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@INPROCEEDINGS{Masood:1038864,
      author       = {Masood, Anum and Drzezga, Alexander and Elmenhorst, E. M.
                      and Foerges, Anna Linea and Lange, Denise and Hennecke, E.
                      and Baur, D. M. and Kroll, Tina and Neumaier, Bernd and
                      Aeschbach, D. and Bauer, Andreas and Landolt, H. P. and
                      Elmenhorst, David and Beer, Simone},
      title        = {{E}nhancing {U}ltra-{L}ow-{D}ose {B}rain {PET}/{MRI}
                      {I}mage {Q}uality {U}sing {D}eep {L}earning},
      reportid     = {FZJ-2025-01680},
      year         = {2024},
      abstract     = {Aim:We developed a deep learning model to enhance the image
                      quality of ultra-low dose brain positron emission tomography
                      (PET). Significantly reducing the injected dose not only
                      minimizes radiation risk in subjects but also provides
                      options for scanning protocols, allowing e.g. for more
                      follow-up studies. Methods:Our dataset comprises 196 brain
                      PET/magnetic resonance (MRI) images (MPRAGE) of healthy
                      volunteers of two sleep deprivation studies, scanned for 100
                      minutes each using the adenosine A1 receptor ligand
                      [F-18]CPFPX (mean injected activity: 179.5 MBq, 10 MBq SD).
                      From our dataset, low-dose images with Dose Reduction
                      Factors (DRF) of 4, 10, 20, 50, and 100 were created by
                      modifying the acquired listmode data, corresponding to
                      injected activities of 45, 18, 9, 3.5 and 1.8 MBq.We first
                      modified and trained a U-Net model using a dataset of 500
                      total-body [F-18]FDG PET/MRI images with the same DRFs as
                      well as the corresponding full-dose images [1]. We then used
                      a Transfer Learning approach to train the model with our
                      data. 8 subjects were hold out for testing our model. For
                      evaluation, we used peak signal-to-noise ratio (PSNR),
                      Structural Similarity (SSIM), Contrast to Noise Ratio (CNR)
                      and Normalized Mean Squared Error (NMSE).Results:Our model
                      can synthesize enhanced low dose images that have
                      considerably reduced noise compared to the low-dose PET
                      image and resemble the standard-dose image. We achieved
                      improved metrics compared to the original U-Net model with
                      average PSNR of 28.02 (U-Net: 21.23), SSIM of 0.81 (U-Net:
                      0.53), CNR of 0.72 (U-Net: 0.61) and NMSE of 0.33 (U-Net:
                      0.67).Conclusions:Our results highlight the potential of our
                      model for enhancing low-dose brain PET/MRI images, allowing
                      for scans with reduced injected dose, shorter scan times or
                      more follow-up studies. References:[1] S Xue et al., Eur J
                      Nucl Med Mol I 49 (6), 1843 (2022).},
      month         = {Apr},
      date          = {2024-04-10},
      organization  = {62. Jahrestagung der Deutschen
                       Gesellschaft für Nuklearmedizin (DGN
                       e. V.), Leipzig (Germany), 10 Apr 2024
                       - 13 Apr 2024},
      subtyp        = {After Call},
      cin          = {INM-2},
      cid          = {I:(DE-Juel1)INM-2-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1038864},
}