k-space and q-space: Combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7T Robin M. Heidemann a , Alfred Anwander a , Thorsten Feiweier b , Thomas R Knösche a , Robert Turner a a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. b Siemens AG, Healthcare Sector, Allee am Roethelheimpark 2, 91052 Erlangen, Germany Corresponding author: Dr. Robin M. Heidemann Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurophysics, Stephanstrasse 1a 04103 Leipzig Germany http://www.cbs.mpg.de/~heidemann Preprint submitted to Neuroimage (2011). Final draft: November 2011 Author Manuscript Author Manuscript Author Manuscript Author Manuscript Preprint submitted to Neuroimage (2012). Published in final edited form as: Neuroimage. Vol 60, Iss 2, Pages 967–978, 2012 DOI: 10.1016/j.neuroimage.2011.12.081
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k-space and q-space: Combining ultra-high spatial
and angular resolution in diffusion imaging
using ZOOPPA at 7T
Robin M. Heidemann a, Alfred Anwander a,
Thorsten Feiweier b, Thomas R Knösche a, Robert Turner a
a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
b Siemens AG, Healthcare Sector, Allee am Roethelheimpark 2, 91052 Erlangen,
Germany
Corresponding author:
Dr. Robin M. Heidemann
Max Planck Institute for Human Cognitive and Brain Sciences,
Department of Neurophysics,
Stephanstrasse 1a
04103 Leipzig
Germany
http://www.cbs.mpg.de/~heidemann
Preprint submitted to Neuroimage (2011). Final draft: November 2011
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Author Manuscript Preprint submitted to Neuroimage (2012).
Published in final edited form as:
Neuroimage. Vol 60, Iss 2, Pages 967–978, 2012 DOI: 10.1016/j.neuroimage.2011.12.081
The AS95 gradient system has a lower maximum gradient amplitude and was
replaced by the newer SC72 gradients in our 7 Tesla system. For signal reception, a
24-element phased array head coil (Nova Medical, Wilmington, MA, USA) was used.
This coil is equipped with a single channel transmit coil for excitation and 24
independent receive-elements. Phase-encoding was chosen anterior-posterior to
obtain less pronounced, symmetric distortions. For the head geometry of the
volunteers examined in this study, in anterior-posterior direction, a minimum FOV of
200 – 218 mm is necessary to avoid aliasing. With the zoomed approach using OVS,
we reduced this FOV down to 141 – 144 mm. Based on the individual minimum
required FOV for each participant, the acceleration due to the zoomed approach
AFZOOM is between 1.39 and 1.53. Since the parallel imaging AFPPA is three for all in
vivo examples here, the resulting overall AF is in the range between 4.2 and 4.6 (see
Table 1).
For all acquisitions, an optimized monopolar Stejskal-Tanner sequence (Morelli
et al., 2010) was used in conjunction with ZOOPPA with the imaging protocol
parameters as listed in Table 1. In all experiments, dMRI data with b = 1000 s/mm2
and 60 directions (interspersed with 7 b0 images used for motion correction) were
obtained with 4-6 averages, resulting in a total acquisition time of around one hour.
For participants 1 and 2 (AS95 gradient system), a fat suppression method as
11
proposed by Ivanov et al. (2010) was used, which is well-suited to ultra-high field
dMRI. For the remaining participants, a combination of the above-mentioned method
and a gradient reversal fat saturation method (Park et al., 1987) was used. This
approach results in a more effective fat saturation as gradient reversal alone with a
reduced duration of the refocusing pulse compared to the method by Ivanov. Due to
this, a shorter TE can be used, resulting in a slightly higher SNR, as shown in Eichner
et al. (2011). For the parameter settings and hardware used, please refer to Table 1.
The data of the first two participants were corrected for subject motion in FSL
using the interspersed b0 images and linearly registered to a T1-weighted anatomical
scan. Then a diffusion tensor was fitted in each voxel and streamline trajectories were
computed using the tensor-line approach (Weinstein et al., 1999; Fillard et al., 2007)
implemented in MedINRIA2.
