Kleban, E., Jones, D. K., & Tax, C. M. W. (2023). The Impact of Head Orientation with Respect
to B0 on diffusion tensor MRI Measures. Imaging Neuroscience, Advance Publication.
https://doi.org/10.1162/imag_a_00012
The Impact of Head Orientation with Respect to B0 on
diffusion tensor MRI measures
Elena Kleban1,2, Derek K Jones1,3, Chantal MW Tax4,5
1CUBRIC, School of Psychology, Cardiff University, Cardiff, Vereinigtes Königreich
2Inselspital, University of Bern, Bern, Schweiz
3MMIHR, Faculty of Health Sciences, Australian Catholic University, Melbourne,
Australia
4CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, Vereinigtes Königreich
5UMC Utrecht, Utrecht University, Utrecht, Die Niederlande
Abstrakt
Diffusion tensor MRI (DT-MRI) remains the most commonly used approach
to characterise white matter (WM) anisotropy. Jedoch, DT estimates may be
affected by tissue orientation w.r.t. due to local gradients and intrinsic
orientation dependence induced by the microstructure. This work aimed to
investigate whether and how diffusion tensor MRI-derived measures depend on the
orientation of the head with respect to the static magnetic field, . By simulating
© 2023 Massachusetts Institute of Technology. Veröffentlicht unter Creative Commons
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WM as two compartments, we demonstrated that compartmental anisotropy can
induce the dependence of diffusion tensor measures on the angle between WM
fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and
increased axial diffusivity were observed in white matter fibres perpendicular to
compared to those parallel to . Fractional anisotropy varied by up to as a
function of the angle between WM fibres and the orientation of the main magnetic
field. To conclude, fibre orientation w.r.t. is responsible for up to variance
in diffusion tensor measures across the whole brain white matter from all subjects
and head orientations. Fibre orientation w.r.t. may introduce additional variance
in clinical research studies using diffusion tensor imaging, particularly when it is
difficult to control for (e.g. fetal or neonatal imaging, or when the trajectories of
fibres change due to e.g. space occupying lesions).
Schlüsselwörter: Diffusion Tensor Imaging, Magnetic Resonance Imaging,
Transverse relaxation, orientation anisotropy, fibre direction
1. Einführung
MRI can provide invaluable information on tissue composition and structure
in vivo through the manipulation of spins with magnetic fields. Several MRI
2
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contrasts have shown a dependence on tissue orientation w.r.t. the main magnetic
field direction ( ), einschließlich
, , and magnetisation transfer [1–19].
Orientation-dependence of the apparent
in adult white matter (WM) hat
primarily been attributed to local magnetic susceptibility-induced gradients from the
myelin sheath, and as such can provide valuable information on its condition in
Gesundheit und Krankheit [10, 19]. Zusätzlich, recent work [20] found that orientational
anisotropy of transverse relaxation rates in newborn WM, with a much lower degree
of myelination, followed the pattern of residual dipolar coupling. Recent works have
demonstrated different orientational behaviours of -estimates in intra- Und
extra-axonal microstructural WM compartments [21, 22], see also Appendix A.
In diffusion MRI (dMRI) typically only the orientation-dependence on
externally applied spatial gradients is considered: it sensitises the signal to the
diffusion of water molecules in one or multiple directions by deliberately applying
magnetic field gradients, and as such can infer information on the directional
organisation of tissue. At low to moderate diffusion weightings, the diffusion tensor
MRT (DT-MRI) representation [23] remains the most commonly used approach to
characterise the diffusion process, and DT-MRI-derived measures such as mean
diffusivity (MD) and fractional anisotropy (FA) reflect both intra-and extra-axonal
signal contributions.
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Theoretically, dMRI signals and derived measures can also exhibit
-orientation dependence when magnetic susceptibility variation is combined with
anisotropic geometry at a subvoxel level. Several mechanisms may contribute to
dMRI-signal-anisotropy in this case. zuerst, several works have considered the
interaction (or cross-term) of susceptibility-induced gradients with the externally
applied diffusion encoding gradients, and their effect on estimates of the apparent
diffusion coefficient (ADC) [15, 24–29]. Speziell, local gradients in the
direction of the diffusion encoding gradient can lead to an under- or overestimation
of ADC from individual isochromats, leading to a reduction of the overall ADC
because isochromats with reduced ADC contribute a higher weighting [24]. Von
employing sequences sensitive and insensitive to local susceptibility-induced
gradients, early ex vivo experiments in WM [26, 30] concluded that the effects from
local gradients on diffusivity values did not have a measurable role in nerve samples
at 4.7T and 2.35T, jeweils, which was later corroborated in vivo at 1.5T [27].
