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In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution.

Authors :
Wang, Fuyixue
Dong, Zijing
Tian, Qiyuan
Liao, Congyu
Fan, Qiuyun
Hoge, W. Scott
Keil, Boris
Polimeni, Jonathan R.
Wald, Lawrence L.
Huang, Susie Y.
Setsompop, Kawin
Source :
Scientific Data; 4/29/2021, Vol. 8 Issue 1, p1-12, 12p
Publication Year :
2021

Abstract

We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T<subscript>1</subscript>-weighted and T<subscript>2</subscript>-weighted images at submillimeter scale along with field maps are also made available. Measurement(s) brain measurement Technology Type(s) magnetic resonance imaging Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14058443 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
8
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
150064094
Full Text :
https://doi.org/10.1038/s41597-021-00904-z