For the remaining data sets, we additionally applied a two-stage hybrid image
restoration procedure to the dMRI data prior to motion correction as described in
Lohmann et al. (2010) and implemented in Lipsia3. In each voxel, multiple fiber
orientations (significance level F > 0.05) (Behrens et al., 2007) were computed using
FSL4. In addition, the fiber orientation density function (fODF) was derived by
constrained spherical deconvolution (Tournier et al., 2007) based on spherical
harmonics of order 6, followed by whole-brain streamline-tracking using MRtrix5.
3. Results
3.1. Phantom study
The results of the phantom study are summarized in Fig 3. Here, we compare a
conventional GRAPPA acquisition with AFPPA = 4 (see Fig. 3A) and a ZOOPPA
acquisition with overall AF = 4 (see Fig. 3B). A similar ZOOPPA acceleration scheme
2 www-sop.inria.fr/asclepios/software/medinria
3 www.cbs.mpg.de/institute/software/lipsia
4 www.fmrib.ox.ac.uk/fsl
5 www.brain.org.au/software/mrtrix
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was used for the following in vivo acquisitions. For both acquisition schemes, the
same overall acceleration was used and all imaging parameters were kept the same.
Corresponding g-factor maps are shown for conventional GRAPPA in Fig. 3C and for
ZOOPPA in Fig. 3D. The conventional GRAPPA g-factor map (Fig. 3C) has a
maximum g-factor of 3.2 and a mean g-factor within a region-of-interest (ROI) in the
phantom of 2.4. Using ZOOPPA with the same overall AF, the maximum g-factor is
1.9, while the mean g-factor in the same ROI is 1.7. This improvement is also visible
in the SNR evaluation, listed in Table 2. Here, SNR values are derived for different
ROIs of different size. ROI I is the upper half FOV of Fig. 3A, which corresponds to
the reduced FOV size of Fig. 3B. ROI II is indicated as a white box in Fig. 3A. ROI III
and IV are smaller and do not contain regions with background noise and no signal.
Even though the SNR values vary across different ROIs, which can be explained by
coil sensitivity variations, the ratios between the SNR values achieved with GRAPPA
and with ZOOPPA are very similar. This is reflected in the percentage gain in SNR for
ZOOPPA (numbers in brackets in Table 2), which is slightly above 25% for this
phantom study. Here, ROI I is excluded because it contains regions with background
noise and no signal and the SNR values might not be reliable in this region.
3.2 Human study
To demonstrate the effects of increased resolution and averaging, we show
color-coded direction maps in Fig. 4. A comparison is drawn between acquisitions
with isotropic resolutions of 1.5 mm (Fig. 4A) and 1.0 mm (Fig. 4B) by showing
enlarged sections of axial slices covering the occipital lobe. Obviously, due to the
increased resolution, more anatomical details are visible in the image obtained at 1.0
mm isotropic resolution. Furthermore, we see a better contrast between gray and
white matter. This can be seen in the enlarged sections of the fractional anisotropy
maps shown in Fig. 4C and in the profile lines through the sulcus indicated by the
white box (see middle of Fig. 4C). Compared to 1.5 mm isotropic resolution (green
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profile), the cortex is clearly visible at 1.0 mm isotropic resolution (black profile).
However, with a very high isotropic resolution SNR becomes a major issue making
averaging and post processing of the raw data important, which is demonstrated in
Fig. 4D. Here, whole axial slices are shown from data with of 800 µm isotropic
resolution without averaging (Fig. 4D left), with 4 averages (Fig. 4D middle) and with
4 averages and the image restoration procedure (Fig. 4D right).
Figure 5 shows trace-weighted images as an example of the coverage
achievable using ZOOPPA with 1 mm isotropic resolution. It comprises the posterior
two thirds of the brain and misses structures like the prefrontal cortex and the anterior
temporal lobe due to the OVS. Even though we acquired data with a large number of
slices (see Table 1), due to the high voxel resolution, the coverage is limited. In the
example shown, it leads to the omission of brain stem and cerebellum. The left
column of Fig. 5 shows the trace weighted images and the middle column shows the
trace weighted images overlaid with the gray matter white matter boundaries derived
from a corresponding anatomical scan and the right column shows additional axial
slices from different positions in the brain.
