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@ARTICLE{Liebert:294309,
      author       = {A. Liebert and H. Schreiter and L. A. Kapsner and J. Eberle
                      and C. M. Ehring and D. Hadler and L. Brock and R. Erber and
                      J. Emons and F. B. Laun and M. Uder and E. Wenkel and S.
                      Ohlmeyer and S. Bickelhaupt$^*$},
      title        = {{I}mpact of non-contrast-enhanced imaging input sequences
                      on the generation of virtual contrast-enhanced breast {MRI}
                      scans using neural network.},
      journal      = {European radiology},
      volume       = {35},
      number       = {5},
      issn         = {0938-7994},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2024-02142},
      pages        = {2603-2616},
      year         = {2025},
      note         = {#LA:E250# / 2025 May;35(5):2603-2616},
      abstract     = {To investigate how different combinations of T1-weighted
                      (T1w), T2-weighted (T2w), and diffusion-weighted imaging
                      (DWI) impact the performance of virtual contrast-enhanced
                      (vCE) breast MRI.The IRB-approved, retrospective study
                      included 1064 multiparametric breast MRI scans (age: 52 ±
                      12 years) obtained from 2017 to 2020 (single site, two 3-T
                      MRI). Eleven independent neural networks were trained to
                      derive vCE images from varying input combinations of T1w,
                      T2w, and multi-b-value DWI sequences (b-value = 50-1500
                      s/mm2). Three readers evaluated the vCE images with regard
                      to qualitative scores of diagnostic image quality, image
                      sharpness, satisfaction with contrast/signal-to-noise ratio,
                      and lesion/non-mass enhancement conspicuity. Quantitative
                      metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy)
                      were analyzed and statistically compared between the input
                      combinations for the full breast volume and both enhancing
                      and non-enhancing target findings.The independent test set
                      consisted of 187 cases. The quantitative metrics
                      significantly improved in target findings when multi-b-value
                      DWI sequences were included during vCE training (p < 0.05).
                      Non-significant effects (p > 0.05) were observed for the
                      quantitative metrics on the full breast volume when
                      comparing input combinations including T1w. Using T1w and
                      DWI acquisitions during vCE training is necessary to achieve
                      high satisfaction with contrast/SNR and good conspicuity of
                      the enhancing findings. The input combination of T1w, T2w,
                      and DWI sequences with three b-values showed the best
                      qualitative performance.vCE breast MRI performance is
                      significantly influenced by input sequences. Quantitative
                      metrics and visual quality of vCE images significantly
                      benefit when multi b-value DWI is added to morphologic
                      T1w-/T2w sequences as input for model training.Question How
                      do different MRI sequences impact the performance of virtual
                      contrast-enhanced (vCE) breast MRI? Findings The input
                      combination of T1-weighted, T2-weighted, and
                      diffusion-weighted imaging sequences with three b-values
                      showed the best qualitative performance. Clinical relevance
                      While in the future neural networks providing virtual
                      contrast-enhanced images might further improve accessibility
                      to breast MRI, the significant influence of input data needs
                      to be considered during translational research.},
      keywords     = {Artificial intelligence (Other) / Breast imaging (Other) /
                      Magnetic resonance imaging (Other) / Neural network (Other)},
      cin          = {E250},
      ddc          = {610},
      cid          = {I:(DE-He78)E250-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39455455},
      doi          = {10.1007/s00330-024-11142-3},
      url          = {https://inrepo02.dkfz.de/record/294309},
}