Interessant, [26] did observe that diffusivity values along the axon varied by about
15% due to reorientation w.r.t. . In silico works provided theoretical background
on the effect of mesoscopic susceptibility on ADC and DT-derived measures under
variable diffusion times [29] and sample orientation [15], jeweils. Außerdem,
the recent observation of differences in compartmental -anisotropy suggests
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another mechanism of -orientation dependence in DT-measures. The intrinsic
-weighting of
the diffusion-weighted spin-echo sequence affects
Die
-weighting of intra- and extra-axonal signal fractions. Infolge, Unterschiede in
compartmental
-orientation
dependence w.r.t.
can
führen
Zu
orientation-dependent variation
in compartmental
signal
fractions and,
consequently, affect DT-measures.
This motivates further investigation of the potential orientational dependence
of DT measure w.r.t. . The additional dMRI dependence on tissue-orientation
w.r.t. may introduce variability in the results when not taken into account,
potentially reducing statistical power to detect true effects, and could even provide
important additional information on tissue microstructure (e.g. myelin). The aim of
this work is to determine the variation of DT-MRI-derived measures as a function of
fibre orientation w.r.t. . Zu diesem Zweck, we investigate the effect of head-orientation
dependence of compartmental [21] Und
the consequent variation of
compartmental signal fractions on DT-MRI measures in silico, and characterise the
-orientation dependence in in vivo human brain data at 3 T using a tiltable RF coil.
2. Methoden
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2.1. Simulations
The following simple simulations investigate the effect of -orientation
dependence of compartmental [21] on estimated DT-MRI measures, thereby not
considering cross terms between the diffusion and background gradient. Der
simulations are based on a ‘standard model’ of diffusion for white matter in the
long-time limit, which models the intra-axonal space as a ‘stick’ with zero
perpendicular apparent diffusivity and the extra-axonal space as axially symmetric
tensor [31–34]. Different levels of complexity are investigated: Erste, in the case of
no fibre dispersion and no noise, one can derive analytical equations for the ADC as
a function of compartmental diffusivities, signal fractions, and compartmental
(which can be -orientation dependent). Zweite, still in the case of no dispersion,
the signal can be generated from analytical equations, noise added, and the DT
fitted. Endlich, this can be repeated for signals generated in the case of fibre
dispersion.
For all simulations, scenarios for a range of (d.h., orientation w.r.t. )
were generated corresponding to the distribution of observed in the in vivo data of
all subjects and head orientations (siehe Sektion 2.2).
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Analytical case: no dispersion and no noise. Consider a simplified
two-compartment model of the diffusion- and relaxation-weighted signal in WM
(no fibre dispersion) as a function of the echo time, , and -value:
(1)
where subscripts i/e denote intra-/extra-axonal compartments, jeweils,
are the relaxation rates, are positive semi-definite diffusion tensors,
and is intra-axonal signal fraction. Suppose and have equal principal
eigenvectors (denoted by ) and parallel and perpendicular eigenvalues
(Wo ) and respectively, then the signal can be simplified as
(2)
Wo .
Considering DTI as a signal representation at sufficiently low -values, i.e.
capturing the first order -term in the Cumulant expansion [35], one can derive
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expressions for the ADC, e.g. by expanding in powers of the analytic expression
für (Eq. 2). For non-interacting compartments, the diffusion coefficient is a
weighted sum of the diffusivities in the individual compartments where the signal
fractions are -weighted. Speziell, the ADC is the first order term of the
Maclaurin series expansion of in :
(3)
Eq. 3 was used to compute apparent axial diffusivity (AD, ), radial diffusivity
(RD, ), MD, and FA. Recent work suggests that the effect of WM fibre
orientation to the magnetic field can most prominently be observed in the
extra-axonal apparent transversal relaxation rate [21]. Der
dependence could be described as
(4)
This orientational dependence of will result in orientational dependence of
the ADC in addition to a straightforward TE dependence.
Analytical noiseless scenarios were simulated using Eq. 3 Und 4. TEs were
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selected to match the in vivo acquisition (cf., section 2.2.1). The axonal fraction was
varied and diffusivities and relaxation rates were
set to the following values: , Und
, , jeweils.
Noise simulations without dispersion. Eq. 2 was used to simulate signals
with and matching the in vivo data section 2.2. Signals were simulated
using the same fractions, intra- and extra-axonal diffusivities and relaxation rates as
for the analytical simulations. Rician noise was added to the signal with an SNR of
100 on the , signal, similar to the in vivo acquisitions [21]. DT were
estimated for each on data using iterative weighted linear
least squares, and AD, RD, MD, and FA were computed.
Noise simulations with dispersion. Endlich, the effect of fibre orientation
dispersion was studied by forward simulating a distribution of orientation-dispersed
compartments according to a Watson distribution, where each sub-compartment (i.e.
each distinctly oriented extra-axonal compartment) can separately exhibit
-orientation dependence [21, Appendix A]. Tissue properties, noise, Und
estimation were as described in the simulations without dispersion.
