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

• Published in: NMR in biomedicine Vol. 35; no. 12; pp. e4798 - n/a
• Main Authors: Yaman, Burhaneddin, Gu, Hongyi, Hosseini, Seyed Amir Hossein, Demirel, Omer Burak, Moeller, Steen, Ellermann, Jutta, Uğurbil, Kâmil, Akçakaya, Mehmet
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.12.2022; John Wiley and Sons Inc
• Subjects: Brain; convolutional neural networks; Data acquisition; data augmentation; Deep learning; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; Knee; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neural Networks, Computer; Neuroimaging; parallel imaging; Physics; Resonance; Sampled data; Self-supervised learning; Supervised Machine Learning; Training; deep learning; parallel imaging; self-supervised learning; convolutional neural networks; image reconstruction; data augmentation

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...