Huiting Zhang^{1}, Ankang Gao^{2}, Shaoyu Wang^{1}, Yang Song^{3}, Jingliang Cheng^{2}, Guang Yang^{3}, and Xu Yan^{1}

^{1}MR Scientific Marketing, Siemens Healthcare, Shanghai, China, ^{2}The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ^{3}Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univeristy, Shanghai, China

### Synopsis

This study aimed to evaluate the performance of three b-value sampling
schemes in calculating multiple diffusion models, including the DTI, DKI, NODDI
and the newly proposed mean apparent propagator (MAP)-MRI models. The three
schemes includes the conventional diffusion spectrum imaging (DSI) acquisition
scheme based on Cartesian grid sampling in q-space, multi-shell sampling with
the same (MDDW) or different (FREE) gradient directions in each shell. Each
scheme supports the estimation of all the four models. The results showed that
generally three schemes generated very similar parameters, and could be all
used in future studies.

### Introduction

Multiple diffusion models are available to detect
the brain microstructure, including conventional Diffusion Tensor Imaging (DTI)
model, and more advanced models such as the Diffusion Kurtosis Imaging (DKI) [1],
Neutire Orientation Dispersion and Density Imaging (NODDI) [2] and newly
proposed Mean Apparent Propagator (MAP)-MRI [3]. In previous studies,
different sampling schemes of b-value were used for these models, and normally
the scheme was optimized for one specific model. In this study, we aim to
evaluate the schemes that could be simultaneously used for multiple diffusion
models, and three b-value sampling schemes were considered, including the
conventional diffusion spectrum imaging (DSI) acquisition scheme based on
Cartesian grid sampling in q-space, multi-shell sampling with the same (MDDW)
or different (FREE) gradient directions in each shell. ### Methods

The brain diffusion data using three sampling schemes, namely DSI, FREE and
MDDW, were acquired on a 3 T MR scanner (MAGNETOM
Prisma, Siemens Healthcare, Erlangen, Germany) for five healthy subjects.
The acquisition parameters of the three schemes with similar acquisition times were
as follows: 1) DSI, 2 b=0 and 98 diffusion
gradient directions with bmax = 3000 s/mm2, diffusion
time δ/Δ = 15.9/35.0 ms, scan time = 6 min 37 s; 2) FREE, b = 0, 1000, 2000 and 3000 s/mm2 with
30 different gradient directions in each shell respectively, δ/Δ = 15.9/35.0 ms,
scan time = 6 min 42 s; 3) MDDW, b =
0, 1000 and 2000 s/mm2 with 64 same gradient directions in each shell,
δ/Δ = 13.9/33.0 ms, scan time = 7 min 57 s. The other parameters were the same
for the three schemes: GRAPPA = 2, slice acceleration factor = 2, slice
thickness = 2.2 mm, voxel size = 2.0 × 2.0 × 2.2 mm3, and slice
number = 60. The quantitative parameters of DTI, DKI, MAP-MRI and NODDI models were
calculated using software developed in-house with Python, called NeuDiLab, which
is based on an open-resource tool DIPY (Diffusion Imaging In Python, http://nipy.org/dipy) and AMICO
(https://github.com/daducci/AMICO)
[4].
The DTI parameters included the fractional anisotropy (FA), the mean
diffusivity (MD-DTI). The DKI parameters included the mean kurtosis (MK) and
the mean diffusivity (MD-DKI). The MAP-MRI parameters included the return to
the origin probability (RTOP), the return to the axis probability (RTAP), the
return to the plane probability (RTPP), the mean square displacement (MSD)
q-space inverse variance (QIV) and the non-Gaussianity (NG). The NODDI
parameters included the intra-cellular volume fraction (ICVF), the isotropic
volume fraction (ISOVF) and the orientation dispersion index (ODI).

The structural
similarity (SSIM) index was used to evaluate the structure
similarity of parameter maps from every pair of sampling schemes [5]. In
addition, two region of interest (ROI) with > 100 voxels were drawn in the
gray matter (GM) and the white matter (WM) regions, respectively. The mean
value for each diffusion parameter, derived from DSI, FREE and MDDW models, in
ROI was compared using the direct comparison and scatter plots. ### Results

Figure 1 shows part parameter maps derived from the four diffusion
models and their corresponding SSIM maps for the three sampling schemes. The
parameter maps were very similar. Except NODDI, the SSIM maps from DTI, DKI and
MAP-MRI models showed good agreements among the three schemes (Figure 1). Three
schemes showed similar parameter values in both ROIs of GM and WM for the four
models (Figure 2), and they also showed the good correlations for all the
parameters, particularly for the DSI and FREE schemes (R2 = 0.988, p
< 0.001) (Figure 3). ### Discussion

In the
comparison of three sampling schemes using DTI, DKI, MAP-MRI and NODDI, the three
schemes showed similar outcomes visually and quantitatively. Compared with the
DSI and FREE schemes, the MDDW scheme showed slight larger differences in a few
quantitative parameters of GM and WM, which might be due to the lower maximum b-value
and lower number of gradient direction in MDDW scheme. Generally, three schemes
generated very similar parameters, and could be all used in future studies.
Meanwhile, considering potential application of the tractography, DSI and FREE
schemes will be recommended as they used more gradient directions and could
better solve the fiber crossing problem. ### Acknowledgements

No acknowledgement found.### References

[1] Hui ES, et al. Neuroimage. 2015.

[2] Zhang H, et al. Neuroimage. 2012.

[3] Özarslan E, et al. NeuroImage. 2013.

[4] Daducci A, et al. NeuroImage. 2015.

[5] Wang Z, et al. IEEE
Trans Image Processing. 2004.