Multi‐mask self‐supervised learning for physics‐guided neural networks in highly accelerated magnetic resonance imaging

Self‐supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self‐supervised learning methods for physics‐guided reconstruction networks split acquired undersampled data...

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Detalles Bibliográficos
Autores principales: Yaman, Burhaneddin, Gu, Hongyi, Hosseini, Seyed Amir Hossein, Demirel, Omer Burak, Moeller, Steen, Ellermann, Jutta, Uğurbil, Kâmil, Akçakaya, Mehmet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669191/
https://www.ncbi.nlm.nih.gov/pubmed/35789133
http://dx.doi.org/10.1002/nbm.4798

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669191/
https://www.ncbi.nlm.nih.gov/pubmed/35789133
http://dx.doi.org/10.1002/nbm.4798

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