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@INPROCEEDINGS{Masood:1038879,
      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 {PET}/{MRI} {U}sing {D}eep
                      {L}earning {M}ethod for {I}mproved {I}nterpretation},
      reportid     = {FZJ-2025-01694},
      year         = {2024},
      abstract     = {Motivation: We developed a deep learning model to enhance
                      the image quality of ultra-low dose brain PET.Goal(s):
                      Significantly reducing the injected dose not only minimizes
                      radiation risk in subjects but also provides options for
                      scanning protocols, and more follow-up studies.Approach: We
                      proposed a 3D-Residual Attention U-Net model initially
                      trained on whole-body [18F]FDG PET/MR images. We used
                      transfer learning approach to fine-tune our proposed model
                      on [18F]CPFPX PET/MRI inhouse dataset.Results: We achieved
                      improved metrics compared to 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).Impact:
                      Our model has potential to generate high-quality PET images
                      from low-dose PET/MR, potentially contribute to
                      implementation of kinetic modelling using PET/MR imaging.
                      Our model is capable of enhancing both whole-body and brain
                      datasets, making it valuable asset for diverse
                      applications.},
      month         = {May},
      date          = {2024-05-04},
      organization  = {2024 ISMRM $\&$ ISMRT Annual Meeting
                       $\&$ Exhibition, Singapore (Singapore),
                       4 May 2024 - 9 May 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)24},
      url          = {https://juser.fz-juelich.de/record/1038879},
}