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

Author:

Wang FuyixueORCID,Dong Zijing,Tian QiyuanORCID,Liao Congyu,Fan Qiuyun,Hoge W. Scott,Keil BorisORCID,Polimeni Jonathan R.ORCID,Wald Lawrence L.,Huang Susie Y.ORCID,Setsompop Kawin

Abstract

AbstractWe 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 T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

Funder

U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering

U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke

U.S. Department of Health & Human Services | NIH | National Institute of Mental Health

U.S. Department of Health & Human Services | NIH | National Center for Research Resources

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Reference87 articles.

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