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Data analysis. To quantify the magnitude of orientation dependence, , Die
simulated values of each DTI-derived measure at each TE were directly represented
by a function of :
(5)
We note that this representation does not exactly describe the orientation
dependence even in the simplest analytical case (Eq. 3), but nevertheless provides a
close approximation (see an example of a -fitting in supporting Figure S1) Und
allows for the quantification of anisotropy through the estimation of . Der
performance of the anisotropic representation relative to the isotropic case,
, was estimated using the rescaled Akaike’s Information Criterion (AIC)
[36, 37]: . Hier, is the minimal AIC value in the set.
Per [37], values allow comparison of the relative merits of representations in
the set as follows: representations having are considered to have similar
substantial support as the representation with , those with
have considerably less evidence, and those with have no support.
Zusätzlich, the isotropic model is selected over the anisotropic, if the
confidence interval of the magnitude of anisotropy included zero [38].
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2.2. In vivo data
In this work we used a subset of the multi-dimensional diffusion- Daten
presented in previous work [21], relevant data acquisition and pre-processing steps
are re-iterated below. The study was approved by the Cardiff University School of
Psychology Ethics Committee and written informed consent was obtained from all
participants in the study.
2.2.1. Data acquisition.
Multi-dimensional diffusion- -weighted data were acquired from five
healthy participants (3 weiblich, 25-31 y.o.) on a 3 T MRI scanner equipped with a
300 mT/m gradient system and a 20ch head/neck receive coil that can tilt about the
L-R axis (Siemens Healthineers, Erlangen, Deutschland). The acquisition was repeated
in default ( ) and tilted ( ) coil-orientation to introduce variable anatomical
orientation w.r.t. . Acquisition parameters are summarised in Figure 1A.
2.2.2. Data processing.
The data were checked for slice-wise outliers [39] and signal drift, corrected
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for Gibbs ringing [40], subject motion, geometrical distortions [41–43] and noise
bias [44–47].
From the pre-processed data a subset with diffusion weightings matching
across echo times was selected (Figure 1B), and for each echo time diffusion tensors,
fibre orientation w.r.t. and single fibre population masks were obtained as
described below. DT were estimated for each on the nominal
Daten, using iterative weighted linear least squares. Gradient
non-linearities were considered and -values/-vectors were corrected
correspondingly prior to fitting [48]. Fibre orientations w.r.t. were computed
from the first eigenvector of the estimated DT. Note that has to be in image
coordinates of each subject/head orientation.
Fibre orientation distribution functions (fODF) [49, 50] were estimated per
using multi-shell multi-tissue constrained spherical deconvolution [51] von dem
data acquired at . From the fODFs single-fibre population (SFP) voxels
with low dispersion ( ) were identified [52]. Dispersion was quantified by
, where are spherical harmonics coefficients [21, 53,
54].
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We used WM tract segments extracted in previous work [21]. Briefly, 18
major WM tracts and, where applicable, their bilateral counterparts were extracted
and segmented using TractSeg [55].
2.2.3. Data analysis.
-dependence of DT measures: pooling all SFP voxels. General trends in
orientational anisotropy of DTI measures were investigated by subdividing the
range of angles into bins, averaging the estimates within each bin, and smoothing.
Speziell, the data were binned in -subsets and the corresponding DT-measure
estimates and -values were averaged across each bin, denoted as and
. Dann, a smoothing spline as a function of and weighted by the number
of data points in each bin was fitted to . An example of this
procedure is shown in supporting Figure S2 for the lowest TE.
The magnitude of anisotropy was defined as the difference between the
minimal and the maximal values of the fitted curves. Their signs were set negative if
the minimal values were below those at . The contribution of orientational
anisotropy to overall variance was calculated as: . Hier,
and are the standard deviations across all SFP voxels with and
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without orientational anisotropy being considered, jeweils. Zusätzlich, mean
values across all SFP voxels were obtained for each measure and TE.
In addition to the spline-analysis, to assess whether DT measures as a function
of showed significant orientation-dependence, we assessed whether an
anisotropic representation described the data better than isotropic (cf., Supporting
Information), using the approach similar to the in silico analysis described in section
2.1.
-dependence of DT measures: tractometry analysis to achieve spatial
correspondence. By comparing the measures estimated within the same anatomical
region at default or tilted coil orientation, we aimed to reduce the effects of the
potential microstructural variability across the WM in the approach described above.
The anatomical correspondence between the coil-orientations was established using
the segments derived from the tractometry approach. The outer-most 20% of tract
segments and the segments with 3 or fewer voxels were excluded to minimise the
effects of fanning and noise, jeweils. To obtain the effect of the re-orientation
we evaluated as a function of
. Hier, denotes the average of corresponding values from SFP voxels
over each segment, and the subscripts and correspond to default and tilted
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head-orientations, jeweils.
3. Ergebnisse
3.1. Simulations
Figure 2AB shows examples of MD, AD, RD, and FA as a function of fibre
orientation to for the noiseless analytical simulations without fibre
dispersion. For the parameter settings investigated, AD and FA increase with (Die
magnitude of anisotropy, ), while RD decreases ( ). The absolute value
of the magnitude of anisotropy, , generally increases with . The resulting
behaviour of MD is non-trivial and sensitive to simulation parameters (z.B., axonal
signal fraction ), with possible sign flips of for increasing .
