CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.

Journal: European radiology
Published Date:

Abstract

OBJECTIVE: To quantitatively compare DLIR and ASiR-V with realistic anatomical images.

Authors

  • Kai Yang
    Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jinjin Cao
    Department of Radiology, Massachusetts General Hospital, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
  • Nisanard Pisuchpen
    Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
  • Avinash Kambadakone
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: akambadakone@mgh.harvard.edu.
  • Rajiv Gupta
    Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Room: GRB-273A, 55 Fruit Street, Boston, MA 02114, USA. Electronic address: Rgupta1@mgh.harvard.edu.
  • Theodore Marschall
    Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
  • Xinhua Li
    Department of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University No. 600, Tianhe Road, Guangzhou 510630, China.
  • Bob Liu
    Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.