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@ARTICLE{Lerche:891299,
      author       = {Lerche, Christoph W. and Radomski, Timon and Lohmann,
                      Philipp and Caldeira, Liliana and Brambilla, Claudia Regio
                      and Tellmann, Lutz and Scheins, Jurgen and Kops, Elena Rota
                      and Galldiks, Norbert and Langen, Karl-Josef and Herzog,
                      Hans and Shah, N. J.},
      title        = {{A} {L}inearized {F}it {M}odel for {R}obust {S}hape
                      {P}arameterization of {FET}-{PET} {TAC}s},
      journal      = {IEEE transactions on medical imaging},
      volume       = {40},
      number       = {7},
      issn         = {1558-254X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-01406},
      pages        = {1852 - 1862},
      year         = {2021},
      abstract     = {The kinetic analysis of 18F -FET time-activity curves (TAC)
                      can provide valuable diagnostic information in glioma
                      patients. The analysis is most often limited to the average
                      TAC over a large tissue volume and is normally assessed by
                      visual inspection or by evaluating the time-to-peak and
                      linear slope during the late uptake phase. Here, we derived
                      and validated a linearized model for TACs of 18F -FET in
                      dynamic PET scans. Emphasis was put on the robustness of the
                      numerical parameters and how reliably automatic voxel-wise
                      analysis of TAC kinetics was possible. The diagnostic
                      performance of the extracted shape parameters for the
                      discrimination between isocitrate dehydrogenase (IDH)
                      wildtype (wt) and IDH-mutant (mut) glioma was assessed by
                      receiver-operating characteristic in a group of 33 adult
                      glioma patients. A high agreement between the adjusted model
                      and measured TACs could be obtained and relative, estimated
                      parameter uncertainties were small. The best differentiation
                      between IDH-wt and IDH-mut gliomas was achieved with the
                      linearized model fitted to the averaged TAC values from
                      dynamic FET PET data in the time interval 4–50 min p.i..
                      When limiting the acquisition time to 20–40 min p.i.,
                      classification accuracy was only slightly lower $(-3\%)$ and
                      was comparable to classification based on linear fits in
                      this time interval. Voxel-wise fitting was possible within a
                      computation time ≈ 1 min per image slice. Parameter
                      uncertainties smaller than $80\%$ for all fits with the
                      linearized model were achieved. The agreement of best-fit
                      parameters when comparing voxel-wise fits and fits of
                      averaged TACs was very high (p < 0.001).},
      cin          = {INM-4 / INM-11 / INM-3 / JARA-BRAIN},
      ddc          = {620},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)VDB1046},
      pnm          = {525 - Decoding Brain Organization and Dysfunction
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-525},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33735076},
      UT           = {WOS:000668842500010},
      doi          = {10.1109/TMI.2021.3067169},
      url          = {https://juser.fz-juelich.de/record/891299},
}