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A. Multi-dimensional -diffusion data were acquired under simultaneous
modulation of echo times and diffusion-gradient amplitudes in a pulsed-gradient
spin-echo sequence with EPI readout. Time between diffusion gradients,
MS, and diffusion gradient duration, MS, were kept fixed for all echo
mal. The gradient orientations were defined in scanner coordinates and thus were
not rotated with the head re-orientation. Additional modulation of fibre orientation
was achieved by head re-orientations relative to .
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B. A subset of the pre-processed multi-dimensional diffusion- -weighted dataset
from previous work[21] was used to calculate echo-time-dependent diffusion
tensors and fibre orientation to (denoted by ) (Blau, bottom left), Und
single-fibre-population (SFP) voxels (Grün, top left).
Figur 1: Methoden
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Figur 2: Simulation results. The signals were estimated for variable echo times
and axonal fractions, , and fixed diffusivities
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, and relaxation rates , . Figures in A. and B.
were estimated analytically (Equations 3 Und 4) and show MD, AD, RD, and FA as
functions of fibre orientation w.r.t. für , Und , jeweils. In
C. the magnitude of anisotropy, , (colors) is shown as a bi-modal function of the
Echozeit (horizontal axis) and the axonal fraction (vertical axis,
). Columns left-to-right are different DTI measures: MD, AD,
RD, FA; rows top-to-bottom are different simulation conditions: using the analytical
Ausdruck, assuming noisy signal with , and adding fibre dispersion
( ) in addition to noise, jeweils.
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A. Each DTI measure (rows) from SFP voxels was plotted against the fibre
orientation, , to the magnetic field. Each column/colour corresponds to a different
DER. Solid lines represent best fitting smoothing spline curves. Dashed red lines
indicate the magic angle of .
B. The estimated mean value, , and the magnitude of anisotropy, , over all SFP
voxels are shown in the first and the second column, jeweils. Third column
shows the amount of decrease in variation of values when orientation w.r.t. Ist
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taken into account. Colours represent the corresponding echo times, for which
anisotropy of the measures was investigated.
Figur 3: Pooled SFP data results.
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Figur 4: Tractometry results. Tractometry was used to achieve anatomical
correspondence between tilted and default head orientation, by comparing values of
DTI-measures in default vs tilted head orientations tract- and segment-wise. A. In
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scatterplots, changes in value of the respective DTI measure with re-orientation
(rows) are plotted as a function of the corresponding change in . B. Barplots
show: the magnitude of anisotropy estimated for each DTI measure and each echo
Zeit (top row); and the change in standard deviation (std) when fibre anisotropy is
taken into account (bottom row). Data in which anisotropic representation (
) described the data better ( ) than isotropic assumption
( ) were indicated by a *-symbol.
Figure 2C shows results for the analytical simulations following Eq. 3 Und 4
(first row), and the noisy simulations without (middle row) and with (dritte Reihe) fibre
dispersion. The plots show the estimated anisotropy (colormap) for the scenario
i m ms, e m ms, and e m ms, echo times matching
the acquisition parameters (horizontal axis) and a range of (vertical axis). Der
columns show results for different DT measures. A grey colour indicates scenarios
for which an isotropic representation was favoured (section 2.2.3). It becomes
immediately apparent that the effect on DT measures can be vastly different
depending on the scenario: in the simple analytical simulations for MD can either
be positive (hoch ) or negative (niedrig ) depending on the intra-axonal signal
fraction and its absolute value becomes larger for increasing . For the simulation
with noise and no dispersion can be positive or negative, and in the case of
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dispersion is lower and negative in the cases investigated. For AD, Ist
predominantly positive in non-dispersion analytical scenario and has the largest
value for high , but in the noisy simulations could be negative. The behaviour
of is more consistent across simulation scenarios. Whereas is mostly
positive and largest for high and low in the no-dispersion noiseless and noisy
Fälle, can be positive or negative in the noisy scenarios but is overall low or
non-significant.
3.2. In vivo data
Pooled data. In Figure 3A DT measures are plotted as functions of fibre
orientation w.r.t. (horizontal axes), and echo time (columns), along with
the corresponding smoothing spline curves highlighting anisotropic effects. Der
data were pooled from all subjects and both head orientations, each data point
represents one SFP voxel. RD and FA show global maxima and minima,
jeweils, closed to the magic angle (dashed red lines), most prominently for low
DER.