In the following, we present a number of examples selected to highlight specific
image properties of the ZOOPPA method at 7 Tesla and the resulting opportunities
for imaging fine anatomical details.
First, we explored the possibilities of the increased resolution achieved by
ZOOPPA in classical tensor-based streamline tractography. For this purpose, we
chose the pons. This brainstem region is known to contain several interdigitated small
fiber bundles. The acquired dMRI data with 1 mm isotropic resolution show minimal
distortions even in such a basal region, as can be seen in Fig. 6A and B. Color-coded
direction maps (Fig. 6C) and streamline tractography (Fig. 6D) demonstrate the
resolution of complex fiber crossings. Qualitative comparison with MR microscopy of
the same region taken from Duvernoy’s Atlas of the Human Brain Stem and
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Cerebellum confirms that dMRI extracts realistic and relevant features of the fiber
architecture.
Second, we investigated whether and to what extent our method is capable of
imaging anisotropy in gray matter. For this purpose, we focused on imaging diffusion
anisotropy in the cortex using different local reconstruction methods. The diffusion
direction in the cortex clearly differs from the direction within the white matter. At an
isotropic resolution of 1 mm, both diffusion tensor (Fig. 7C) and ball-and-stick model
(Fig. 7D) yield clear radial anisotropy in most cortical areas with fractional anisotropy
values of up to 0.4 (see a myelin stained cortex section taken from Braitenberg 1962
in Fig. 7B for qualitative comparison). Voxels at the gray-white matter interface show
reduced anisotropy (highlighted in one position with an arrow in Fig. 7C), possibly due
to partial volume effects. Note the resolution of fiber crossings in the white matter by
the ball-and-stick model (Fig. 7D). Fiber orientation density functions (Fig. 7E)
demonstrate even finer detail of intracortical diffusion patterns across different layers
within the cortex.
Third, we demonstrated the ability of the ZOOPPA technique to resolve complex
fiber crossings within the white matter. Here, not only the ultra-high spatial resolution
(800 µm isotropic resolution), but also the high angular resolution (60 directions) is of
relevance. We focus on the triple crossing area in the internal capsule, where fibers
of the corpus callosum (CC), the cortico-spinal tract (CST) and the superior
longitudinal fasciculus (SLF) intersect in a complex way. Figure 8 highlights the fact
that all three fiber bundles can be followed through the crossing area showing fine
details of interdigitated sub-bundles and the fan-like arrangement of the fiber
streamlines.
Finally, we studied the possibility of reconstructing fiber tracts forming the
interface between gray and white matter. These fibers are particularly important as
they determine the connectivity between particular gray matter areas and the white
matter fiber bundles. In Fig. 9, we show that sub-millimeter (800 µm) ZOOPPA data
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can be used to track white matter fibers into the cortex. For a qualitative comparison,
see a myelin-stained section of a gyrus (inset in Fig. 9A, taken from Braitenberg
1962).
4. Discussion
4.1 ZOOPPA – substantial improvement in image quality
Fast MRI acquisition techniques in conjunction with substantial progress in the
development of MR hardware, such as stronger and faster gradients, have reduced
the acquisition time of a two dimensional image of the human body from several
minutes to a few seconds, or in some cases, fractions of a second. This corresponds
to an acceleration factor of several hundred. Compared to this, the acceleration
factors achievable with parallel imaging seem to be small. However, one has to keep
in mind two facts: First, a factor of two faster imaging due to improved gradient
performance requires double the gradient strength and a gradient rise time that is four
times faster. Second, parallel imaging is used in addition to the performance of the
gradient system. In this study, we used 95% of the maximum gradient amplitude for
dMRI. Even with the high performance gradient system used, without the acceleration
due to ZOOPPA, the echo time of the dMRI acquisition with 0.8 mm isotropic
resolution would increase from 65 ms to 149 ms. Such a long echo time would result
in severe signal losses due to the shortened T2 relaxation time at 7 Tesla compared
to 3 Tesla, spoiling any SNR benefit obtained from the higher field strength.
