Abdominal computed tomography localizer image generation: A deep learning approach.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes. In this study, we investigate the possibility of using deep learning models to reconstruct one localization scan image from the other, thus reducing the patient dose and simplifying the clinical workflow.

Authors

  • Zongxi Liu
    Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, 3202 N. Maryland Ave, Milwaukee, WI, 53201, USA. Electronic address: zongxi@uwm.edu.
  • Huimin Zhao
    Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. zhao5@illinois.edu.
  • Xiang Fang
    Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
  • Donglai Huo
    Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. Donglai.Huo@ucdenver.edu.