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Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the...
Gespeichert in:
| Veröffentlicht in: | J Clin Med |
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| Hauptverfasser: | , , , , , , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI
2020
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7074150/ https://ncbi.nlm.nih.gov/pubmed/32013069 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/jcm9020364 |
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