Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences.

Journal: Korean journal of radiology
PMID: 39780631

Abstract

OBJECTIVE: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.

Authors

  • Kyu Sung Choi
    Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Chanrim Park
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Ji Ye Lee
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Kyung Hoon Lee
    Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
  • Young Hun Jeon
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Inpyeong Hwang
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Roh Eul Yoo
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Tae Jin Yun
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Mi Ji Lee
    Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
  • Keun-Hwa Jung
    Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Koung Mi Kang
    Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.

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