Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.

Journal: Journal of magnetic resonance imaging : JMRI
PMID:

Abstract

BACKGROUND: Deep learning (DL) often requires an image quality metric; however, widely used metrics are not designed for medical images.

Authors

  • Chenwei Tang
    Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.
  • Laura B Eisenmenger
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • Leonardo Rivera-Rivera
    Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • Eugene Huo
    Department of Radiology, University of California, San Francisco, California, USA.
  • Jacqueline C Junn
    Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Anthony D Kuner
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • Thekla H Oechtering
    Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.
  • Anthony Peret
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • Jitka Starekova
    University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
  • Kevin M Johnson
    From the Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar St, Thompkins East 2, New Haven, CT 06520 (K.M.J., H.E.J., Y.Z., L.H.S.); College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China (Y.Z.); Upstate Carolina Radiology PA, Spartanburg, SC (D.A.D.); and Department of Biomedical Engineering, Yale University, New Haven, Conn (L.H.S.).