ArticleMagnetic resonance in medicine2026
A network-assisted joint image and motion estimation approach for robust 3D MRI motion correction across severity levels.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- A network-assisted joint image and motion estimation approach for robust 3D MRI motion correction across severity levels.Magnetic resonance in medicine · 2026Article
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Authors and funding
5 authors.
Funding
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Abstract
purposeThe purpose of this work was to develop and evaluate a novel method that leverages neural networks and physical modeling for 3D motion correction at different levels of corruption.
methodsThe novel method ("UNet+JE") combines an existing neural network ("UNet
resultsUNet+JE provided better motion correction than UNet
conclusionsUNet+JE benefitted from the robustness of joint estimation and the fast image improvement provided by the neural network, enabling the method to provide high quality 3D image correction under a wide range of motion corruption within shorter runtimes.
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Registered trials
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