Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks
Abstract Motion artefacts caused by the patient’s body movements affect magnetic resonance imaging (MRI) accuracy. This study aimed to compare and evaluate the accuracy of motion artefacts correction using a conditional generative adversarial network (CGAN) with an autoencoder and U-net models. The...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35794-1 |