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@ARTICLE{Haase:274031,
      author       = {Haase, Robert and Pinetz, Thomas and Kobler, Erich and
                      Bendella, Zeynep and Gronemann, Christian and Paech, Daniel
                      and Radbruch, Alexander and Effland, Alexander and
                      Deike-Hofmann, Katerina},
      title        = {{A}rtificial {T}1-{W}eighted {P}ostcontrast {B}rain {MRI}:
                      {A} {D}eep {L}earning {M}ethod for {C}ontrast {S}ignal
                      {E}xtraction.},
      journal      = {Investigative radiology},
      volume       = {60},
      number       = {2},
      issn         = {0020-9996},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {Ovid},
      reportid     = {DZNE-2025-00012},
      pages        = {105 - 113},
      year         = {2025},
      abstract     = {Reducing gadolinium-based contrast agents to lower costs,
                      the environmental impact of gadolinium-containing
                      wastewater, and patient exposure is still an unresolved
                      issue. Published methods have never been compared. The
                      purpose of this study was to compare the performance of 2
                      reimplemented state-of-the-art deep learning methods
                      (settings A and B) and a proposed method for contrast signal
                      extraction (setting C) to synthesize artificial T1-weighted
                      full-dose images from corresponding noncontrast and low-dose
                      images.In this prospective study, 213 participants received
                      magnetic resonance imaging of the brain between August and
                      October 2021 including low-dose (0.02 mmol/kg) and full-dose
                      images (0.1 mmol/kg). Fifty participants were randomly set
                      aside as test set before training (mean age ± SD, 52.6 ±
                      15.3 years; 30 men). Artificial and true full-dose images
                      were compared using a reader-based study. Two readers noted
                      all false-positive lesions and scored the overall
                      interchangeability in regard to the clinical conclusion.
                      Using a 5-point Likert scale (0 being the worst), they
                      scored the contrast enhancement of each lesion and its
                      conformity to the respective reference in the true image.The
                      average counts of false-positives per participant were 0.33
                      ± 0.93, 0.07 ± 0.33, and 0.05 ± 0.22 for settings A-C,
                      respectively. Setting C showed a significantly higher
                      proportion of scans scored as fully or mostly
                      interchangeable (70/100) than settings A (40/100, P < 0.001)
                      and B (57/100, P < 0.001), and generated the smallest mean
                      enhancement reduction of scored lesions (-0.50 ± 0.55)
                      compared with the true images (setting A: -1.10 ± 0.98;
                      setting B: -0.91 ± 0.67, both P < 0.001). The average
                      scores of conformity of the lesion were 1.75 ± 1.07, 2.19
                      ± 1.04, and 2.48 ± 0.91 for settings A-C, respectively,
                      with significant differences among all settings (all P <
                      0.001).The proposed method for contrast signal extraction
                      showed significant improvements in synthesizing postcontrast
                      images. A relevant proportion of images showing inadequate
                      interchangeability with the reference remains at this
                      dosage.},
      keywords     = {Humans / Deep Learning / Male / Contrast Media / Female /
                      Prospective Studies / Magnetic Resonance Imaging: methods /
                      Middle Aged / Brain: diagnostic imaging / Adult / Contrast
                      Media (NLM Chemicals)},
      cin          = {AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39074258},
      doi          = {10.1097/RLI.0000000000001107},
      url          = {https://pub.dzne.de/record/274031},
}