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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...

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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:
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
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author 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
author_facet 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
author_sort Ryu, Kyeong Hwa
collection PubMed
description 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 retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI).
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spelling pubmed-70741502020-03-19 Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment 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 J Clin Med Article 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 retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI). MDPI 2020-01-29 /pmc/articles/PMC7074150/ /pubmed/32013069 http://dx.doi.org/10.3390/jcm9020364 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title_full Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title_fullStr Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title_full_unstemmed Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title_short Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment
title_sort validation of deep learning-based artifact correction on synthetic flair images in a different scanning environment
topic Article
url 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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