"MASSIVE" brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation. Issue 5 (13th May 2016)

• Record Type: Journal Article
• Authors: Froeling, Martijn; Tax, Chantal M.W.; Vos, Sjoerd B.; Luijten, Peter R.; Leemans, Alexander
• Is Part Of: Magnetic resonance in medicine. Volume 77:Issue 5(2017)
• Page Start: 1797
• Page End: 1809
• Publication Date: 2016-05-13
• Subjects: diffusion MRI -- structural MRI -- modeling -- methods development -- evaluation -- brain dataset; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.26259
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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 B0 field maps and noise maps for subsets of the dMRI scans, and ten three‐dimensional FLAIR, T1 ‐, and T2 ‐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