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Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI

  • Paediatric Neuroradiology
  • Published:
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Abstract

Introduction

Deep learning–based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI.

Methods

A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray–white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction.

Results

The overall image quality, noisiness, and gray–white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions.

Conclusion

Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.

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Acknowledgements

The authors appreciate Jaeseung Kim Jae for technical support.

Author information

Authors and Affiliations

  1. Department of Radiology, Seoul National University Children’s Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Soo-Hyun Kim, Young Hun Choi, Seul Bi Lee, Yeon Jin Cho, Seung Hyun Lee & Jung-Eun Cheon

  2. Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea

    Young Hun Choi, Seul Bi Lee, Yeon Jin Cho, Seung Hyun Lee & Jung-Eun Cheon

  3. GE Healthcare Korea, 416 Hangang-daero, Jung-gu, Seoul, 04637, Republic of Korea

    Joon Sung Lee

  4. Department of Radiology, SMG-SNU Boramae Medical Center, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea

    Su-Mi Shin

  5. Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea

    Jung-Eun Cheon

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  1. Soo-Hyun Kim
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Correspondence to Young Hun Choi.

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Kim, SH., Choi, Y.H., Lee, J.S. et al. Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology 65, 207–214 (2023). https://doi.org/10.1007/s00234-022-03053-1

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  • DOI: https://doi.org/10.1007/s00234-022-03053-1

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