Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.

Journal: Journal of imaging informatics in medicine
Published Date: Jun 28, 2024

Abstract

The aim of this study was to investigate the effect of iterative motion correction (IMC) on reducing artifacts in brain magnetic resonance imaging (MRI) with deep learning reconstruction (DLR). The study included 10 volunteers (between September 2023 and December 2023) and 30 patients (between June 2022 and July 2022) for quantitative and qualitative analyses, respectively. Volunteers were instructed to remain still during the first MRI with fluid-attenuated inversion recovery sequence (FLAIR) and to move during the second scan. IMCoff DLR images were reconstructed from the raw data of the former acquisition; IMCon and IMCoff DLR images were reconstructed from the latter acquisition. After registration of the motion images, the structural similarity index measure (SSIM) was calculated using motionless images as reference. For qualitative analyses, IMCon and IMCoff FLAIR DLR images of the patients were reconstructed and evaluated by three blinded readers in terms of motion artifacts, noise, and overall quality. SSIM for IMCon images was 0.952, higher than that for IMCoff images (0.949) (p < 0.001). In qualitative analyses, although noise in IMCon images was rated as increased by two of the three readers (both p < 0.001), all readers agreed that motion artifacts and overall quality were significantly better in IMCon images than in IMCoff images (all p < 0.001). In conclusion, IMC reduced motion artifacts in brain FLAIR DLR images while maintaining similarity to motionless images.

Authors

  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Hiroyuki Akai
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Shimpei Kato
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Taku Tajima
    Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
  • Naoki Yoshioka
    Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
  • Toshihiro Furuta
    Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
  • Hajime Kageyama
    Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
  • Yui Toda
    Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
  • Masaaki Akahane
    Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
  • Kuni Ohtomo
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Shigeru Kiryu
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

Keywords

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