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...

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Detalles Bibliográficos
Autores principales: Ryu, Kyeong Hwa, Baek, Hye Jin, Gho, Sung-Min, Ryu, Kanghyun, Kim, Dong-Hyun, Park, Sung Eun, Ha, Ji Young, Cho, Soo Buem, Lee, Joon Sung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074150/
https://www.ncbi.nlm.nih.gov/pubmed/32013069
http://dx.doi.org/10.3390/jcm9020364

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074150/
https://www.ncbi.nlm.nih.gov/pubmed/32013069
http://dx.doi.org/10.3390/jcm9020364

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