Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks

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 training...

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Detalles Bibliográficos
Autores principales: Usui, Keisuke, Muro, Isao, Shibukawa, Syuhei, Goto, Masami, Ogawa, Koichi, Sakano, Yasuaki, Kyogoku, Shinsuke, Daida, Hiroyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220077/
https://www.ncbi.nlm.nih.gov/pubmed/37237139
http://dx.doi.org/10.1038/s41598-023-35794-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220077/
https://www.ncbi.nlm.nih.gov/pubmed/37237139
http://dx.doi.org/10.1038/s41598-023-35794-1

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