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@ARTICLE{Bauer:274477,
      author       = {C. Bauer$^*$ and H. Teske$^*$ and A. Walter$^*$ and P.
                      Hoegen$^*$ and S. Adeberg and J. Debus$^*$ and O. Jaekel$^*$
                      and K. Giske$^*$},
      title        = {{B}iofidelic image registration for head and neck region
                      utilizing an in-silico articulated skeleton as a
                      transformation model.},
      journal      = {Physics in medicine and biology},
      volume       = {68},
      number       = {9},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2023-00622},
      pages        = {095006},
      year         = {2023},
      note         = {#EA:E040#LA:E040# / 2023 Apr 19;68(9)},
      abstract     = {We propose an integration scheme for a biomechanical motion
                      model into a deformable image registration. We demonstrate
                      its accuracy and reproducibility for adaptive radiation
                      therapy in the head and neck $region.\
Approach:$ The
                      novel registration scheme for the bony structures in the
                      head and neck regions is based on a previously developed
                      articulated kinematic skeleton model. The realized iterative
                      single-bone optimization process directly triggers posture
                      changes of the articulated skeleton, exchanging the
                      transformation model within the deformable image
                      registration $process.\
Accuracy$ in terms of target
                      registration errors in the bones is evaluated for 18 vector
                      fields of three patients between each planning CT and six
                      fraction CT scans distributed along the treatment
                      $course.\
Main$ results: The median of target
                      registration error distribution of the landmark pairs is 1.4
                      ± 0.3 mm. This is sufficient accuracy for adaptive
                      radiation therapy. The registration performs equally well
                      for all three patients and no degradation of the
                      registration accuracy can be observed throughout the
                      $treatment.\
Significance:$ Deformable image
                      registration, despite its known residual uncertainties, is
                      until now the tool of choice towards online re-planning
                      automation. By introducing a biofidelic motion model into
                      the optimization, we provide a viable way towards an
                      in-build quality $assurance.\&#xD.$},
      keywords     = {articulated skeleton (Other) / biomechanical model (Other)
                      / bone and joint kinematics (Other) / deformable image
                      registration (Other) / head and neck cancer (Other) /
                      interfractional motion (Other)},
      cin          = {E040 / E050 / HD01},
      ddc          = {530},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36972617},
      doi          = {10.1088/1361-6560/acc7f1},
      url          = {https://inrepo02.dkfz.de/record/274477},
}