A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 25, 2020

Abstract

PURPOSE: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality.

Authors

  • Mareike Thies
    Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Jan-Nico Zäch
    Computer Vision Laboratory, Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland.
  • Cong Gao
    State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.
  • Russell Taylor
    Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
  • Nassir Navab
    Chair for Computer Aided Medical Procedures & Augmented Reality, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
  • Andreas Maier
    Pattern Recognition Lab, University Erlangen-Nürnberg, Erlangen, Germany.
  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.

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