Therefore, the main objective for dMRI at ultra-high field strength is to shorten the
echo time. The other challenge with high resolution EPI is to minimize susceptibility-
induced geometric distortions. Since this effect scales with the field strength, we need
higher AFs at 7 Tesla as compared to 3 Tesla in order to compensate for this. Thus, it
is obvious that acceleration techniques such as ZOOPPA are playing an even more
important role in imaging at ultra-high field strength.
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Wiesinger et al. (2004) showed that under certain assumptions, higher parallel
image AFs can be achieved at higher field strength. However, EPI acquisitions at 7
Tesla with acceleration factors greater than three are still problematic and tend not to
be useful, due to the increasing g-factor penalty, resulting in severe SNR losses. As
shown in Fig. 3, an AF = 4, achieved using GRAPPA only (Fig. 3A), is sub-optimal.
Conventional GRAPPA acquisitions suffer from a higher g-factor compared to the
ZOOPPA approach (compare Figs. 3C and 3D). The reduced g-factor of the
ZOOPPA approach is also reflected in an increased SNR. The ZOOPPA acquisition
shows a 25% higher SNR compared to conventional GRAPPA for the phantom
acquisitions performed in this study (see Table 2). In general, as shown in Eq. 3, we
expect a maximum SNR gain with ZOOPPA compared to conventional parallel
imaging, given by the ratio of the g-factors.
For practical application of the proposed method it is relevant that the voxels are
cubic. Although dMRI studies with highly anisotropic image voxels have been
performed, this strategy is not suited to tractography in most parts of the brain. Only
in regions like the hippocampus for example (as in Yassa et al., 2010), it might be
advantageous to use thicker slices when the slices are exactly orthogonal to the
structure, and major changes are only expected along in-plane directions. However,
the convoluted structure of the cerebral cortex and inhomogeneous white matter
structures (white matter voxels contain more than one population of axonal fibers)
make it important to use high resolution isotropic voxels. Besides the reduced partial
volume effect when thinner slices are used, an additional advantage of thinner slices
is that intravoxel dephasing, due to through-plane gradients, is reduced (Howseman
et al., 1999, Weiskopf et al., 2007). These dephasing effects can cause severe signal
dropouts in EPI.
A further important aspect of the in vivo examples shown here is the acquisition
time. With about one hour total acquisition time, the ZOOPPA dMRI experiments
could be affected by hardware issues as well as physiological issues. A test run of the
800 µm isotropic resolution ZOOPPA protocol with the SC72 gradient system for 90
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minutes did not show any hardware constraints, such as measurement interrupts due
to heating of the gradient coil. The one hour protocol used in this work is tolerable for
healthy volunteers. However, a shorter acquisition time would be beneficial not only
for larger studies or patient studies, but also to enable the acquisition of other
applications, such as high resolution anatomical and functional scans within the same
scan session. One reason for the long scan time is simply the resolution. Due to the
very high resolution, averaging is necessary to address the SNR issue which is
demonstrated in Fig. 4D. Besides averaging, another reason for the long scan time is
the relatively long repetition time (TR). Due to SAR limitations TR has to be increased
from a minimum TR of 12 s to 14 s (1 mm protocol as listed in Table 2 middle). The
OVS pulse adds essentially the same amount of SAR than a 180° pulse.
Furthermore, the duration of the OVS pulse adds another 3 s to the TR. In summary,
the use of OVS prolongs the total scan time (assuming 4 averages) by about 24 min.
In the current implementation of ZOOPPA, for each slice an OVS pulse is played out.
Here, SAR could be reduced by a factor of two by using OVS only for every second
slice. Another potential solution would be to combine the ZOOPPA approach with a
simultaneous multi-slice excitation and readout technique, such as CAIPIRINHIA
(Breuer et al., 2005). This method allows a reduction of the repetition time, and
therefore the acquisition time, by a factor of three, while showing very promising
results for EPI acquisitions (Setsompop et al., 2011).
