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Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation

BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training. MATERIALS AND METHODS: Fort...

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書誌詳細
出版年:AJNR Am J Neuroradiol
主要な著者: Hagiwara, A., Otsuka, Y., Hori, M., Tachibana, Y., Yokoyama, K., Fujita, S., Andica, C., Kamagata, K., Irie, R., Koshino, S., Maekawa, T., Chougar, L., Wada, A., Takemura, M.Y., Hattori, N., Aoki, S.
フォーマット: Artigo
言語:Inglês
出版事項: American Society of Neuroradiology 2019
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7028623/
https://ncbi.nlm.nih.gov/pubmed/30630834
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3174/ajnr.A5927
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