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@ARTICLE{Maier:301773,
      author       = {J. Maier$^*$ and S. Sawall$^*$ and M. Arheit and P. Paysan
                      and M. Kachelriess$^*$},
      title        = {{D}eep learning-based cone-beam {CT} motion compensation
                      with single-view temporal resolution.},
      journal      = {Medical physics},
      volume       = {52},
      number       = {7},
      issn         = {0094-2405},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-01153},
      pages        = {e17911},
      year         = {2025},
      note         = {#EA:E025#LA:E025# / 2025 Jul;52(7):e17911},
      abstract     = {Cone-beam CT (CBCT) scans that are affected by motion often
                      require motion compensation to reduce artifacts or to
                      reconstruct 4D (3D+time) representations of the patient. To
                      do so, most existing strategies rely on some sort of gating
                      strategy that sorts the acquired projections into motion
                      bins. Subsequently, these bins can be reconstructed
                      individually before further post-processing may be applied
                      to improve image quality. While this concept is useful for
                      periodic motion patterns, it fails in case of non-periodic
                      motion as observed, for example, in irregularly breathing
                      patients.To address this issue and to increase temporal
                      resolution, we propose the deep single angle-based motion
                      compensation (SAMoCo).To avoid gating, and therefore its
                      downsides, the deep SAMoCo trains a U-net-like network to
                      predict displacement vector fields (DVFs) representing the
                      motion that occurred between any two given time points of
                      the scan. To do so, 4D clinical CT scans are used to
                      simulate 4D CBCT scans as well as the corresponding ground
                      truth DVFs that map between the different motion states of
                      the scan. The network is then trained to predict these DVFs
                      as a function of the respective projection views and an
                      initial 3D reconstruction. Once the network is trained, an
                      arbitrary motion state corresponding to a certain projection
                      view of the scan can be recovered by estimating DVFs from
                      any other state or view and by considering them during
                      reconstruction.Applied to 4D CBCT simulations of breathing
                      patients, the deep SAMoCo provides high-quality
                      reconstructions for periodic and non-periodic motion. Here,
                      the deviations with respect to the ground truth are less
                      than 27 HU on average, while respiratory motion, or the
                      diaphragm position, can be resolved with an accuracy of
                      about 0.75 mm. Similar results were obtained for real
                      measurements where a high correlation with external motion
                      monitoring signals could be observed, even in patients with
                      highly irregular respiration.The ability to estimate DVFs as
                      a function of two arbitrary projection views and an initial
                      3D reconstruction makes deep SAMoCo applicable to arbitrary
                      motion patterns with single-view temporal resolution.
                      Therefore, the deep SAMoCo is particularly useful for cases
                      with unsteady breathing, compensation of residual motion
                      during a breath-hold scan, or scans with fast gantry
                      rotation times in which the data acquisition only covers a
                      very limited number of breathing cycles. Furthermore, not
                      requiring gating signals may simplify the clinical workflow
                      and reduces the time needed for patient preparation.},
      keywords     = {4D CBCT (Other) / deep learning (Other) / motion
                      compensation (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:40467957},
      doi          = {10.1002/mp.17911},
      url          = {https://inrepo02.dkfz.de/record/301773},
}