Data was collected from an ex vivo fixed Vervet (Chlorocebus aethiops) monkey brain, obtained from the Montreal Monkey Brain Bank. The monkey, cared for on the island of St. Kitts, had been treated in line with a protocol approved by The Caribbean Primate Center of St. Kitts. The brain had previously been stored and prepared according to Dyrby et al. [1]. The data was collected with a Bruker Biospec 70/20 7.0 T scanner (Billerica, Massachusetts, USA) using a quadrature RF coil (300 MHz). The brain was let to reach room temperature and to mechanically stabilize prior the start of the acquisition. The acquisition was conducted using a constant air flow directed towards the brain to avoid short-term instability artifacts [1]. A single-line readout PGSE sequence with pulse duration δ = 9.6 ms and separation Δ = 17.5 ms was used to collect the data organized in five shells with b = 4000 s/mm2, 7000 s/mm2, 23,000 s/mm2, 27,000 s/mm2, and 31,000 s/mm2 each containing the same 96 directions which were obtained by electrostatic repulsion [2], plus b = 0 images. One dataset was collected at echo time TE = 35.5 ms and the other at TE = 45.5 ms.

- Denoising was performed [3].
- Gibbs ringing removal was performed [4].

More details can be found at

Marco Pizzolato, Mariam Andersson, Erick Jorge Canales-Rodríguez, Jean-Philippe Thiran, Tim B. Dyrby,
Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal,
Magnetic Resonance Imaging,
2021, ISSN 0730-725X,
https://doi.org/10.1016/j.mri.2021.11.012.


Some relevant files:
- how_to_cite.txt reports an example of how to cite the data
- citation.bib and citation.txt contain the citation to the journal article
- M0683_TR_3500_TE_35.5_d_09.6_D_17.5_dirs_96_D_G_wMax_06_axes_02.nii.gz (data collected at TE=35.5 ms)
- M0683_TR_3500_TE_45.5_d_09.6_D_17.5_dirs_96_D_G_wMax_06_axes_02.nii.gz (data collected at TE=45.5 ms)
- files "_scheme.txt" contain the scheme files structured as indicated in the file "scheme_columns.txt"
- files "_scheme.pkl" contain the pandas DataFrame version of the corresponding scheme file
- mask.nii.gz is a maks of the brain
- mask_wm.nii.gz is a conservative mask of the white matter
- the zip file derivatives contains
	- spherical mean and variance T2 maps calculated at the different b-values 
	- FA map calculated using the lower shells as indicated in the article
	- the count of peaks obtained with Constrained Spherical Deconvolution as indicated in the article

You can contact Marco Pizzolato [pizzolato.marco.research@gmail.com, marcop@drcmr.dk] for any issues.

References:

[1] T.B. Dyrby, W.F. Baaré, D.C. Alexander, J. Jelsing, E. Garde, L.V. Søgaard
An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets Hum. Brain Mapp., 32 (2011), pp. 544-563

[2] D.K. Jones, M.A. Horsfield, A. Simmons
Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging
Magn Reson Med Official J Int Soc Magn Reson Med, 42 (1999), pp. 515-525

[3] X. Ma, K. Uğurbil, X. Wu
Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation
Neuroimage, 215 (2020), p. 116852

[4] E. Kellner, B. Dhital, V.G. Kiselev, M. Reisert
Gibbs-ringing artifact removal based on local subvoxel-shifts
Magn. Reson. Med., 76 (2016), pp. 1574-1581