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@INBOOK{Scheins:858926,
      author       = {Scheins, Jürgen and Kops, E. Rota and Caldeira, L. and Ma,
                      B.},
      title        = {{CHAPTER} 7. {PET} {Q}uantification},
      address      = {Cambridge},
      publisher    = {Royal Society of Chemistry},
      reportid     = {FZJ-2018-07762},
      series       = {New Developments in NMR},
      pages        = {162 - 182},
      year         = {2018},
      comment      = {Hybrid MR-PET Imaging / Shah, N Jon (Editor)},
      booktitle     = {Hybrid MR-PET Imaging / Shah, N Jon
                       (Editor)},
      abstract     = {A major benefit of the three-dimensional (3D) PET imaging
                      technique in neuroscience, as well as in clinical
                      applications, is that it offers the possibility of
                      dynamically quantifying metabolic processes with a
                      sensitivity of up to 10−12 mol L−1 for the tracer
                      concentration. However, all positron emission tomographs
                      provide biased data with complex dependencies, which means
                      that to obtain quantitative activity distributions in 3D, it
                      is necessary to make several corrections. For example,
                      inhomogeneous detector efficiencies, photon attenuation,
                      Compton scattering, and random coincidences need to be
                      corrected. Furthermore, dynamic imaging represents a
                      challenge, because a high temporal resolution requires short
                      acquisition time frames with rather poor statistics of
                      recorded events from the radioactive decay. Apart from the
                      necessary corrections, the applied reconstruction method has
                      an important impact on the achievable image quality in PET.
                      In this respect, iterative reconstruction methods are
                      becoming the state-of-the-art techniques as they offer
                      superior image quality when compared to analytical methods.
                      Although iterative reconstruction is associated with higher
                      computational demand, the higher calculation effort can be
                      moderated by using a range of optimisation strategies and
                      has been further helped by the remarkable boost in
                      computational resources over the last two decades.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)7},
      doi          = {10.1039/9781788013062-00162},
      url          = {https://juser.fz-juelich.de/record/858926},
}