### Synopsis

**Several diffusion-weighted MRI
techniques for modeling tissue microstructure have been developed and validated
during the past two decades. While offering various neuroanatomical inferences,
these techniques differ in their proposed optimal acquisition design, which
impede clinicians and researchers to benefit from all potential inference
methods, particularly when limited time is available. We examined the
performance of the most common diffusion models with respect to acquisition
parameters at 7T when limiting the acquisition time to about 10 minutes. The most
balanced compromise among all combinations in terms of the robustness of the
estimates was a two-shell scheme with b-values of 1,000 and 2,500 s/mm**^{2}
with 75 diffusion-encoding gradients, 25 and 50 samples for low and high
b-values, respectively. ### Purpose

To obtain a balanced DWI acquisition scheme that
can cover the varying requirements of the most commonly used microstructural
models at 7T.

### Method

We acquired a reference dataset with
multiple shells, covering a wide range of b‑values (700 to 3,000 s/mm

^{2})
using an optimal multi-shell sampling scheme

^{1} that includes the suggested
protocols of the microstructural DWI techniques listed below. From this data
set we generated a large number of sub‑datasets from the multi-shell data
(around 900 combinations). The sub-datasets were evaluated based on different
criteria for the following techniques:
NODDI (neurite orientation distribution
and density index), CSD (constrained spherical deconvolution), DTI (diffusion
tensor imaging) and WMTI (white matter tract integrity)

^{2-5}.
For NODDI the entire multi-shell data was concatenated and the NODDI tissue
model was fitted to the complete reference data set. The derived model
parameters were used as the gold standard to evaluate parameters obtained from
the various sub-datasets. For CSD the linear fascicle evaluation

^{6} technique and fibre reconstruction error rate compared with the full dataset were
used to assess b-values ≥2000s/mm

^{2}. For DTI the root mean square
errors (RMSE) of sub-datasets for b-values ≤1300 s/mm

^{2} were compared
to their full datasets. Finally, the optimal choices from above were compared
with the suggested WMTI protocol

^{2}: 1,000 and 2,000 s/mm

^{2}.
Using these analysis results from all methods we
defined an optimal range by identifying protocols that are not particularly
suitable for multi-purpose diffusion-weighted imaging (Figure 5).

### Acquisition

A healthy, 20-year-old right-handed male was
scanned after giving written informed consent according to the local ethics
committee approval. The acquisition was performed on a 7T whole-body
research scanner (Siemens Healthcare, Erlangen, Germany)
with a maximum gradient of 70 mT/m and a maximum slew rate of 200 T/m/s using a
single-channel quadrature transmit RF coil and a 32-channel receive array coil
(Nova Medical Inc, MA). The protocol consisted of six high angular resolution
diffusion-weighted shells, with b-values of 700, 1000, 1300, 2000, 2500 and 3000 s/mm

^{2},
respectively, using Stejskal-Tanner DW gradients. Other parameters: 1.8 mm
isotropic voxels, 70 slices, 120×120 matrix size, field-of-view 216×216mm,
phase partial Fourier 6/8, GRAPPA acceleration factor 3. A fixed TE of 68.4ms
and a TR of 8100 ms were used with an effective echo spacing of 0.22ms. Total
scan time was 38m:58s for the complete reference data set. A sampling scheme
consisting of 90 directions with a low:high b-value ratio of 1:2 were designed
using the

*q-space sampling* web
application

^{1}.

### Results

NODDI: The six combinations that had
the highest correlation with the gold standard are plotted. For intra-cellular volume
fraction (ICVF), the 700-2500 and 1000-2500 dataset (first-second b-values) had
the highest correlation with the gold standard (Figure 1). The orientation
distribution index (ODI) results showed a high similarity (correlation >0.9)
across all datasets. Combinations with either a b-values of 1300 and 3000 or
combinations with ≤60 samples underperformed and consequently were rejected by
NODDI analysis (Figure 5).
CSD: No decrease in the mean prediction
errors as a result of b-value were observed, rather a slight increase was
apparent (Figure 2A). This slight increase could be due to the lower SNR of datasets
with high b-values. The number of samples required to meet a fibre
reconstruction error equivalent to the full data set was ≥46, ≥48, and ≥50 for
b-values of 2000, 2500 and 3000 s/mm

^{2}, respectively (Figure 2B-D).
DTI: The RMSE showed little improvement
after 20 samples for b-values of 1000 and 1300 s/mm

^{2}. For a b‑value
of 700 s/mm

^{2} more samples were required (≥24) to obtain the acceptable
relative error rate compared to the full dataset (Figure 3).
WMTI: the evaluation of the 1000-2500 and
1000-3000 datasets (Figure 4) showed that the correlation with the estimates
from the conventional protocol decreased when the second b-value was larger.

### Summary

Overall, our suggested design for a
multi-purpose diffusion-weighted microstructural imaging at 7T with a minimal
number of total samples that best satisfies all models is a protocol with
b-values of 1000 and 2500 s/mm

^{2}, with 25 and 50 samples, uniformly
distributed over two shells. With the 1.8 mm isotropic resolution, TR of 8100 ms
and 5 unweighted measurements (b0), this acquisition scheme could be scanned in
<11mins at 7T. The acquired data can be used for DTI, tractography, NODDI,
WMTI and potentially more techniques.

### Acknowledgements

MB acknowledges
funding from ARC Future Fellowship grant FT140100865. The authors acknowledge
the facilities of the National Imaging Facility at the Centre for Advanced
Imaging, University of Queensland and the scientific support of
Siemens Ltd, Bowen Hills, Australia.### References

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