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@ARTICLE{Shah:906426,
      author       = {Shah, N. J. and Abbas, Zaheer and Ridder, Dominik and
                      Zimmermann, Markus and Oros-Peusquens, Ana-Maria},
      title        = {{A} {N}ovel {MRI}-{B}ased {Q}uantitative {W}ater {C}ontent
                      {A}tlas of the {H}uman {B}rain},
      journal      = {NeuroImage},
      volume       = {252},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2022-01438},
      pages        = {119014 -},
      year         = {2022},
      abstract     = {The measurement of quantitative, tissue-specific MR
                      properties, e.g., water content, longitudinal relaxation
                      time (T1) and effective transverse relaxation time (T2*),
                      using quantitative MRI at a clinical field strength (1.5 T
                      to 3T) is a well-explored topic. However, none of the
                      commonly used standard brain atlases, such as MNI or JHU,
                      provide quantitative information. Within the framework of
                      quantitative MRI of the brain, this work reports on the
                      development of the first quantitative brain atlas for tissue
                      water content at 3T. A methodology to create this
                      quantitative atlas of in vivo brain water content based on
                      healthy volunteers is presented, and preliminary, practical
                      examples of its potential applications are also
                      shown.Established methods for the fast and reliable
                      measurement of the absolute water content were used to
                      achieve high precision and accuracy. Water content and T2*
                      were mapped based on two different methods: an
                      intermediate-TR, two-point method and a long-TR, single-scan
                      method. Twenty healthy subjects (age 25.3 ± 2.5 years) were
                      examined with these quantitative imaging protocols. The
                      images were normalised to MNI stereotactic coordinates, and
                      water content atlases of healthy volunteers were created for
                      each method and compared. Regions-of-interest were generated
                      with the help of a standard MNI template, and water content
                      values averaged across the ROIs were compared to water
                      content values from the literature.Finally, in order to
                      demonstrate the strength of quantitative MRI, water content
                      maps from patients with pathological changes in the brain
                      due to stroke, tumour (glioblastoma) and multiple sclerosis
                      were voxel-wise compared to the healthy brain.The water
                      content atlases were largely independent of the method used
                      to acquire the individual water maps. Global grey matter and
                      white matter water content values between the methods agreed
                      with each other to within 0.5 $\%.$ The feasibility of
                      detecting abnormal water content in the brains of patients
                      based on comparison to a healthy brain water content atlas
                      was demonstrated.In summary, the first quantitative water
                      content brain atlas in vivo has been developed and a
                      voxel-wise assessment of pathology-related changes in the
                      brain water content has been performed. These results
                      suggest that qMRI, in combination with a water content
                      atlas, allows for a quantitative interpretation of changes
                      due to disease and could be used for disease monitoring.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35202813},
      UT           = {WOS:000766272000003},
      doi          = {10.1016/j.neuroimage.2022.119014},
      url          = {https://juser.fz-juelich.de/record/906426},
}