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“MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation
Author(s) -
Froeling Martijn,
Tax Chantal M.W.,
Vos Sjoerd B.,
Luijten Peter R.,
Leemans Alexander
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26259
Subject(s) - diffusion mri , computer science , fractional anisotropy , magnetic resonance imaging , artificial intelligence , fluid attenuated inversion recovery , standardization , data set , data mining , pattern recognition (psychology) , nuclear medicine , medicine , radiology , operating system
Purpose In this work, we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development. Methods MRI data of one healthy subject (female, 25 years) were acquired on a clinical 3 Tesla system (Philips Achieva) with an eight‐channel head coil. In total, the subject was scanned on 18 different occasions with a total acquisition time of 22.5 h. The dMRI data were acquired with an isotropic resolution of 2.5 mm 3 and distributed over five shells with b‐values up to 4000 s/mm 2 and two Cartesian grids with b‐values up to 9000 s/mm 2 . Results The final dataset consists of 8000 dMRI volumes, corresponding B 0 field maps and noise maps for subsets of the dMRI scans, and ten three‐dimensional FLAIR, T 1 ‐, and T 2 ‐weighted scans. The average signal‐to‐noise‐ratio of the non–diffusion‐weighted images was roughly 35. Conclusion This unique set of in vivo MRI data will provide a robust framework to evaluate novel diffusion processing techniques and to reliably compare different approaches for diffusion modeling. The MASSIVE dataset is made publically available (both unprocessed and processed) on www.massive-data.org . Magn Reson Med 77:1797–1809, 2017. © 2016 International Society for Magnetic Resonance in Medicine