Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.

Journal: Magma (New York, N.Y.)
Published Date: May 17, 2024

Abstract

OBJECTIVE: This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).

Authors

  • Atita Suwannasak
    Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Road, Muang, Chiang Mai, 50200, Thailand.
  • Salita Angkurawaranon
    Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand. Electronic address: salita.ang@cmu.ac.th.
  • Prapatsorn Sangpin
    Philips (Thailand) Ltd, New Petchburi Road, Bangkapi, Huaykwang, Bangkok, Thailand.
  • Itthi Chatnuntawech
    National Nanotechnology Center, Pathum Thani, Thailand.
  • Kittichai Wantanajittikul
    Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
  • Uten Yarach
    Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Road, Muang, Chiang Mai, 50200, Thailand. uten.yarach@cmu.ac.th.

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