Deep Learning-Based Synthetic TOF-MRA Generation Using Time-Resolved MRA in Fast Stroke Imaging.

Journal: AJNR. American journal of neuroradiology
Published Date:

Abstract

BACKGROUND AND PURPOSE: Time-resolved MRA enables collateral evaluation in acute ischemic stroke with large-vessel occlusion; however, a low SNR and spatial resolution impede the diagnosis of vascular occlusion. We developed a CycleGAN-based deep learning model to generate high-resolution synthetic TOF-MRA images using time-resolved MRA and evaluated its image quality and clinical efficacy.

Authors

  • Sung-Hye You
    From the Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (K.S.C., B.J.); Department of Radiology, Korea University College of Medicine, Anam Hospital, Seoul, Republic of Korea (S.H.Y.); Bio Imaging and Signal Processing Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (Y.H., J.C.Y.); Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul 110-744, Republic of Korea (S.H.C.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.H.C.); Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea (S.H.C.); KAIST Institute for Health Science and Technology, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (B.J.); and KAIST Institute for Artificial Intelligence, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (B.J.).
  • Yongwon Cho
    Department of Convergence Medicine, Asan Medical Center, College of Medicine, University of Ulsan, 88, Olympic-ro 43-gil, Seoul, 05505, South Korea.
  • Byungjun Kim
    Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
  • Kyung-Sook Yang
    Department of Biostatistics (K.-S.Y.), Korea University College of Medicine, Seoul, Korea.
  • InSeong Kim
    Siemens Healthineers (I.K.), Seoul, Korea.
  • Bo Kyu Kim
    From the Department of Radiology, (S.-H.Y., B.K., B.K.K., A.P., S.E.P.), Anam Hospital, Korea University College of Medicine, Seoul, Korea.
  • Arim Pak
    From the Department of Radiology, (S.-H.Y., B.K., B.K.K., A.P., S.E.P.), Anam Hospital, Korea University College of Medicine, Seoul, Korea.
  • Sang Eun Park
    From the Department of Radiology, (S.-H.Y., B.K., B.K.K., A.P., S.E.P.), Anam Hospital, Korea University College of Medicine, Seoul, Korea.