Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).

Authors

  • Rine Nakanishi
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Sethuraman Sankaran
  • Leo Grady
  • Jenifer Malpeso
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Razik Yousfi
    HeartFlow Inc., Redwood City, CA, USA.
  • Kazuhiro Osawa
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Indre Ceponiene
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Negin Nazarat
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Sina Rahmani
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Kendall Kissel
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Eranthi Jayawardena
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Christopher Dailing
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA.
  • Christopher Zarins
    HeartFlow Inc., Redwood City, CA, USA.
  • Bon-Kwon Koo
    Department of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • James K Min
    3 Department of Radiology, Weill Cornell Medicine , New York, New York.
  • Charles A Taylor
  • Matthew J Budoff
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA. mbudoff@labiomed.org.