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@ARTICLE{Schneider:169797,
      author       = {T. M. Schneider and J. Ma and P. Wagner and N. Behl$^*$ and
                      A. M. Nagel$^*$ and M. E. Ladd$^*$ and S. Heiland and M.
                      Bendszus and S. Straub$^*$},
      title        = {{M}ultiparametric {MRI} for {C}haracterization of the
                      {B}asal {G}anglia and the {M}idbrain.},
      journal      = {Frontiers in neuroscience},
      volume       = {15},
      issn         = {1662-453X},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {DKFZ-2021-01551},
      pages        = {661504},
      year         = {2021},
      note         = {#LA:E020#},
      abstract     = {Objectives To characterize subcortical nuclei by
                      multi-parametric quantitative magnetic resonance imaging.
                      Materials and Methods: The following quantitative
                      multiparametric MR data of five healthy volunteers were
                      acquired on a 7T MRI system: 3D gradient echo (GRE) data for
                      the calculation of quantitative susceptibility maps (QSM),
                      GRE sequences with and without off-resonant magnetic
                      transfer pulse for magnetization transfer ratio (MTR)
                      calculation, a magnetization-prepared 2 rapid acquisition
                      gradient echo sequence for T1 mapping, and (after a coil
                      change) a density-adapted 3D radial pulse sequence for 23Na
                      imaging. First, all data were co-registered to the GRE data,
                      volumes of interest (VOIs) for 21 subcortical structures
                      were drawn manually for each volunteer, and a combined
                      voxel-wise analysis of the four MR contrasts (QSM, MTR, T1,
                      23Na) in each structure was conducted to assess the
                      quantitative, MR value-based differentiability of
                      structures. Second, a machine learning algorithm based on
                      random forests was trained to automatically classify the
                      groups of multi-parametric voxel values from each VOI
                      according to their association to one of the 21 subcortical
                      structures. Results The analysis of the integrated
                      multimodal visualization of quantitative MR values in each
                      structure yielded a successful classification among nuclei
                      of the ascending reticular activation system (ARAS), the
                      limbic system and the extrapyramidal system, while
                      classification among (epi-)thalamic nuclei was less
                      successful. The machine learning-based approach facilitated
                      quantitative MR value-based structure classification
                      especially in the group of extrapyramidal nuclei and reached
                      an overall accuracy of $85\%$ regarding all selected nuclei.
                      Conclusion Multimodal quantitative MR enabled excellent
                      differentiation of a wide spectrum of subcortical nuclei
                      with reasonable accuracy and may thus enable sensitive
                      detection of disease and nucleus-specific MR-based contrast
                      alterations in the future.},
      keywords     = {basal ganglia (Other) / machine learning (Other) / magnetic
                      resonance imaging (Other) / magnetization transfer (Other) /
                      quantitative susceptibility mapping (Other) / sodium imaging
                      (Other) / ultra high field (Other)},
      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:34234639},
      pmc          = {pmc:PMC8255625},
      doi          = {10.3389/fnins.2021.661504},
      url          = {https://inrepo02.dkfz.de/record/169797},
}