% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Greenspan:265790,
      author       = {Pinetz, Thomas and Kobler, Erich and Haase, Robert and
                      Deike-Hofmann, Katerina and Radbruch, Alexander and Effland,
                      Alexander},
      editor       = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin
                      and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood,
                      Tanveer and Taylor, Russell},
      title        = {{F}aithful {S}ynthesis of {L}ow-{D}ose
                      {C}ontrast-{E}nhanced {B}rain {MRI} {S}cans {U}sing
                      {N}oise-{P}reserving {C}onditional {GAN}s},
      volume       = {14221},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {DZNE-2023-01039},
      isbn         = {978-3-031-43894-3 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {607 - 617},
      year         = {2023},
      comment      = {Medical Image Computing and Computer Assisted Intervention
                      – MICCAI 2023 / Greenspan, Hayit (Editor) ; Cham :
                      Springer Nature Switzerland, 2023, Chapter 57 ; ISSN:
                      0302-9743=1611-3349 ; ISBN:
                      978-3-031-43894-3=978-3-031-43895-0 ;
                      doi:10.1007/978-3-031-43895-0},
      booktitle     = {Medical Image Computing and Computer
                       Assisted Intervention – MICCAI 2023 /
                       Greenspan, Hayit (Editor) ; Cham :
                       Springer Nature Switzerland, 2023,
                       Chapter 57 ; ISSN: 0302-9743=1611-3349
                       ; ISBN:
                       978-3-031-43894-3=978-3-031-43895-0 ;
                       doi:10.1007/978-3-031-43895-0},
      abstract     = {Today Gadolinium-based contrast agents (GBCA) are
                      indispensable in Magnetic Resonance Imaging (MRI) for
                      diagnosing various diseases. However, GBCAs are expensive
                      and may accumulate in patients with potential side effects,
                      thus dose-reduction is recommended. Still, it is unclear to
                      which extent the GBCA dose can be reduced while preserving
                      the diagnostic value – especially in pathological regions.
                      To address this issue, we collected brain MRI scans at
                      numerous non-standard GBCA dosages and developed a
                      conditional GAN model for synthesizing corresponding images
                      at fractional dose levels. Along with the adversarial loss,
                      we advocate a novel content loss function based on the
                      Wasserstein distance of locally paired patch statistics for
                      the faithful preservation of noise. Our numerical
                      experiments show that conditional GANs are suitable for
                      generating images at different GBCA dose levels and can be
                      used to augment datasets for virtual contrast models.
                      Moreover, our model can be transferred to openly available
                      datasets such as BraTS, where non-standard GBCA dosage
                      images do not exist. © The Author(s), under exclusive
                      license to Springer Nature Switzerland AG 2023.},
      month         = {Oct},
      date          = {2023-10-08},
      organization  = {26th International Conference on
                       Medical Image Computing and
                       Computer-Assisted Intervention, MICCAI
                       2023, Vancouver (Canada), 8 Oct 2023 -
                       12 Oct 2023},
      cin          = {AG Radbruch},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-031-43895-0_57},
      url          = {https://pub.dzne.de/record/265790},
}