The barplots in Figure 3B show the average value ( , first column) or the
magnitude of anisotropy ( , second column) obtained from all SFP voxels for a
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given measure (rows). MD, AD and FA increase as a function of ( ), while
RD decreases ( ). The anisotropic component is least dependent on the echo
time for axial diffusivity. For other measures is non-monotonic (für
evaluated TE-s) with its absolute value being minimal (for MD) or maximal (RD,
FA) at around 75-100 MS. The fibre-orientation-independent component (Erste
column, Figure 3B) evolves non-monotonically as a function of TE. The relative
range of change of DT-measures across angles (computed as , results not
shown) can reach values up to 20%. Endlich, column three of Figure 3B shows the
fraction by which anisotropy effects contribute to overall variance, showing the
largest contribution for AD (around at ms). For MD, RD, and FA the
variance contribution was , , Und , jeweils, at the same shortest echo
Zeit
We also observed an overall similar behaviour in magnitude of anisotropy
when the pooled data were evaluated using sin -representation instead of the
spline-fit (cf., supporting Figure S3).
Segment-wise comparison. The scatterplots
in Figure 4A show
segment-wise differences between the values in tilted and default head orientation of
each measure (rows) against the sin of fibre orientations w.r.t. . Each column
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and the corresponding colour of the linear fit represent different echo
mal. The fitting results are summarised in the top row of Figure 4B, and the
fraction of variance contributed by the anisotropy effects is in the barplots of the
bottom row.
Compared to the pooled analysis, the sign of anisotropy was the same
(positive for MD, AD, and FA and negative for RD), but the trend as a function of
TE was different for the segment-wise analysis (e.g. the magnitude of anisotropy
in RD increased with echo time whereas the pooled analysis showed a decrease
for the largest echo times).
4. Diskussion
We used diffusion- -correlation data acquired in two head-orientations using
a tiltable coil [21] to achieve a larger range of orientations and investigate the effect
of head-orientation on diffusion tensor measures: mean, axial and radial
diffusivities, and fractional anisotropy. We observed that fibre orientation w.r.t.
may be responsible for up to three, seven, and two percent of variance in MD, AD,
and RD, jeweils, at ms and about four percent of variance in FA at
the same TE. We also utilised tractometry to achieve anatomical correspondence
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and used the sin -representation to estimate the effect of head-reorientation.
4.1. TE-dependence of DTI-measures.
Echo-time-dependence of diffusion coefficients and DT-derived measures
has long been recognised. [56] have reported an increase/decrease of ADC with
longer when diffusion weighting was applied parallel/perpendicular to the rat’s
trigeminal nerve. These are in correspondence with analytical observations
visualised in e.g. Figure 2A: axial diffusivity, AD, erhöht sich, while radial diffusivity,
RD, decreases with longer echo times. Assaf and Cohen [57] performed diffusion
experiments with variable echo time to demonstrate the presence of two distinct
diffusing compartments, they also found that the signal of the slow diffusing
component has a lower relaxation rate. Das, wieder, would correspond to the
decrease in radial diffusivity with longer TE. Endlich, Qin et al. [58] have explored
DTI measures as functions of echo time in rhesus monkey internal capsule. Sie
similarly reported a decrease in the radial and increase in axial diffusivities with
longer TEs, but also an increase in fractional anisotropy and no significant changes
to the mean diffusivity. Lin et al. [59] made similar observations for the human
corpus callosum and internal capsule, in addition they observed no TE-dependence
of AD in the corpus callosum.
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In our data, which were pooled from WM SFP voxels, we did not observe any
linear trends (vgl. isotropic representation, , Figure 3B), the non-monotonic
variations of the DTI-measures could be due to the variability of each measure as a
function of TE between SFP voxels. Zusätzlich, the much noisier data at longer
TEs could also have contributed to these differences. Noch, for echo times ms
we observed a decrease in RD and an increase in FA, which agree with observations
made by Qin et al. [58] and Lin et al. [59], and similar to the latter we saw no
significant changes in AD.
From the same data compartmental transverse relaxation rates were
previously estimated [21], and faster extra-axonal signal decay was observed, welche
is in correspondence with previous findings [56, 57, 60, 61].
4.2. Head-orientation dependence of DT measures
Orientational anisotropy of DT measures observed in vivo and in silico.
We estimated non-zero magnitude of orientation anisotropy in all DTI measures
with both methods: pooled SFP voxels, and tract-segment-wise comparison between
default and tilted head orientations. Under the assumption of sin -behaviour, Die
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correspondence in estimated magnitude of anisotropy between the two methods was
higher at shorter echo times of ms and ms, the accuracy at longer echo times
was potentially compromised by decreased SNR. Ähnlich, the contribution of
anisotropy effects to the variance of DTI measures decreased with increasing echo
Zeit. Comparing the spline with the sin -representation in the pooled results, Die
absolute values of obtained using spline fitting were subtly higher than those
estimated using the sin -approximation, but overall followed the same trend as a
function of TE.
