Jingu Lee1, Woojin Jung1, Yoonho Nam2, and Jongho Lee1
1Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of
Synopsis
In this work, we
measured the effect size of both myelin concentration and fiber orientation in
R2*. Additionally, we generated the myelin concentration and/or
fiber orientation bias free R2* maps which may have important
applications.Purpose
R
2* (=1/T
2*) is influenced by various sources such as magnetic
susceptibility, chemical shift, and microstructural anisotropy.
In white matter of the brain, it has been demonstrated that R
2* is
affected by fiber orientation relative to B
0 [1, 2] and
myelin concentration [3]. However, the portions of R
2* that the two
sources explain (i.e. the effect size) have not been explored simultaneously. In
this study, we measured the contribution of fiber orientation and myelin
concentration to R
2* quantitatively to demonstrate that the two
sources are primary determinant for R
2*. Additionally, we proposed
novel R
2* maps that are corrected for fiber orientation and/or
myelin concentration biases. The bias-corrected R
2* maps and histograms
may serve as reliable data that discriminate patients from healthy controls.
Methods
Three
healthy volunteers were scanned at 3T. [R2*
map] 3D multi-echo GRE was acquired with following parameters: FOV=256×256×104mm3,
voxel size=2×2×2mm3,
TR=67ms, TE=1.5:2.03:32ms
(16 echoes). For R2* mapping, a mono-exponential function was fitted
to three different echo periods: early echoes (first 8 echoes), late echoes (last
8 echoes) and all echoes. [Myelin concentration map] The
multi-echo GRE data were reprocessed for GRE myelin water imaging (MWI),
generating a myelin water fraction (MWF) map as a marker for myelin
concentration. The process utilized a three pool complex model as summarized in
[4]. [Fiber orientation map] To
estimate fiber orientation, DTI was acquired. The resulting color DTI FA map
was processed to generate a map of relative angles between fibers and B0.
[R2* models] The R2*
signals were modeled:
Myelin
concentration model: R2*(MWF) = a0 + a1·MWF
Fiber
orientation model: R2*(θ) = b0 + b1·cos2θ+b2·cos4θ
Complete
model: R2*(θ,MWF) = c0 + c1·MWF+c2·MWF·cos2θ+c3·MWF·cos4θ
where
θ
is the angle in the fiber orientation map and MWF is from MWI. A white matter
mask was generated using thresholds in FA (>0.4). To improve reliability, the fiber
orientation was quantized with a step size of 10° and MWF
with a step size of 2% (3.6% for the complete model). The MWF values larger
than 18% were discarded due to a small number of voxels. The
resulting data were fitted to each model, generating the coefficients. This
approach may cause a bias due to non-uniform distribution of MWF over the fiber
orientations but does not require multiple scans with different head
orientations [5]. After the model fitting, R2* plots, R2*
maps and histograms that removed the effects of each model were displayed.
Results
Both
MWF and fiber orientation showed significant effects on R2* (Fig. 1),
confirming the previous findings [1-3]. When quantified, the MWF change from 0%
to 18% showed 5.09±0.06Hz change whereas the orientation dependent R2*
showed 3.29±0.92Hz change. These results suggest that the myelin concentration
has 1.54±0.29 times larger effect than the fiber orientation. When the effect
of fiber orientation was corrected (Fig. 1C), the linear fit of MWF improved (R2
from 0.973 to 0.981). The resulting map showed a less biased R2* map,
revealing reduced R2* contrasts for different directional fibers (white
circles; Fig. 3B vs. 3C). When
MWF was removed, the resulting plot revealed abnormally small R2* in
perpendicular orientation (Fig. 1D). This change is not physically and can be explained
by a large number of perpendicular fibers with high myelination (which was
confirmed). Hence the resulting MWF corrected map (Fig. 3D) may contain
overcorrection of MWF. When the full model was fitted (Fig. 1G), the resulting R2*
showed no fiber orientation and no MWF dependency (Fig. 1E, 1F). The resulting
map (Fig. 3E) revealed relatively uniform R2* across white matter compared
to the other maps. This map may reflect other susceptibility sources (e.g.
iron). The histogram also demonstrated the effects of the bias corrections in
each model, generating a more Gaussian distribution after compensating for both
effects (Fig. 5).
In
this work, the R2* estimation used a single exponential decay
fitting and is sensitive to the choice of echo times. The early echoes had larger
weighting of the myelin signal and showed larger orientation effect (Fig. 4).
The opposite was true for the late echoes. Hence, one may choose echo times based on
the interests of the study. Similar results were observed in the R2*
histograms, showing a larger bias toward high R2* values in the
early echoes (Fig.5 black lines).
Discussion
and conclusion
In
this work, we measured
the effect size of myelin concentration and fiber orientation in R
2*,
demonstrating myelin concentration has 1.54 times larger effect than fiber
orientation. Additionally, we generated the myelin concentration and/or fiber
orientation bias free R
2* maps which may have important applications.
Acknowledgements
This research was supported by the Brain Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015M3C7A1031969).References
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van Gelderen, Li-Wei Kuo, et al. T2*-based fiber orientation mapping,
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