The proposed ZOOPPA approach of combining parallel imaging and zoomed
imaging is not limited to the use of GRAPPA and OVS with adiabatic RF pulses, as
used in this study. In fact, other combinations as for example GRAPPA with 2D RF
pulses may have advantages in terms of a more homogeneous excitation at 7 Tesla.
In general, for ZOOPPA, any combination of a parallel imaging technique with a
reduced FOV approach can be used.
4.2 Tractography with high resolution ZOOPPA data – what can be gained?
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In tractography, it is assumed that the diffusion MR signal accurately reflects the
true fiber orientations at each point. This assumption is clearly a simplification, as its
literal fulfillment would require a number of very strict conditions to be met: (1) the
diffusion time is sufficiently long for the molecules to hit the relevant boundaries and,
if no such boundaries exist, travel far enough to dephase and cause signal loss; (2)
the sampling of the signal is sufficiently dense, in angular space (diffusion directions),
in radial space (diffusion lengths) and in time; and (3) the voxel size is in the order of
magnitude of the relevant structures, i.e., the axon diameter. It is evident that these
conditions cannot be met in practice, leading to imperfections in fiber track
reconstruction. Regarding condition (1), diffusion times and gradient areas are usually
chosen such that in the absence of boundaries, significant signal loss occurs, and this
signal loss is reduced by boundaries in distances that correspond to typical axonal
diameters (approximately between 0.1 and 5 µm; personal communication, Almut
Schüz, Tübingen, Germany) and packing densities (approx. 380000 fibers per mm²,
Aboitiz et al., 1992). For example, free diffusion in water at body temperature leads to
an average displacement of 25 µm for a diffusion time of 30 ms. Much less perfectly
condition (2) can be fulfilled: in human in vivo dMRI, we often measure with a number
of uniformly distributed gradient directions and sample the diffusion propagator with a
single combination of gradient area and diffusion time (b-value). In specialized
settings, in particular in animal or post-mortem studies, multiple b-values (Assaf and
Basser, 2005; Wedeen et al., 2005) or even independent variation of diffusion time
and gradient strength (Assaf et al., 2008) can also be applied. However, even with
these settings, it remains a discrete sampling of the diffusion propagator. This leads
to significant blurring of the angular profile of fiber directions. With respect to
condition (3), we realize that, even if the other two conditions were perfectly met, the
finite voxel size remains a serious obstacle to accurate fiber reconstruction. Even if
every fiber orientation within a voxel is identified with complete accuracy, we do not
know which points on the voxel boundaries these fibers connect. In other words, the
angular profile of fiber orientations is averaged over the voxel volume. Partial volume
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effects with other structures with completely different diffusion properties, for example
ventricles, can also cause severe errors.
Any gain in echo time and therefore SNR can be invested in improvements of the
diffusion propagator sampling (more b-values and/or gradient directions) or
refinement of the voxel resolution, or some tradeoff between both. It has been shown
that using high q-space sampling (515 gradients) in combination with relatively low
spatial resolution (2.75 mm isotropic) enables the resolution of fine details, in
particular fiber crossings in both white and gray matter areas (Wedeen et al., 2008).
On the other hand, in this work we show that more modest sampling of the q-space
(only one radius, 60 directions) combined with very high voxel resolution (1.0 and 0.8
mm isotropic) yields similar results, at least in crossings of large fiber bundles.