The in vivo RD and MD estimates as a function of followed trends also
seen in the analytical simulations, i.e. positive for AD and FA and negative for
RD. Jedoch, also opposite signs for were observed in the noisy simulations,
e.g. in AD. This could not merely be caused by e i (the opposite was
simuliert), but it is hypothesised that this could be attributed to the complexity of
tissue (e.g. dispersion, a distribution of diffusivities and within and across voxels
in the in vivo results, and other origins of orientation dependence) and different
levels of noise, amongst others. One can also observe that the estimated of AD
decreased as function of in vivo in contrast to the increase in the toy-example.
Origin of anisotropic effects of DTI in WM. The simulations considered the
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effect of -weighting and different -anisotropy behaviour in the intra- Und
extra-axonal space on DT measures, assuming a dominant role for myelin
susceptibility effects in the extra-axonal space. Jedoch, the origin of the
orientation dependence may be more complex. In in vivo data both the sin
behaviour and a more general spline representation were used to investigate the
-dependence,
indeed resulting
in a similar estimated contribution of
orientation-dependence to the variance of DT measures in the pooled analysis and
similar magnitudes of anisotropy. This similarity partially supported the assumption
made in simulations, d.h., that the difference in -dependence between the
intra- and extra-axonal signals (i.e. sin -dependence in the extra-axonal space) ist ein
major contributor to the orientational anisotropy. Yet the behaviour of the spline
curves deviates from the typical sin -shape which indeed suggests that the nature
of anisotropy must be more complex.
The hypothesis that self-induced gradients arising from local variations in
magnetic susceptibility could be an additional source of variation in apparent
diffusion coefficients has been proposed by several works [26, 30]. Trudeau et al.
[30] measured diffusivity values at 4.7T in excised porcine spinal cords at room
temperature, with diffusion gradients applied parallel and perpendicular to the
primary fibre orientation. By reorienting the sample relative to the main magnetic
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field direction, they were able to manipulate the distribution of local magnetic
susceptibility. Beaulieu and Allen [26] performed similar experiments at 2.35T on
excised nerve fibres from garfish and frog. Both studies reported no detectable
impact of local gradients on diffusivity values in these samples, and neither
attributed the observed [26] orientation dependence w.r.t. to the effects of local
gradients. Upon closer inspection of [30, FIG. 3], a trend may be apparent with
regards to fibre orientation w.r.t. . Although the distributions of – or -values
overlap when measured at either sample orientation, the average values for seem
lower and the average values for seem higher when the primary fibre orientation
is along . Beaulieu and Allen [26] solidified the apparent trend for the
dependence of -values on primary fibre orientation w.r.t. , by reporting
lower values measured when fibres were along the magnetic field. Ähnlich, in our
in vivo data axial diffusivities were higher for fibres across compared to fibres
along , while radial diffusivities followed the opposite trend. Knight et al. [15]
have previously simulated the effects of mesoscopic magnetic field inhomogeneities
near a hollow cylinder on and also reported head-orientation dependence of MD
and FA values. They considered cross-terms between local gradients and encoding
gradients to be negligible. Wang et al.[62] have rotated an extracted mouse brain
w.r.t. the main magnetic field and evaluated MD and FA for seven major brain
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Regionen, one of which was white matter. They did not observe any significant
variations across orientations of MD/FA in WM, however they did not break down
WM into sub-ROIs of similar fibre orientation, potentially averaging away effects
due to re-orientation. Bartels et al. [63] have recently studied MD, AD and RD as
function of fibre orientation w.r.t. . They reported MD to behave in
correspondence with simulations by Knight et al. [15], but AD/RD obtained from
their data are respectively minimal/maximal around the magic angle, suggesting a
different origin of anisotropy. Interessant, our data showed similar trends (cf.,
spline curves or piecewise average in SI). RD also showed a local maximum near the
magic angle. The AD-curves appeared monotonous but still an increase in gradient
around the same angle was evident. Zusätzlich, a local minimum was apparent in
the FA-curves. Pang [64, 65] also suggests an important role for magic angle effects.
Studies which investigate the -related anisotropic effects in DTI are limited
in number. Noch, the anisotropic effects in DTI measures from WM observed here are
coherent with those seen in previous works investigating
-anisotropy, obwohl
comparatively less pronounced. The majority of studies cover anisotropic effects of
the WM signal evolution from the multi-echo gradient-recalled-echo (mGRE)
sequence [1–12]: thanks to its sensitivity to -inhomogeneities it provides strong
contrast in regions composed of tissues with different magnetic susceptibilities
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(myelinated WM fibres, in this particular case). Although most dMRI sequences are
spin-echo-based,
in which
the -effects are refocused, some magnetic
susceptibility effects may shine through. Auf der einen Seite, incoherent molecular motion
happening between the excitation pulse and the spin-echo combined with local
-inhomogeneities induced by the myelin sheath may lead to residual
non-fully-refocused phases, on the other hand, echo-planar readout has some
unavoidable
-weighting during the acquisition window. That said, the centre of
the -space is closer to the centre spin-echo, and is therefore less affected;
additionally, lower-resolution data are expected to suffer less from this effect.
