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@ARTICLE{German:168148,
      author       = {A. German and A. Mennecke and J. Martin and J. Hanspach and
                      A. Liebert and J. Herrler and T. A. Kuder$^*$ and M. Schmidt
                      and A. Nagel and M. Uder and A. Doerfler and J. Winkler and
                      M. Zaiss and F. B. Laun},
      title        = {{B}rain tissues have single-voxel signatures in
                      multi-spectral {MRI}.},
      journal      = {NeuroImage},
      volume       = {234},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DKFZ-2021-00713},
      pages        = {117986},
      year         = {2021},
      note         = {Volume 234, 1 July 2021, 117986},
      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 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.},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33757906},
      doi          = {10.1016/j.neuroimage.2021.117986},
      url          = {https://inrepo02.dkfz.de/record/168148},
}