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Dataset Closed Access

EDEN2020 Human Brain MRI Datasets for Healthy Volunteers

Castellano, Antonella; Falini, Andrea

Data manager(s)
Galvan, Stefano
Project leader(s)
Rodriguez y Baena, Ferdinando
Project member(s)
Pieri, Valentina

High-resolution  MR datasets of a cohort of 15 healthy adult subjects acquired on  a  3T  scanner  at  the Neuroradiology Unit and CERMAC (Center  of Excellence for High Field Magnetic Resonance), Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy. The data includes:

  • T1_3D_PROSET_Sag:  T1-weighted  volumetric  sequence  acquired  on  the sagittal planefor  morphological  characterization.  This  sequence  demonstrates  difference  in  the  T1 relaxation time of tissues and provide excellent contrast between GM and WM.
  • 3D_FLAIR_Tra: Fluid‑Attenuated Inversion Recovery volumetric sequence acquired on the axial planefor morphological characterization. This is an inversion recovery sequence with a long inversion time (TI), which results in removing signal from the cerebrospinal fluid from the images.
  • SWIp_axial: Susceptibility‑Weighted Imaging sequence acquired on the axial plane.This  is  a three-dimensional  high-spatial  resolution  Gradient  Echo  MRI  sequence providing excellent contrast for venous vascular modeling.
  • s3DI_MC_HR: three‑dimensional   high‑resolution   time‑of‑flight   (TOF)   MR angiography  acquisition  to visualize  flow  within  the  arterial  vessel.  It  is  based  on  the phenomenon of flow-related enhancement of spins entering into an imaging slice. As a result of being unsaturated, these spins give more signal that surrounding stationary spins.
  • MIP_s3DI_MC_HR:  angiographic  3D  visualization  of  TOF  images  using  the maximum intensity projection (MIP) technique of reconstruction.
  • raw_data_DTI_32: Diffusion Tensor Imaging raw data. This is a diffusion-weighted Spin  Echo  EPI  single-shot  pulse  sequence  acquired  on  the axial  planealong  32  gradient directions at a b-value of 1000 s/mm2 and one volume without diffusion-weighting (b0 image).
  • raw_data_NODDI: multi-compartmental dMRI sequence for advanced tractography and  NODDI  analyses,  including  an  axial  high  angular  resolution  diffusion-weighted  imaging (HARDI) acquisition along 60 gradient directions at a b-value of 3000 s/mm2,a DTI acquisition along 35 directions at a b-value of 711 s/mm2 and 11 volumes without diffusion-weighting  (b0  images).  The  phase-encoding  direction was  anterior-to-posterior for all these acquisitions.
  • B0_reverse: a sequence without diffusion-weighting having the same geometrical parameters of the ‘raw_data_NODDI’ images, but acquired using a reversed phase-encoding direction (posterior-to-anterior).   This   volume   allowed   estimation   and   correction for susceptibility-induced distortions.

‘DTI’ Folder’: This folder contains the DTI-derived parametric maps calculated off-linefrom the ‘raw_data_DTI_32’ acquisition (32 gradient directions, b-value = 1000 s/mm2) and saved in the NIfTI-1 Data Format.

‘HARDI’ Folder: This folder contains the parametric maps calculated off-linefrom the HARDI acquisition (60 gradient directions, b-value = 3000 s/mm2) of the ‘raw_data_NODDI’ sequence. Maps are saved in the NIfTI-1 Data Format.

‘Tractography’ Folder: This  folder  contains  the  probabilistic  tractography  reconstructions  of  the  main  white  matter  fiber tracts,  calculated  from  the  HARDI  acquisition (60  gradient  directions, b-value  =  3000  s/mm2)  of  the ‘raw_data_NODDI’ sequence. Dipy has been used for q-ball residual-bootstrap fiber tracking. The folder contains a minimum number of two pair of tracts for each subjects.

‘NODDI’ Folder: This folder contains the Neurite orientation dispersion and density imaging (NODDI) parametric maps calculated off-line from the ‘raw_data_NODDI’ acquisition (60 gradient directions at b=3000 s/mm2, 35  gradient  directions  at b=711  s/mmand  11  b0  volumes)  and  saved  in  the  NIfTI-1  Data  Format. Maps have been generated using the NODDI Matlab Toolbox (https://www.nitrc.org/projects/noddi_toolbox).

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