In der Tat, Gil et al. [14] reported sin -dependence of macroscopic -values on
fibre orientation to .
Another candidate for
the orientational dependence of is
Die
aforementioned magic angle effect (or dipole-dipole interactions) with the
characteristic cos -behaviour. So far those were not considered the
primary source of WM -anisotropy in adults in vivo and postmortem brain, Aber
also not excluded as a potential contributor [6, 11, 66]. Interessant, Bartels et al.
[20] studied orientation dependence in the newborn brain having low
myelination and observed very different behaviour from the adult brain, vorschlagen
a primary role for residual dipolar coupling. In the absence of myelin,
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neurofilaments and microtubules of the axonal cytoskeleton are aligned with the
axon were hypothesised
to contribute
to orientation-dependence. Similar
observations were made on our data separating the intra- and extra-axonal relaxation
Tarife [21]: cos fitted the intra-axonal data best.
Summarising, compartmental
-values have been reported to depend on
orientation differentially [7, 9, 21, 67], which could intrinsically lead to
DT-dependence on fibre orientation w.r.t. , regardless of the underlying
microscopic mechanisms.
4.3. Limitations and future work
Anatomical correspondence. The pooled analysis considers all single fibre
population voxels throughout the WM together to estimate a single magnitude of
orientation dependence, however the simulations reveal that micro-anatomical
differences (z.B., signal fractions, myelin sheath thickness, fibre density and other
potential contributors to compartmental -differences) can lead to different
orientation dependence. The tractometry analysis aims to address this to a certain
extent by pooling voxels more locally, but with two head orientations as used in this
study it remains challenging to estimate local differences in orientation dependence.
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More head orientations and a boost in SNR could help to further investigate this.
Hier, more efficient acquisition-schemes, such as ZEBRA [68], would be beneficial
to enable reasonable acquisition times. Darüber hinaus, anatomical correspondence could
be further achieved by co-registering the data from the two head orientations in
future work. To accomplish this, it is essential to employ a reliable registration
method that can effectively handle the residual nonlinear effects. Außerdem, von
pooling the data from all subjects’ S P WM voxels we were able to compensate for
low number of subjects. With more subjects one could investigate the anisotropy
w.r.t. of individual tracts and consequently provide additional anatomical
Information.
Gradient nonlinearities. Ein anderer
limitation potentially arises from
nonlinearities of gradient fields. With the rotation of the tiltable coil the head is
positioned further from the iso-centre, where gradient nonlinearities have a larger
Wirkung. This in turn influences the effective -matrix, and could introduce additional
variability between the non-tilted and tilted orientation. In addition to effects
reported as a result of the effective -matrix not being taken into account [69–71], Wenn
gradient nonlinearities cause the effective -value to be higher than the imposed
value, kurtosis effects may start to play a more prominent role and bias DT
estimates. In the current work we take into account the effective -matrices, und zu
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further minimise this potential confound we analysed a subset of the data at
ms for which a lower -value of s mm was available (Figure 1A).
Supplementary Figure S4
shows a comparison of
the pooled- Und
tractometry-analyses with maximum -value of s mm and s mm .
The observation of orientation dependence remained unchanged, with larger
estimated absolute magnitude of anisotropy at the lower -value for AD, RD, Und
FA in both analyses and also MD in the tractometry analysis.
We also considered the effect of gradient non-linearities in the estimation of
the fibre direction. In this work, the first eigenvector of the DT was used, but this can
be done in alternative ways and with different estimation techniques, e.g. spherical
deconvolution to obtain the fODF. The reason this work opted for the current
approach is that spherical deconvolution approaches typically do not take into
account gradient nonlinearities [70]. The DT estimation used in this manuscript does
take this into account and the estimates of the maps and fibre direction come from
the same DT estimate.
Crossing fibres. This scope of this work is limited to single fibre population
voxels. Previous work has characterised per fibre population in crossing fibre
voxels, Zum Beispiel [72]. In the current work, based on [21], we have simulated a
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distribution of orientation-dispersed compartments according to a Watson
distribution, where each sub-compartment (e.g. each extra-axonal zeppelin) can
separately exhibit -orientation dependence. This could be straightforwardly
adapted to model crossing fibres, but the bundles will have to have the same
relaxation properties. A recently presented abstract described estimation of such a
model for multi-echo gradient-echo sequences [73].
SNR. Endlich,
the SNR distribution
in WM can change with
head-reorientation. While the tiltable coil minimises differences in the coil-to-brain
distance across different head orientations, SNR may still be affected due to e.g.
change in the reception efficiency of the tiltable coil as the axis of the coil is rotated
away from , gradient non-uniformities, or shim. Previous work [21] showed
that the temporal SNR (tSNR) distribution in WM globally overlapped between
tilted and default orientation, and Supplementary Figure S5 further investigates this
per tract-segment from the tractometry pipeline. Overall the estimated tSNR of the
same location in tilted vs default orientation is distributed along the line , but a
global fit through tSNR measurements from all locations implies that tSNR values in
the tilted position could be up to lower than in the default orientation.