Therefore, it might be instructive to consider what could be achieved by pushing
further in either direction, assuming, for the sake of argument, that SNR does not
impose limitations. Even perfect (infinite) sampling of diffusion direction and b-value
with finite voxel size would still lead to an average propagator. In contrast, if the voxel
size were reduced to the typical axon diameter, even very crude sampling of the
diffusion propagator would result in an accurate detection of the fiber direction (as
there is only one fiber left in the voxel) and would enable, in principle, perfect
tractography. Obviously, it is not clear how these extreme (and unattainable) cases
translate into more realistic situations and, beyond doubt, optimal imaging schemes
will in practice always include some compromise between k-space and q-space
sampling. However, our results seem to indicate that improvement in voxel resolution
is a fairly potent means of improving the reconstruction power of tractography. This
might be related to the fact that, in many parts of the brain, fiber architecture is more
complicated than simple crossing of major fiber tracts. Instead, fiber bundles and
separating gray matter structures often have a very small spatial extent. When using
large voxels, even with dense sampling of the diffusion propagator, severe
misidentifications of fiber bundles can be the result. For example, the fiber systems of
the external and extreme capsules are separated by the claustrum with a thickness of
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little more than 1 mm in many places. With voxels sizes of 2 mm or more, partial
volume effects would lead to a mixture of connections of the two capsule systems, no
matter how accurately the mean fODF of the voxel is reconstructed. Similar problems
may occur everywhere in the brain, where the extent of relevant structures is in or
below the order of magnitude of the voxels, which is demonstrated with the example
shown in Fig. 4A-C. Obviously, more anatomical details are visible and a better
delineation of the cortex is possible in the color coded direction maps based on the
data with 1.0 mm compared to 1.5 mm isotropic resolution.
The methodology employed for local modeling as well as for fiber tractography in this
work comprises standard state-of-the-art techniques. We used streamline tracking on
the basis of either single tensors or multi-compartment models (ball-and-stick) or
fODFs computed using spherical deconvolution. In this way, it is possible to highlight
the specific benefit of the imaging method.
The triple crossing between the three major fiber bundles of the corona radiata, the
superior longitudinal fascicle and the projections of the corpus callosum has been
used by many authors to demonstrate the ability of their algorithms to resolve fiber
crossings (Tuch et al., 2003; Kaden et al., 2007; Wedeen et al; 2008; Kaden et al.,
2008; Descoteaux et al., 2009). Here, we show that in spite of the small voxel size,
the data have sufficient SNR to reconstruct this crossing, using streamline
tractography based on spherical deconvolution fODFs (Fig. 7). Comparable to other
techniques, we were not only able to follow all three fiber bundles through the
crossing area, but also to see how the corpus callosum fibers start to fan out in the
crossing area.
A more significant challenge is posed by crossings and interdigitations of small fiber
bundles, such as in the brain stem. By using the simple tensor as a basis for the
reconstruction of the pontine fiber bundles, we could demonstrate that with a
sufficiently small voxel size, it is possible to resolve fiber crossings composed of small
interdigitated bundles, even with this most simple approximation of the diffusion
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propagator (Fig. 6). When using larger voxels, more complex local models, such as
DSI (see Wedeen et al., 2008; Fig. 8), are needed to achieve comparable results
(although it is not even clear if the fine structure of the interdigitated bundles can be
reconstructed at all). Please note that the insets in Figs. 6, 7 and 9 showing examples
of MR microscopy (Fig. 6C) and Weigert stain (Figs. 7B and 9A) only serve to
demonstrate the correspondence between the dMRI results and to the gross
anatomy.
An important test ground for tractography algorithms is the identification of fiber
pathways in gray matter and at the interface between gray and white matter. Both are
particularly important for the estimation of anatomical connectivity. Here, we
demonstrate that the radial structure of the cortex as well as the fibers entering the
cortex can be imaged in fine detail. While using a simple tensor already allows the
identification of the radial structure in the cortex (Fig. 7C), using more sophisticated
local modeling allows us to distinguish between different layers in the cortex with
different properties (Fig. 7E) as well as to track fibers from the white matter into the
cortex (Fig. 9). This requires high spatial resolution. Note that for reconstruction of
fine fiber crossings in the cortex, a mere increase in spatial resolution is not sufficient
(at least within practical bounds), as fibers do not usually cross as interdigitated
bundles, but at a much finer spatial scale (Wedeen et al., 2008).
