VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, an...

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Veröffentlicht in:	arXiv.org 2022-06
Hauptverfasser:	Desai, Arjun D, Gunel, Beliz, Ozturkler, Batu M, Beg, Harris, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher, Pauly, John M, Chaudhari, Akshay S
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Consistency Data augmentation Image quality Image reconstruction Inverse problems Magnetic resonance imaging Physics Robustness Signal to noise ratio Training
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