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@ARTICLE{Lopes:276157,
      author       = {Lopes, Leonor and Jiao, Fangyang and Xue, Song and Pyka,
                      Thomas and Krieger, Korbinian and Ge, Jingjie and Xu, Qian
                      and Fahmi, Rachid and Spottiswoode, Bruce and Soliman, Ahmed
                      and Buchert, Ralph and Brendel, Matthias and Hong, Jimin and
                      Guan, Yihui and Bassetti, Claudio L A and Rominger, Axel and
                      Zuo, Chuantao and Shi, Kuangyu and Wu, Ping},
      title        = {{D}opaminergic {PET} to {SPECT} domain adaptation: a cycle
                      {GAN} translation approach.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {52},
      number       = {3},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DZNE-2025-00229},
      pages        = {851 - 863},
      year         = {2025},
      abstract     = {Dopamine transporter imaging is routinely used in
                      Parkinson's disease (PD) and atypical parkinsonian syndromes
                      (APS) diagnosis. While [11C]CFT PET is prevalent in Asia
                      with a large APS database, Europe relies on [123I]FP-CIT
                      SPECT with limited APS data. Our aim was to develop a deep
                      learning-based method to convert [11C]CFT PET images to
                      [123I]FP-CIT SPECT images, facilitating multicenter studies
                      and overcoming data scarcity to promote Artificial
                      Intelligence (AI) advancements.A CycleGAN was trained on
                      [11C]CFT PET (n = 602, $72\%PD)$ and [123I]FP-CIT SPECT (n =
                      1152, $85\%PD)$ images from PD and non-parkinsonian control
                      (NC) subjects. The model generated synthetic SPECT images
                      from a real PET test set (n = 67, $75\%PD).$ Synthetic
                      images were quantitatively and visually evaluated.Fréchet
                      Inception Distance indicated higher similarity between
                      synthetic and real SPECT than between synthetic SPECT and
                      real PET. A deep learning classification model trained on
                      synthetic SPECT achieved sensitivity of $97.2\%$ and
                      specificity of $90.0\%$ on real SPECT images. Striatal
                      specific binding ratios of synthetic SPECT were not
                      significantly different from real SPECT. The striatal
                      left-right differences and putamen binding ratio were
                      significantly different only in the PD cohort. Real PET and
                      real SPECT had higher contrast-to-noise ratio compared to
                      synthetic SPECT. Visual grading analysis scores showed no
                      significant differences between real and synthetic SPECT,
                      although reduced diagnostic performance on synthetic images
                      was observed.CycleGAN generated synthetic SPECT images
                      visually indistinguishable from real ones and retained
                      disease-specific information, demonstrating the feasibility
                      of translating [11C]CFT PET to [123I]FP-CIT SPECT. This
                      cross-modality synthesis could enhance further AI
                      classification accuracy, supporting the diagnosis of PD and
                      APS.},
      keywords     = {Humans / Tomography, Emission-Computed, Single-Photon:
                      methods / Positron-Emission Tomography: methods / Parkinson
                      Disease: diagnostic imaging / Parkinson Disease: metabolism
                      / Image Processing, Computer-Assisted: methods / Male /
                      Female / Dopamine: metabolism / Deep Learning / Middle Aged
                      / Dopamine Plasma Membrane Transport Proteins: metabolism /
                      Aged / Tropanes / CFT PET (Other) / Cycle GAN (Other) /
                      Domain adaptation (Other) / FP-CIT SPECT (Other) /
                      Parkinson’s disease (Other) / Dopamine (NLM Chemicals) /
                      Dopamine Plasma Membrane Transport Proteins (NLM Chemicals)
                      / 2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropane
                      (NLM Chemicals) / Tropanes (NLM Chemicals)},
      cin          = {AG Haass},
      ddc          = {610},
      cid          = {I:(DE-2719)1110007},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39557690},
      doi          = {10.1007/s00259-024-06961-x},
      url          = {https://pub.dzne.de/record/276157},
}