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

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 t...

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Published in:Neuroradiology 2023, Vol.65 (1), p.207-214
Main Authors: Kim, Soo-Hyun, Choi, Young Hun, Lee, Joon Sung, Lee, Seul Bi, Cho, Yeon Jin, Lee, Seung Hyun, Shin, Su-Mi, Cheon, Jung-Eun
Format: Article
Language:English
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Summary: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  
ISSN:0028-3940
1432-1920
DOI:10.1007/s00234-022-03053-1