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Brain tissues have single-voxel signatures in multi-spectral MRI

German, Alexander ; Mennecke, Angelika ; Martin, Jan LU ; Hanspach, Jannis ; Liebert, Andrzej ; Herrler, Jürgen ; Kuder, Tristan Anselm ; Schmidt, Manuel ; Nagel, Armin and Uder, Michael , et al. (2021) In NeuroImage 234.
Abstract

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... (More)

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%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain, Data Analysis, High-Field Imaging, Machine Learning, MRI, Segmentation
in
NeuroImage
volume
234
article number
117986
publisher
Elsevier
external identifiers
  • pmid:33757906
  • scopus:85103695851
ISSN
1053-8119
DOI
10.1016/j.neuroimage.2021.117986
language
English
LU publication?
yes
id
dcc1392a-c792-4dcc-9e35-e9aaf2b31363
date added to LUP
2021-12-22 14:57:18
date last changed
2024-03-23 16:16:41
@article{dcc1392a-c792-4dcc-9e35-e9aaf2b31363,
  abstract     = {{<p>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%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.</p>}},
  author       = {{German, Alexander and Mennecke, Angelika and Martin, Jan and Hanspach, Jannis and Liebert, Andrzej and Herrler, Jürgen and Kuder, Tristan Anselm and Schmidt, Manuel and Nagel, Armin and Uder, Michael and Doerfler, Arnd and Winkler, Jürgen and Zaiss, Moritz and Laun, Frederik Bernd}},
  issn         = {{1053-8119}},
  keywords     = {{Brain; Data Analysis; High-Field Imaging; Machine Learning; MRI; Segmentation}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{NeuroImage}},
  title        = {{Brain tissues have single-voxel signatures in multi-spectral MRI}},
  url          = {{http://dx.doi.org/10.1016/j.neuroimage.2021.117986}},
  doi          = {{10.1016/j.neuroimage.2021.117986}},
  volume       = {{234}},
  year         = {{2021}},
}