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@INPROCEEDINGS{Gicquel:283149,
      author       = {Gicquel, Malo and Flood, Gabrielle and Zhao, Ruoyi and
                      Wuestefeld, Anika and Spotorno, Nicola and Strandberg, Olof
                      and Xiao, Yu and Åström, Kalle and Wisse, Laura E. M. and
                      van Westen, Danielle and Berron, David and Hansson, Oskar
                      and Vogel, Jacob W.},
      title        = {{AI} {S}uperresolution: {C}onverting {T}1‐weighted {MRI}
                      from 3{T} to 7{T} resolution toward enhanced imaging
                      biomarkers for {A}lzheimer's disease},
      journal      = {Alzheimer's and dementia},
      volume       = {21},
      number       = {S2},
      issn         = {1552-5260},
      reportid     = {DZNE-2026-00045},
      pages        = {e106600},
      year         = {2025},
      abstract     = {Background:High-resolution (7T) MRI facilitates in vivo
                      imaging of fine anatomical structures selectively affected
                      in Alzheimer's disease (AD), including medial temporal lobe
                      subregions. However, 7T data is challenging to acquire and
                      largely unavailable in clinical settings. Here, we use deep
                      learning to synthesize 7T resolution T1-weighted MRI images
                      from lower-resolution (3T) images.Method:Paired 7T and 3T
                      T1-weighted images were acquired from 178 participants (134
                      clinically unimpaired, 48 impaired) from the Swedish
                      BioFINDER-2 study. To synthesize 7T-resolution images from
                      3T images, we trained two models: a specialized U-Net, and a
                      U-Net mixed with a generative adversarial network
                      (U-Net-GAN) on $80\%$ of the data. We evaluated model
                      performance on the remaining $20\%,$ compared to models from
                      the literature (V-Net, WATNet), using image-based
                      performance metrics and by surveying five blinded MRI
                      professionals based on subjective quality. For n = 11
                      participants, amygdalae were automatically segmented with
                      FastSurfer on 3T and synthetic-7T images, and compared to a
                      manually segmented “ground truth”. To assess downstream
                      performance, FastSurfer was run on n = 3,168 triplets of
                      matched 3T and AI-generated synthetic-7T images, and a
                      multi-class random forest model classifying clinical
                      diagnosis was trained on both datasets.Result:Synthetic-7T
                      images were generated for images in the test set (Figure
                      1A). Image metrics suggested the U-Net as the top performing
                      model (Figure 1B), though blinded experts qualitatively
                      rated the GAN-U-Net as the best looking images, exceeding
                      even real 7T images (Figure 1C). Automated segmentations of
                      amygdalae from the synthetic GAN-U-Net model were more
                      similar to manually segmented amygdalae, compared to the
                      original 3T they were synthesized from, in 9/11 images
                      (Figure 2). Classification obtained modest performance
                      $(accuracy∼60\%)$ but did not differ across real or
                      synthetic images (Figure 3A). Synthetic image models used
                      slightly different features for classification (Figure
                      3B).Conclusion:Synthetic T1-weighted images approaching 7T
                      resolution can be generated from 3T images, which may
                      improve image quality and segmentation, without compromising
                      performance in downstream tasks. This approach holds promise
                      for better measurement of deep cortical or subcortical
                      structures relevant to AD. Work is ongoing toward improving
                      performance, generalizability and clinical utility.},
      month         = {Jul},
      date          = {2025-07-27},
      organization  = {Alzheimer’s Association
                       International Conference, Toronto
                       (Canada), 27 Jul 2025 - 31 Jul 2025},
      cin          = {AG Berron},
      ddc          = {610},
      cid          = {I:(DE-2719)5000070},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)1 / PUB:(DE-HGF)16},
      doi          = {10.1002/alz70856_106600},
      url          = {https://pub.dzne.de/record/283149},
}