Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Journal: Academic radiology
Published Date: Jun 9, 2022

Abstract

RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs.

Authors

  • Ayis Pyrros
    DuPage Medical Group, Radiology. Electronic address: ayis@ayis.org.
  • Andrew Chen
    University of Illinois at Urbana-Champaign, Department of Computer Science.
  • Jorge Mario Rodríguez-Fernández
    University of Illinois at Chicago, Department of Neurology.
  • Stephen M Borstelmann
    University of Central Florida School of Medicine, UCF College of Medicine, 6850 Lake Nona Blvd, Orlando, FL 32827. Electronic address: sborstelmannmd@gmail.com.
  • Patrick A Cole
    Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois.
  • Jeanne Horowitz
    Northwestern Memorial Hospital, Northwestern University, Radiology.
  • Jonathan Chung
    Department of Medical Imaging, Western University, ON, Canada; Digital Image Group, London, ON, Canada.
  • Paul Nikolaidis
    Northwestern Memorial Hospital, Northwestern University, Radiology.
  • Viveka Boddipalli
    DuPage Medical Group, Radiology.
  • Nasir Siddiqui
    DuPage Medical Group, Radiology.
  • Melinda Willis
    DuPage Medical Group, Radiology.
  • Adam Eugene Flanders
    Thomas Jefferson University Hospital, Radiology.
  • Sanmi Koyejo
    Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois.

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