Preliminary experiments in a phantom with the body coils for signal reception
suggest that the impact of shim and gradient non-uniformities may be greater
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than the impact of receive coil efficiency (results not shown). While we have
attempted to correct for noise bias – which can significantly impact DTI estimates
[74] – denoising strategies could further reduce the impact of noise-differences
especially at longer TE.
5. Abschluss
DT measures may vary up to as a function of WM fibre orientation
w.r.t. in the scenarios investigated. Fibre orientation can be responsible for up to
variance in diffusion tensor measures across single fibre populations of the
whole brain white matter. While potentially containing useful information on e.g.
myelination, the orientation dependence of DTI w.r.t. can be an additional
source of variance camouflaging the effect-of-interest in clinical research studies,
particularly when the effect size is small and it is difficult to control for fibre
orientation w.r.t. (e.g. fetal or neonatal imaging, or when the trajectories of
fibres change due to e.g. space occupying lesions).
Danksagungen
For the purpose of open access, the author has applied a CC BY public
38
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copyright licence to any Author Accepted Manuscript version arising from this
Vorlage. CMWT is supported by the Wellcome Trust [215944/Z/19/Z] and a
Veni grant (17331) from the Dutch Research Council (NWO). DKJ, CMWT, Und
EK were all supported by a Wellcome Trust Investigator Award (096646/Z/11/Z)
and DKJ and EK were supported by a Wellcome Strategic Award (104943/Z/14/Z).
The data were acquired at the UK National Facility for In Vivo MR Imaging
of Human Tissue Microstructure funded by the EPSRC (grant EP/M029778/1), Und
The Wolfson Foundation.
We would like to thank Siemens Healthineers, and particularly Fabrizio
Fasano, Peter Gall, and Matschl Volker, for the provision of the tiltable RF-coil used
in this work. We would also like to thank John Evans, Greg Parker and Umesh
Rudrapatna for technical support, Maxime Chamberland for the tractometry analysis
in the original publication on compartmental -anisotropy, and Stefano Zappalà for
helpful discussions.
Erklärung zur Datenverfügbarkeit
Data available on request due to privacy/ethical restrictions.
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Autorenbeiträge
EK: Konzeptualisierung; Formal analysis; Untersuchung; Methodik;
Software; Validierung; Visualisierung; Writing – original draft; Writing – review &
Bearbeitung.
DK: Akquise von Fördermitteln; Ressourcen; Writing – review & Bearbeitung.
CMWT: Konzeptualisierung; Formal analysis; Untersuchung; Methodik;
Projektverwaltung; Software; Aufsicht; Validierung; Writing – original draft;
Writing – review & Bearbeitung
Declaration of Competing Interests
No conflict of interest to disclose.
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A. Appendix: Key findings from Tax et al. [21]
In our previous work, we estimated the apparent -values for intra- Und
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extra-axonal compartments from the WM SFP data acquired by varying nominal
-values and TEs simultaneously. The acquisition parameters are reproduced in this
work in Figure 1A. The compartmental spin-echo signals with associated apparent
-values were included in the compartmental model of diffusion in WM. Letzteres
describes the signal as a convolution of the signal associated with a population of
perfectly parallel fibres with a fibre orientation distribution function. The diffusion
in the intra- and extra-axonal spaces was described by ‘stick’ and ‘zeppelin’ tensors,
jeweils.
We then characterised the dependence of the compartmental -values on
WM fibre orientation angle w.r.t. :
iso
aniso sin aniso sin
(A1)
(A2)
iso is a -independent isotropic component of , whereas describes
the orientation-dependent component. We allowed the corresponding anisotropic
coefficients aniso to be independent, linked, or set to zero, to achieve different
variations of this generalised representation. This resulted in a set of the following
five representations:
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iso
iso aniso sin
iso aniso sin
iso aniso
cos
iso aniso sin aniso sin
(A3)
(A4)
(A5)
(A6)
(A7)
All of them were used to analyse the data pooled from all SFP voxels and head
orientations, while only the first three were applied to analyse data, which were
anatomically matched between head orientations using tractometry. We also
analysed the -values estimated by fitting a mono-exponential function to the data
obtained at to compare them to previous studies.
Main results from the pooled data. Intra-axonal -values were best
described by the Eq. A6 with the isotropic component of s and the
magnitude of anisotropy of s . The AIC of the isotropic representation (Eq.
A3) was larger than that of the anisotropic representation with and
iso s . Extra-axonal -values were best represented by the Eq. A5 with
the isotropic component of s and the magnitude of anisotropy of s .
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The corresponding isotropic representation was not supported with
and iso s .
Main results from the tractometry analysis. Intra-axonal values were best
supported by the isotropic representation, while extra-axonal values were best
supported by sin -representation with the magnitude of anisotropy of
S .
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