Although our results must be seen as a “low resolution” approximation of the true
fiber architecture in white and gray matter, they clearly indicate that using the gain in
SNR due to the use of ultra-high field strength in conjunction with ZOOPPA is well
invested in the increase of voxel resolution.
Conclusion
The synergetic effect of using diffusion MRI with ZOOPPA at ultra-high field
strength with high performance gradients enables accelerated EPI acquisitions with
minimal artifacts and a high SNR. As a result, high angular resolution data with an
22
isotropic resolution down to 800 µm can be obtained. We show that the method is
powerful enough to reconstruct large scale fiber crossings (as many other methods
have likewise proved to be able to do), to resolve fine interdigitated fiber bundles in
the brain stem, to resolve consistent fiber orientation as well as layered structure in
the cortex and to resolve the gray-white matter interface by tracking white matter
fibers into the cortex. By virtue of our results as well as theoretical considerations, we
argue that using the gain in SNR provided by our method is well invested in the
increase of voxel resolution.
6. Acknowledgements
We would like to thank Felix Breuer of the Research Center Magnetic Resonance
Bavaria (MRB) in Würzburg and Josef Pfeuffer, David Porter, Keith Heberlein, Stefan
Huwer and Heiko Meyer of Siemens Healthcare in Erlangen for their support and
technical contributions. Furthermore, we wish to thank the following people at the Max
Planck Institute in Leipzig: Robert Trampel, Dimo Ivanov, Cornelius Eichner, Elisabeth
Wladimirow and Domenica Wilfling. Finally, we wish to thank Fabrizio Fasano of the
University of Parma.
Part of this work is supported by the FET project CONNECT of the European Union
(www.brain-connect.eu).
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FIGURE CAPTIONS
Fig. 1: Depiction of how zoomed imaging and parallel imaging are combined (ZOOPPA) to achieve a high acceleration factor: (A) Non-accelerated, 100% FOV. (B) Zoomed accelerated, 50% FOV with OVS, AFZOOM = 2 without aliasing. (C) ZOOPPA accelerated, 17% FOV. The reduced FOV is further reduced by a factor of three. The overall AF is AFZOOM multiplied by AFPPA, which results in overall AF = 6. The parallel image reconstruction with AFPPA = 3 will unfold this image to 50% FOV as in (B). Fig. 2: ZOOPPA acquisition and final image: (A) An OVS band is placed to null the signal outside the region of interest (indicated by an orange box). The FOV can be reduced to 50% FOV (AFZOOM = 2) without aliasing. The FOV is further reduced by AFPPA = 3, resulting in a FOV of 17% (indicated by the gray box). This translates into overall AF = 6. (B) The ZOOPPA image: The parallel image reconstruction with AFPPA = 3 unfolds the aliased image to 50% FOV. (C) For comparison, an image obtained with overall AF = 3 using GRAPPA is shown. This image is cropped to match the coverage of the ZOOPPA image. Fig. 3: Phantom study to compare SNR and g-factor maps of conventional GRAPPA to GRAPPA with zoomed imaging (ZOOPPA). (A) Factor of four accelerated GRAPPA acquisition. (B) Factor of four accelerated ZOOPPA acquisition. Here, AF
PPA = 2 and
AFZOOM
= 2, resulting in overall AF = 4. Corresponding g-factor maps for (C) GRAPPA
and (D) ZOOPPA, both with overall AF = 4. Fig. 4: Comparison between different resolutions and number of averages: (A and C) Sections of an axial color coded directional map with 1.5 mm isotropic resolution (A) and with 1.0 mm isotropic resolution (B). (C) Corresponding enlarged sections, as indicated by the white boxes in (A) and (B), showing the fractional anisotropy values. Profiles, indicated by white lines are plotted in the middle of (C). (D) The effect of averaging on the results obtained from data with 0.8 mm isotropic resolution. (left) Without averaging, (middle) 4-times averaging and (right) 4-times averaging and images restoration procedure. Fig. 5: (left column) Trace-weighted images of a ZOOPPA acquisition with 1 mm isotropic resolution. The coronal (top), sagital (middle) and axial (bottom) view show the coverage which can be achieved with this protocol acquiring 94 slices. (middle column) Trace-weighted images overlaid with the gray/white matter boundaries from a corresponding anatomical scan. (right column) Three additional axial slices from different regions of the brain. Fig. 6: Imaging of the pons and cerebellar region. (A and B) Main diffusion directions overlaid onto the T1 anatomy in a sagittal slice. The images demonstrate the low
