Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction

Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled da...

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Veröffentlicht in:	IEEE transactions on medical imaging 2024-06, Vol.43 (6), p.2358-2369
Hauptverfasser:	Leynes, Andrew P., Deveshwar, Nikhil, Nagarajan, Srikantan S., Larson, Peder E. Z.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Bayes Theorem Bayesian analysis Bayesian deep learning Brain - diagnostic imaging calibrationless MRI Coils Compressed sensing Data sampling deep image prior Deep Learning Humans Image contrast Image Processing, Computer-Assisted - methods Image reconstruction Imaging Magnetic resonance imaging Magnetic Resonance Imaging - methods Neural networks Sampled data Sampling self-supervised deep learning Sensitivity Statistical inference Supervised Machine Learning undersampled MRI
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