量研学術機関リポジトリ「QST-Repository」は、国立研究開発法人 量子科学技術研究開発機構に所属する職員等が生み出した学術成果（学会誌発表論文、学会発表、研究開発報告書、特許等）を集積しインターネット上で広く公開するサービスです。 Welcome to QST-Repository where we accumulates and discloses the academic research results(Journal Publications, Conference presentation, Research and Development Report, Patent, etc.) of the members of National Institutes for Quantum Science and Technology.
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Deep learning-based noise reduction technique for DWI contains a risk of outputting values that are greatly deviating from what it should be because of the instability problem of deep learning. The neural network model was designed in this study to suppress this risk which can fix the generated value for each pixel within the range of values of neighboring pixels in the original image. The results of the volunteer study suggested that the proposed method has potential to provide effective denoising beside suppressing the instability risk.
Deep learning-based DWI Denoising method that suppressed the "instability" problem
