Martijn Froeling1, Chantal M W Tax2, Sjoerd B Vos2,3, Peter R Luijten1, Alexander Leemans2. 1. Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands. 2. Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands. 3. Translational Imaging Group, CMIC, University College London, London, United Kingdom.
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 mm3 and distributed over five shells with b-values up to 4000 s/mm2 and two Cartesian grids with b-values up to 9000 s/mm2 . 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.
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 mm3 and distributed over five shells with b-values up to 4000 s/mm2 and two Cartesian grids with b-values up to 9000 s/mm2 . 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.
Authors: R Rehmann; L Schlaffke; M Froeling; R A Kley; E Kühnle; M De Marées; J Forsting; M Rohm; M Tegenthoff; T Schmidt-Wilcke; M Vorgerd Journal: Eur Radiol Date: 2018-12-17 Impact factor: 5.315
Authors: Carmen Tur; Francesco Grussu; Ferran Prados; Thalis Charalambous; Sara Collorone; Baris Kanber; Niamh Cawley; Daniel R Altmann; Sébastien Ourselin; Frederik Barkhof; Jonathan D Clayden; Ahmed T Toosy; Claudia Am Gandini Wheeler-Kingshott; Olga Ciccarelli Journal: Mult Scler Date: 2019-05-10 Impact factor: 6.312
Authors: Chantal M W Tax; Tom Dela Haije; Andrea Fuster; Carl-Fredrik Westin; Max A Viergever; Luc Florack; Alexander Leemans Journal: Neuroimage Date: 2016-07-25 Impact factor: 6.556