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@ARTICLE{Pan:292609,
      author       = {L. Pan$^*$ and C. Sachpekidis$^*$ and J. Hassel and P.
                      Christopoulos and A. Dimitrakopoulou-Strauss$^*$},
      title        = {{I}mpact of different parametric {P}atlak imaging
                      approaches and comparison with a 2-tissue compartment
                      pharmacokinetic model with a long axial field-of-view
                      ({LAFOV}) {PET}/{CT} in oncological patients.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {52},
      number       = {2},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2024-01835},
      pages        = {623-637},
      year         = {2025},
      note         = {#EA:E060#LA:E060# / 2025 Jan;52(2):623-637},
      abstract     = {The recently introduced Long-Axial-Field-of-View (LAFOV)
                      PET-CT scanners allow for the first-time whole-body dynamic-
                      and parametric imaging. Primary aim of this study was the
                      comparison of direct and indirect Patlak imaging as well as
                      the comparison of different time frames for Patlak
                      calculation with the LAFOV PET-CT in oncological patients.
                      Secondary aims of the study were lesion detectability and
                      comparison of Patlak analysis with a two-tissue-compartment
                      model (2TCM).50 oncological patients with 346 tumor lesions
                      were enrolled in the study. All patients underwent [18F]FDG
                      PET/CT (skull to upper thigh). Here, the
                      Image-Derived-Input-Function) (IDIF) from the descending
                      aorta was used as the exclusive input function. Four sets of
                      images have been reviewed visually and evaluated
                      quantitatively using the target-to-background (TBR) and
                      contrast-to-noise ratio (CNR): short-time (30 min)-direct
                      (STD) Patlak Ki, short-time (30 min)-indirect (STI) Patlak
                      Ki, long-time (59.25 min)-indirect (LTI) Patlak Ki, and
                      50-60 min SUV (sumSUV). VOI-based 2TCM was used for the
                      evaluation of tumor lesions and normal tissues and compared
                      with the results of Patlak model.No significant differences
                      were observed between the four approaches regarding the
                      number of tumor lesions. However, we found three discordant
                      results: a true positive liver lesion in all Patlak Ki
                      images, a false positive liver lesion delineated only in LTI
                      Ki which was a hemangioma according to MRI and a true
                      negative example in a patient with an atelectasis next to a
                      lung tumor. STD, STI and LTI Ki images had superior TBR in
                      comparison with sumSUV images (2.9-, 3.3- and 4.3-fold
                      higher respectively). TBR of LTI Ki were significantly
                      higher than STD Ki. VOI-based k3 showed a 21-fold higher TBR
                      than sumSUV. Parameters of different models vary in their
                      differential capability between tumor lesions and normal
                      tissue like Patlak Ki which was better in normal lung and
                      2TCM k3 which was better in normal liver. 2TCM Ki revealed
                      the highest correlation (r = 0.95) with the LTI Patlak Ki in
                      tumor lesions group and demonstrated the highest correlation
                      with the STD Patlak Ki in all tissues group and normal
                      tissues group (r = 0.93 and r = 0.74 respectively).Dynamic
                      [18F]-FDG with the new LAFOV PET/CT scanner produces Patlak
                      Ki images with better lesion contrast than SUV images, but
                      does not increase the lesion detection rate. The time window
                      used for Patlak imaging plays a more important role than the
                      direct or indirect method. A combination of different
                      models, like Patlak and 2TCM may be helpful in parametric
                      imaging to obtain the best TBR in the whole body in future.},
      keywords     = {2-tissue compartment model (Other) / Kinetic modelling
                      (Other) / Long axial field-of-view PET/CT (Other) / Patlak
                      parametric imaging (Other) / [18F]FDG PET/CT (Other)},
      cin          = {E060},
      ddc          = {610},
      cid          = {I:(DE-He78)E060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39256215},
      doi          = {10.1007/s00259-024-06879-4},
      url          = {https://inrepo02.dkfz.de/record/292609},
}