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Deep Autofocus with Cone-Beam CT Consistency Constraint

  • Alexander PreuhsEmail author
  • Michael Manhart
  • Philipp Roser
  • Bernhard Stimpel
  • Christopher Syben
  • Marios Psychogios
  • Markus Kowarschik
  • Andreas Maier
Conference paper
  • 44 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

High quality reconstruction with interventional C-arm conebeam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced in the backprojection operation. With prolonged acquisition times of interventional C-arm CBCT the likelihood of rigid patient motion increases. To adapt the backprojection operation accordingly, a motion estimation strategy is necessary. Recently, a novel learning-based approach was proposed, capable of compensating motions within the acquisition plane. We extend this method by a CBCT consistency constraint, which was proven to be effcient for motions perpendicular to the acquisition plane. By the synergistic combination of these two measures, in and out-plane motion is well detectable, achieving an average artifact suppression of 93 %. This outperforms the entropy-based state-of-the-art autofocus measure which achieves on average an artifact suppression of 54%.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Alexander Preuhs
    • 1
    Email author
  • Michael Manhart
    • 2
  • Philipp Roser
    • 1
  • Bernhard Stimpel
    • 1
  • Christopher Syben
    • 1
  • Marios Psychogios
    • 3
  • Markus Kowarschik
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangen-NürnbergDeutschland
  2. 2.Siemens Healthcare GmbHForchheimDeutschland
  3. 3.Department of Neuroradiology,University Hospital BaselBaselSchweiz

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