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@ARTICLE{Faust:293339,
      author       = {J. F. Faust and A. J. Krafft and D. Polak and P. Speier and
                      N. G. R. Behl and N. Ooms and J. Roll and J. Krieger and M.
                      E. Ladd$^*$ and F. Maier},
      title        = {{R}apid {CNN}-based needle localization for automatic slice
                      alignment in {MR}-guided interventions using 3{D}
                      undersampled radial white-marker imaging.},
      journal      = {Medical physics},
      volume       = {51},
      number       = {11},
      issn         = {0094-2405},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2024-01887},
      pages        = {8018-8033},
      year         = {2024},
      note         = {2024 Nov;51(11):8018-8033},
      abstract     = {In MR-guided in-bore percutaneous needle interventions,
                      typically 2D interactive real-time imaging is used for
                      navigating the needle into the target. Misaligned 2D imaging
                      planes can result in losing visibility of the needle in the
                      2D images, which impedes successful targeting. Necessary
                      iterative manual slice adjustment can prolong interventional
                      workflows. Therefore, rapid automatic alignment of the
                      imaging planes with the needle would be preferable to
                      improve such workflows.To investigate rapid 3D localization
                      of needles in MR-guided interventions via a convolutional
                      neural network (CNN)-based localization algorithm using an
                      undersampled white-marker contrast acquisition for the
                      purpose of automatic imaging slice alignment.A radial 3D
                      rf-spoiled gradient echo MR pulse sequence with white-marker
                      encoding was implemented and a CNN-based localization
                      algorithm was employed to extract position and orientation
                      of an aspiration needle from the undersampled white-marker
                      images. The CNN was trained using porcine tissue phantoms
                      (257 needle trajectories, four-fold data augmentation,
                      $90\%/10\%$ split into training and validation dataset).
                      Achievable localization times and accuracy were evaluated
                      retrospectively in an ex vivo study (109 needle
                      trajectories) for a range of needle orientations between
                      78° and 90° relative to the B0 field. A proof-of-concept
                      in vivo experiment was performed in two porcine animal
                      models and feasibility of automatic imaging slice alignment
                      was evaluated retrospectively.Ex vivo needle localization
                      was achieved with a median localization accuracy of 1.9 mm
                      (distance needle tip to detected needle axis) and a median
                      angular deviation of 2.6° for needle orientations between
                      86° and 90° to the B0 field from fully sampled WM images
                      (resolution of (4 mm)3, 6434 acquired radial k-space spokes,
                      acquisition time of 80.4 s) in a field-of-view of (256 mm)3.
                      Localization accuracy decreased with increasing
                      undersampling and needle trajectory increasingly aligned
                      with B0. For needle orientations between 86° and 90° to
                      the B0 field, a highly accelerated acquisition of only 32
                      k-space spokes (acquisition time of 0.4 s) yielded a median
                      localization accuracy of 3.1 mm and a median angular
                      deviation of 4.7°. For needle orientations between 78° and
                      82° to the B0 field, a median accuracy and angular
                      deviation of 3.5 mm and 6.8° could still be achieved with
                      64 sampled spokes (acquisition time of 0.8 s). In vivo, a
                      localization accuracy of 1.4 mm and angular deviation of
                      3.4° was achieved sampling 32 k-space spokes (acquisition
                      time of 0.48 s) with the needle oriented at 87.7° to the B0
                      field. For a needle oriented at 77.6° to the B0 field,
                      localization accuracy of 5.3 mm and angular deviation of
                      6.8° were still achieved sampling 128 k-space spokes
                      (acquisition time of 1.92 s), allowing for retrospective
                      slice alignment.The investigated approach enables passive
                      biopsy needle localization in 3D. Acceleration of the
                      localization to real-time applicability is feasible for
                      needle orientations approximately perpendicular to B0. The
                      method can potentially facilitate MR-guided needle
                      interventions by enabling automatic imaging slice alignment
                      with the needle.},
      keywords     = {device localization (Other) / interventional MRI (Other) /
                      percutaneous needle intervention (Other)},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39292615},
      doi          = {10.1002/mp.17376},
      url          = {https://inrepo02.dkfz.de/record/293339},
}