A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks.

Journal: Medical physics
Published Date: Nov 19, 2019

Abstract

PURPOSE: To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes.

Authors

  • Shaikat M Galib
    Missouri University of Science and Technology, Rolla, MO, USA.
  • Hyoung K Lee
    Department of Nuclear Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA.
  • Christopher L Guy
    Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA.
  • Matthew J Riblett
    Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA.
  • Geoffrey D Hugo
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, 63110, MO, USA.

Keywords

External Resources

Popular Topics
Recent Journals