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@ARTICLE{Kabelac:301775,
      author       = {A. F. Kabelac$^*$ and E. Eulig$^*$ and J. Maier$^*$ and M.
                      Hammermann$^*$ and M. Knaup$^*$ and M. Kachelriess$^*$},
      title        = {{L}atent space reconstruction for missing data problems in
                      {CT}.},
      journal      = {Medical physics},
      volume       = {52},
      number       = {7},
      issn         = {0094-2405},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-01155},
      pages        = {e17910},
      year         = {2025},
      note         = {#EA:E025#LA:E025# / 2025 Jul;52(7):e17910},
      abstract     = {The reconstruction of a computed tomography (CT) image can
                      be compromised by artifacts, which, in many cases, reduce
                      the diagnostic value of the image. These artifacts often
                      result from missing or corrupt regions in the projection
                      data, for example, by truncation, metal, or limited angle
                      acquisitions.In this work, we introduce a novel deep
                      learning-based framework, latent space reconstruction (LSR),
                      which enables correction of various types of artifacts
                      arising from missing or corrupted data.First, we train a
                      generative neural network on uncorrupted CT images. After
                      training, we iteratively search for the point in the latent
                      space of this network that best matches the compromised
                      projection data we measured. Once an optimal point is found,
                      forward-projection of the generated CT image can be used to
                      inpaint the corrupted or incomplete regions of the measured
                      raw data.We used LSR to correct for truncation and metal
                      artifacts. For the truncation artifact correction, images
                      corrected by LSR show effective artifact suppression within
                      the field of measurement (FOM), alongside a substantial
                      high-quality extension of the FOM compared to other methods.
                      For the metal artifact correction, images corrected by LSR
                      demonstrate effective artifact reduction, providing a
                      clearer view of the surrounding tissues and anatomical
                      details.The results indicate that LSR is effective in
                      correcting metal and truncation artifacts. Furthermore, the
                      versatility of LSR allows its application to various other
                      types of artifacts resulting from missing or corrupt data.},
      keywords     = {computed tomography (Other) / deep learning (Other) /
                      missing data (Other)},
      cin          = {E025},
      ddc          = {610},
      cid          = {I:(DE-He78)E025-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40468155},
      doi          = {10.1002/mp.17910},
      url          = {https://inrepo02.dkfz.de/record/301775},
}