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

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...

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Bibliographic Details
Main Authors: Keisuke Usui, Isao Muro, Syuhei Shibukawa, Masami Goto, Koichi Ogawa, Yasuaki Sakano, Shinsuke Kyogoku, Hiroyuki Daida
Format: Article
Language:English
Published: Nature Portfolio 2023-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-35794-1

Internet

https://doi.org/10.1038/s41598-023-35794-1

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