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@ARTICLE{Qubala:284633,
      author       = {A. Qubala and J. Shafee and V. Batista and J. Liermann and
                      M. Winter and D. Piro and O. Jäkel$^*$},
      title        = {{C}omparative evaluation of a surface-based respiratory
                      monitoring system against a pressure sensor for 4{DCT} image
                      reconstruction in phantoms.},
      journal      = {Journal of applied clinical medical physics},
      volume       = {25},
      number       = {2},
      issn         = {1526-9914},
      address      = {Reston, Va.},
      publisher    = {ACMP},
      reportid     = {DKFZ-2023-02040},
      pages        = {e14174},
      year         = {2024},
      note         = {#LA:E040# / 2024 Feb;25(2):e14174},
      abstract     = {Four-dimensional computed tomography (4DCT), which relies
                      on breathing-induced motion, requires realistic surrogate
                      information of breathing variations to reconstruct the tumor
                      trajectory and motion variability of normal tissues
                      accurately. Therefore, the SimRT surface-guided respiratory
                      monitoring system has been installed on a Siemens CT
                      scanner. This work evaluated the temporal and spatial
                      accuracy of SimRT versus our commonly used pressure sensor,
                      AZ-733 V. A dynamic thorax phantom was used to reproduce
                      regular and irregular breathing patterns acquired by SimRT
                      and Anzai. Various parameters of the recorded breathing
                      patterns, including mean absolute deviations (MAD), Pearson
                      correlations (PC), and tagging precision, were investigated
                      and compared to ground-truth. Furthermore, 4DCT
                      reconstructions were analyzed to assess the volume
                      discrepancy, shape deformation and tumor trajectory.
                      Compared to the ground-truth, SimRT more precisely
                      reproduced the breathing patterns with a MAD range of 0.37
                      ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54
                      and 5.85 ± 3.61 mm for regular and irregular breathing
                      patterns, respectively. Additionally, SimRT provided a more
                      robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all
                      investigated breathing patterns. Further, the peak and
                      valley recognition were found to be more accurate and stable
                      using SimRT. The comparison of tumor trajectories revealed
                      discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT,
                      respectively. Moreover, volume discrepancies up to 1.71 ±
                      $1.62\%$ and 1.24 ± $2.02\%$ were found for both Anzai and
                      SimRT, respectively. SimRT was validated across various
                      breathing patterns and showed a more precise and stable
                      breathing tracking, (i) independent of the amplitude and
                      period, (ii) and without placing any physical devices on the
                      patient's body. These findings resulted in a more accurate
                      temporal and spatial accuracy, thus leading to a more
                      realistic 4DCT reconstruction and breathing-adapted
                      treatment planning.},
      keywords     = {4DCT reconstruction (Other) / Surface-guided radiotherapy
                      (Other) / breathing detection (Other) / breathing surrogate
                      (Other) / commissioning (Other) / mobile tumors (Other) /
                      motion artifacts (Other) / respiratory monitoring system
                      (Other)},
      cin          = {E040},
      ddc          = {530},
      cid          = {I:(DE-He78)E040-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37815197},
      doi          = {10.1002/acm2.14174},
      url          = {https://inrepo02.dkfz.de/record/284633},
}