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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
Authors
Ryu, Kyeong HwaBaek, Hye JinGho, Sung-MinRyu, KanghyunKim, Dong-HyunPark, Sung EunHa, Ji YoungCho, Soo BuemLee, Joon Sung
Ewha Authors
조수범
Issue Date
2020
Journal Title
JOURNAL OF CLINICAL MEDICINE
ISSN
2077-0383JCR Link
Citation
JOURNAL OF CLINICAL MEDICINE vol. 9, no. 2
Keywords
neural networks (computer)deep learningimage enhancementmagnetic resonance imagingimage interpretationcomputer-assisted
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
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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DOI
10.3390/jcm9020364
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의료원 > 의료원 > Journal papers
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