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distortion level of the ZOOPPA images. Note the fine level of anatomical details in the image, for example, the radial diffusivity in the cortex and the separate lamina in the pons. (C) Color-coded fractional anisotropy map in the same slice. The texture indicates the fiber orientation. This map resolves sub-parts of the fine laminar medio-lateral oriented structures (red) in the pons dividing ventro-dorsal oriented fibers (blue) into the fronto-pontine tract (F), the cortico-spinal tract (P) and the temporo-parieto-pontine fibers (PT). For qualitative comparison, see inset in (C), showing a similar slice in MR microscopy at 9.4 T (Duvernoy’s Atlas of the Human Brain Stem and Cerebellum, 2009, Fig. 8.46, modified). (D) Streamline tractography showing the separation of interdigitated medial-lateral and inferior-superior fiber bundles. Fig. 7: Radial cortical anisotropy in the trans-occipital sulcus shown in three different participants with three different methods at the same nominal resolution. (A) Anatomical reference showing the coronal slice (Talairach y=-73) with the depicted region. (B) Myeloarchitecture of human cortex (Weigert stain, from Braitenberg 1962) showing radial anisotropy. (C) Principle diffusion directions based on the tensor model overlaid on the color-coded fractional anisotropy map. (D) Diffusion directions computed by a multiple compartment model (ball and two sticks). E: Fiber orientation density functions computed by spherical deconvolution. In all three examples, a clear radial asymmetry in the cortex is observed. Fig. 8: Streamline tracking in fiber crossing area with sub-millimeter isotropic resolution (800 µm) on top of fiber orientation density plots. The streamlines are color-coded according to their local orientation. (A) Coronal section of the brain with crossings of corpus callosum (CC, red), cortico-spinal tract (CST, blue) and superior longitudinal fascicle (SLF, green). (B) Enlarged section of the region indicated in (A). Fig. 9: Fiber orientation density plots (A) and streamline tracking (B) of sub-millimeter isotropic resolution (800 µm) dMRI data in a horizontal slice through parietal lobe, showing white matter fiber tracts entering the cortex. See inset for myeloarchitecture of a gyrus for qualitative comparison (Weigert stain, from Braitenberg 1962). Fig. 10: Supplementary material: A single diffusion direction after motion correction and 4-times averaged. Here, only every second slice is shown.
Table 1: List of imaging protocol parameters for the in vivo acquisitions with 60 diffusion directions: Protocols with 1 mm isotropic resolution have been obtained with an earlier gradient system (AS95) and with a new gradient system (SC72). Sub-millimeter isotropic resolution has been obtained with the new gradient system.
Table 1
Pixels within
ROI GRAPPA AFPPA = 4
ZOOPPA AF = 4 (AFPPA = 2 and AFZOOM = 2)
SNR ROI I 18870 37 48 (+30%)
SNR ROI II 13200 56 71 (+27%)
SNR ROI III 7000 66 84 (+27%)
SNR ROI IV 4500 61 77 (+26%)
Table 2: Mean SNR values of different regions of interest (ROIs) averaged over 4 slices obtained in an oil phantom. Conventional GRAPPA is compared to GRAPPA combined with zoomed imaging (ZOOPPA). The imaging parameters are kept the same for both experiments. ROI I is exactly the region covered with the ZOOPPA approach (half the FOV, as shown in Fig. 3B) including the background. ROIs II-IV are regions within the phantom with shrinking areas. Due to coil sensitivity variations, the SNR is locally dependent. However, for all positions investigated, the ratio is similar, indicating an SNR gain of more than 25% when ZOOPPA is used (percentage gain compared to conventional GRAPPA in brackets).