Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging.

Journal: European journal of radiology
Published Date:

Abstract

PURPOSE: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.

Authors

  • Sung Hwan Bae
    Department of Radiology, Soonchunhyang University College of Medicine, Seoul Hospital, Seoul, Republic of Korea.
  • Jiyoung Hwang
    Department of Radiology, Soonchunhyang University College of Medicine, Seoul, Republic of Korea;
  • Seong Sook Hong
    Department of Radiology, Soonchunhyang University College of Medicine, Seoul, Republic of Korea;
  • Eun Ji Lee
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Jewon Jeong
    From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.).
  • Thomas Benkert
    Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Strasse 6, 97080 Würzburg, Germany (J.F.H., S.V., C.M., L.M.P., T.A.B., H.K., A.M.W.); and Department of Application Development, Siemens Healthcare, Erlangen, Germany (T.B., J.P.).
  • JaeKon Sung
    Siemens Healthineers Ltd., Seoul, Republic of Korea.
  • Simon Arberet
    Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA.