procedures. They were divided into 5 groups according
to the age (1M-≤6M, >6M-≤1Y, >1Y-≤2Y, >2Y-≤3Y, and >3Y-≤6Y). A 3D
gradient-echo sequence of enhanced T2* weighted angiography (ESWAN) was
employed on a 3.0T MR system (GE Signa HDxt). TR=51ms, number of echoes=6, TE=6~60ms,
FA=20°, slice/gap=2mm /0mm, NEX = 0.69, FOV=18×18cm
2, matrix=256×256.
For each subject, we performed a fitting of the data acquired at the 6 TEs to
obtain a monoexponential signal decay curve (i.e. $$$S\left(t\right)=S0\times\exp^{-tR2*}$$$,
where S=measured data, S0=multiplicative constant, t=echo time). QSM
was obtained by the modified SHARP
3 and LSQR method
4. Regions
of interest (ROIs) were outlined manually in caudate nucleus (CN), putamen (PUT),
globus pallidus (GP) and thalamus (THA) (Fig 1). Susceptibility in splenium of
the corpus callosum was taken as the reference. Same ROIs were used to for both
QSM and R2* images. Susceptibility in QSM and R2* phase values were calculated
and correlated with the ages and the reference iron concentrations. Note
these reference values of iron concentrations were estimated from age using an empirical
equation that was derived in an earlier postmortem study
5.
Results
Representative QSM susceptibility maps and
R2* maps with different age are shown in Fig 2. The susceptibility in QSM and R2*
values of various deep gray nuclei exhibited significantly positive
correlations with age especially in the GP (Fig. 3). The susceptibility and R2*
values in 5 age groups are shown in Table. Both of the susceptibility and R2*
values showed the strongly positive correlation with the iron content (P <
0.05) (Fig 4). The coefficient of correlation between the iron and the susceptibility
is higher than the one with the R2* in each region.
Discussion
In brain, non-heme iron that presents
sufficient concentration to affect MR contrast resides in ferritin or hemosiderin
molecules, leading to susceptibility and R2* increase. R2* maps showed good
contrast just between GP and surrounding tissues but very weak contrast between
gray and white matter. In comparison, QSM showed not only good contrast between
gray and white matter but also between iron-rich nuclei and surrounding tissues,
which indicated QSM is more intuitive to reflect the susceptibility development
of deep gray nuclei
2. Both of the susceptibility and R2* values
showed the strongly positive correlation with the iron content. The higher correlation
with the iron in deep gray nuclei was found by QSM, although the correlation is
similar in the GP. Thus, the susceptibility in QSM would be more sensitive and
objective than R2* even in the regions with lower iron content. However, the
R2* values have markedly higher positive correlation with the age than
susceptibility of QSM, except in the GP with highest iron content. It may be interpreted
that R2* value could be affected by iron and other age-related factors such as gradually
decreased water content
6 in
the gray matter simultaneously.
Conclusion
It suggested that QSM may provide a more promising
and reliable tool for assessment of iron content in children’s deep gray nuclei,
even in the regions with lower iron content.
Acknowledgements
New Century Excellent Talent Support Plan from Ministry of Education of China (NCET-11-0438); the National Natural Science Foundation of China (81171317); the National Natural Science Foundation of China (81471631) from Dr. Jian Yang.References
1. Morris CM. Any
old iron? Brain. 2011; 134(Pt 4):924-927.
2. Li W, Wu B,
Batrachenko A, et al. Differential developmental trajectories of magnetic
susceptibility in human brain gray and white matter over the lifespan. Hum
Brain Mapp. 2014;35(6):2698-2713.
3. Schweser F, Deistung A, Lehr BW, et al. Quantitative imaging of intrinsic magnetic tissue
properties using MRI signal phase: an approach to in vivo brain iron
metabolism? Neuroimage. 2011;54(4):2789-2807.
4. Li W, Wu B, Liu
C. Quantitative susceptibility mapping of human brain reflects spatial
variation in tissue composition. Neuroimage. 2011;55(4):1645-1656.
5. Hallgren B,
Sourander P. The effect of age on the non-haemin iron in the human brain. J
Neurochem. 1958; 3: 41-51.
6. Lee J, Shmueli K, Kang BT, et al. The contribution of myelin to magnetic
susceptibility-weighted contrasts in high-field MRI of the brain. Neuroimage.
2012;59(4):3967-3975.