Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Abstract Background To investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder. Methods Twenty healthy volunteers were examined using at 3-T scanner with a f...

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Bibliographic Details
Main Authors: Thomas Dratsch, Florian Siedek, Charlotte Zäske, Kristina Sonnabend, Philip Rauen, Robert Terzis, Robert Hahnfeldt, David Maintz, Thorsten Persigehl, Grischa Bratke, Andra Iuga
Format: Article
Language:English
Published: SpringerOpen 2023-10-01
Series:European Radiology Experimental
Subjects:
Artifacts
Artificial intelligence
Deep learning
Magnetic resonance imaging
Shoulder joint
Online Access:https://doi.org/10.1186/s41747-023-00377-2

Internet

https://doi.org/10.1186/s41747-023-00377-2

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