View : 652 Download: 0
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 Hwa; Baek, Hye Jin; Gho, Sung-Min; Ryu, Kanghyun; Kim, Dong-Hyun; Park, Sung Eun; Ha, Ji Young; Cho, Soo Buem; Lee, Joon Sung
- Ewha Authors
- 조수범
- Issue Date
- 2020
- Journal Title
- JOURNAL OF CLINICAL MEDICINE
- ISSN
- 2077-0383
- Citation
- JOURNAL OF CLINICAL MEDICINE vol. 9, no. 2
- Keywords
- neural networks (computer); deep learning; image enhancement; magnetic resonance imaging; image interpretation; computer-assisted
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- 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).
- DOI
- 10.3390/jcm9020364
- Appears in Collections:
- 의료원 > 의료원 > Journal papers
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML