Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1783506771438993408 |
---|---|
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). |
format | Online Article Text |
id | pubmed-7074150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ryukyeonghwa validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT baekhyejin validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT ghosungmin validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT ryukanghyun validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT kimdonghyun validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT parksungeun validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT hajiyoung validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT chosoobuem validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment AT leejoonsung validationofdeeplearningbasedartifactcorrectiononsyntheticflairimagesinadifferentscanningenvironment |