Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Reconstruction of 3D knee 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 artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee. Methods Twenty healthy volunteers were examined us...

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Bibliographic Details
Main Authors: Thomas Dratsch, Charlotte Zäske, Florian Siedek, Philip Rauen, Nils Große Hokamp, Kristina Sonnabend, David Maintz, Grischa Bratke, Andra Iuga
Format: Article
Language:English
Published: SpringerOpen 2024-04-01
Series:European Radiology Experimental
Subjects:
Artifacts
Artificial intelligence
Deep learning
Knee joint
Magnetic resonance imaging
Online Access:https://doi.org/10.1186/s41747-024-00446-0

Internet

https://doi.org/10.1186/s41747-024-00446-0

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