Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI.

Dataset description: Accompanying data for manuscript “A scalable method to improve gray matter segmentation at ultra high field MRI” written by Omer Faruk Gulban, Marian Schneider, Ingo Marquardt, Roy Haast, Federico De Martino.

Published in PLOS One, June  6, 2018.

The dataset consist of 7 Tesla MRI anatomical images of living human brains (whole brain; 0.7mm isotropic resolution; T1 weighted, T2* weighted, proton density weighted MPRAGE images; inversion 1, inversion 2, T1, uni, MP2RAGE images; Multi-echo 3D GRE) and hand labeled cortical gray matter images (for further details see section 4.1 of our manuscript).

Folder structure is organized according to Brain Imaging Data Structure (BIDS). Further details can be found the README files.

Citation

Please cite the following paper together with this dataset doi:

• Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to improve gray matter segmentation at ultra high field MRI. PLOS ONE, 13(6), e0198335. http://doi.org/10.1371/journal.pone.0198335

Bibtex format:

```
@article{Gulban2018,
author = {Gulban, Omer Faruk and Schneider, Marian and Marquardt, Ingo and Haast, Roy A. M. and {De Martino}, Federico},
doi = {10.1371/journal.pone.0198335},
editor = {Pham, Dzung},
issn = {1932-6203},
journal = {PLOS ONE},
month = {jun},
number = {6},
pages = {e0198335},
title = {{A scalable method to improve gray matter segmentation at ultra high field MRI}},
url = {http://dx.plos.org/10.1371/journal.pone.0198335},
volume = {13},
year = {2018}
}

```