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Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data

PURPOSE: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully-sampled datasets. THEORY AND METHODS: Self-supervised learning via data under-sampling (SSDU) for physics-guided deep learning (DL) reconstruction partitions available measureme...

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書誌詳細
出版年:Magn Reson Med
主要な著者: Yaman, Burhaneddin, Hosseini, Seyed Amir Hossein, Moeller, Steen, Ellermann, Jutta, Uğurbil, Kâmil, Akçakaya, Mehmet
フォーマット: Artigo
言語:Inglês
出版事項: 2020
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7811359/
https://ncbi.nlm.nih.gov/pubmed/32614100
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/mrm.28378
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