A learning-based method for online adjustment of c-arm cone-beam ct source trajectories for artifact avoidance

HIGHLIGHTS

  • who: Mareike Thies from the Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA have published the Article: A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance, in the Journal: (JOURNAL)
  • what: The authors propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task i.e. verification of screw placement. The authors demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific . . .

     

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