Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

Journal: IEEE transactions on bio-medical engineering

Published Date: Sep 19, 2024

Abstract

OBJECTIVE: We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B ∼ 50 mT) MRI.

Authors

David Schote
Lukas Winter
Christoph Kolbitsch
Georg Rose

Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg Universitaetsplatz 2, Magdeburg 39106, Germany.
Oliver Speck

Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; German Center for Neurodegenerative Disease, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany.
Andreas Kofler

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Knee Magnetic Resonance Imaging Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (38696296)

Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

Abstract

Authors

Keywords

External Resources

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