TY  - JOUR
AU  - German, Alexander
AU  - Mennecke, Angelika
AU  - Martin, Jan
AU  - Hanspach, Jannis
AU  - Liebert, Andrzej
AU  - Herrler, Jürgen
AU  - Kuder, Tristan Anselm
AU  - Schmidt, Manuel
AU  - Nagel, Armin
AU  - Uder, Michael
AU  - Doerfler, Arnd
AU  - Winkler, Jürgen
AU  - Zaiss, Moritz
AU  - Laun, Frederik Bernd
TI  - Brain tissues have single-voxel signatures in multi-spectral MRI.
JO  - NeuroImage
VL  - 234
SN  - 1053-8119
CY  - Orlando, Fla.
PB  - Academic Press
M1  - DKFZ-2021-00713
SP  - 117986
PY  - 2021
N1  - Volume 234, 1 July 2021, 117986
AB  - Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: Ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60
LB  - PUB:(DE-HGF)16
C6  - pmid:33757906
DO  - DOI:10.1016/j.neuroimage.2021.117986
UR  - https://inrepo02.dkfz.de/record/168148
ER  -