Hunter G Moss1,2, Emilie T McKinnon1,2,3, and Jens H Jensen1,2
1Neuroscience, Medical University of South Carolina, Charleston, SC, United States, 2Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, United States, 3Neurology, Medical University of South Carolina, Charleston, SC, United States
Synopsis
The
validation of white matter (WM) tissue modeling for diffusion MRI is
challenging, in part, because some of the predicted microstructural parameters
(e.g., compartment-specific diffusivities) cannot be easily measured with
independent methods such as histology. Most WM tissue models are designed to
utilize single diffusion encoding (SDE) MRI data as provided by conventional
diffusion MRI sequences. Since multiple diffusion encoding (MDE) MRI yields
more information than SDE, it allows for tissue modeling that requires fewer
assumptions. Hence, MDE can be applied to help validate the predictions for all
SDE model parameters. Here we give an explicit example of this.
Introduction
There are several proposed tissue
models for estimating specific microstructural properties in white matter (WM) from
diffusion MRI (dMRI) data.1-7 The validation of these models is challenging, in
part, because many of the parameters they predict are difficult to measure with
independent techniques such as histology. This is particularly true for
compartment-specific diffusivities which are often among the most interesting of
the predicted quantities. One approach for meeting this challenge is to employ
multiple diffusion encoding (MDE) MRI since it generates more comprehensive
information about the diffusion dynamics than is possible to obtain with the
single diffusion encoding (SDE) MRI sequences (e.g., the Stejskal-Tanner
sequence)8 conventionally used with tissue modeling. As a
consequence, one can construct MDE tissue models that rely on fewer assumptions
than SDE models. Comparison of MDE predictions with the SDE predictions is then
a meaningful (if not definitive) test for the accuracy the SDE model. As an
example, we use a recently proposed triple diffusion encoding (TDE)9 technique to test an SDE method known as fiber ball white
matter (FBWM)10 modeling. A key difference between the two is that
FBWM idealizes diffusion in the extra-axonal water pool as Gaussian while this
assumption is not needed if TDE data is used. Although useful as a benchmark
for FBWM, as well as other tissue models, the TDE method is less practical
because the needed sequence is not widely available and requires stronger
gradients than found on typical clinical MRI systems.Methods
Three healthy adult volunteers were
recruited under a protocol approved by the MUSC IRB. TDE data
were gathered with 64 diffusion-encoding directions, an axial b-value of 4000
s/mm2, and a radial b-value of 307 s/mm2 on a Prismafit
scanner. Other imaging parameters were TE = 122 ms and TR = 3900 ms with (3 mm)3 voxels. Diffusional kurtosis imaging (DKI)11 data with matched imaging
parameters were also acquired for b-values of b = 0, 1000 and 2000 s/mm2. Ten additional b = 0 s/mm2 images were
gathered at the end of each dMRI sequence.
Raw diffusion data were processed
prior to parameter estimation using MP-PCA denoising,12 Gibbs ringing artifact correction,13 motion correction,14 Gaussian smoothing15 and Rician noise bias correction.16 Estimation of microstructural parameters for both TDE and FBWM was
performed using methods detailed in prior publications.9,10,17 Parameter values were only compared within cerebral
WM. The calculated tissue modeling parameters
were the axonal water fraction, $$$ f $$$, the intra-axonal diffusivity, $$$D_a$$$ , the extra-axonal mean diffusivity, $$$\bar{D}_e$$$ , the extra-axonal radial diffusivity, $$$D_{e\perp}$$$ , and the extra-axonal axial diffusivity, $$$D_{e||}$$$. Aggregate diffusion parameters such as mean
diffusivity (MD) and fractional anisotropy (FA) were also derived.15Results
Figure 1 shows an axial slice from one volunteer
comparing the modeling parameters estimated from the FBWM and TDE techniques.
The WM voxels are colored indicating where the methods are applicable. Qualitatively similar results are seen in deep WM regions but some
discrepancies are apparent particularly in edge voxels where gray matter/cerebrospinal
fluid (CSF) partial volume effects may be expected. This is most clearly seen in
the $$$ D_a $$$ maps which show considerably more spatial
variations for FBWM.
Histograms of the voxel counts within
WM are overlaid for both TDE and FBWM in Figure 2. A remarkable overlap of the
histogram values is seen with the mean values for the two technique differing
by no more than 7%. However, the spread in $$$ D_a $$$ values for FBWM is substantially
larger than for TDE, while the spread in $$$\bar{D}_e$$$ is substantially smaller for TDE.
Figure 3 shows the average parameter
values for each subject as a function of fractional anisotropy (FA) (bin size =
0.1). A striking similarity is again seen between the two methods in the low to
medium anisotropy regimes. Noticeable differences do occur in the higher
anisotropy regions, especially for $$$ D_{e||} $$$.
To elucidate potential sources
for the observed differences, $$$ D_a $$$ as
a function of mean diffusivity (MD) is shown in Figure 4 for both TDE and FBWM.
It is seen that $$$ D_a $$$ is strongly correlated with MD for FBWM
and not for TDE, suggesting that CSF partial volume effects may be contributing
to the higher spatial variability seen with FBWM. This is plausible because the
TDE approach is expected to be much less sensitive to CSF contamination.
Discussion
Both intra- and extra-axonal WM
tissue modeling parameters were quantitatively compared between TDE and FBWM
with good congruence is being observed. Some observed discrepancies may be due
to a greater sensitivity to partial volume effects for FBWM. Although the MDE
predictions are not true gold standards, they are based on fewer assumptions
than the SDE predictions and can thus serve as a meaningful reference. Overall,
our results support the accuracy of FBWM especially in deep WM where consistency
with TDE was best.
The approach used here illustrates how SDE tissue
models can be tested with MDE MRI, which provides information on diffusion
properties that other commonly used validation techniques, such as histology,
are unable to assess. Since TDE predicts the full intra-axonal and
extra-axonal diffusion tensors, it could also be used to test SDE MRI methods
other than FBWM. Acknowledgements
NIH NINDS funding: F31NS108623
(to H. Moss)
NIH funding: T32DC014435
(to J. Dubno)
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