Cited 11 times in
Deep-learned short tau inversion recovery imaging using multi-contrast MR images
DC Field | Value | Language |
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dc.contributor.author | 이영한 | - |
dc.date.accessioned | 2021-01-19T08:02:44Z | - |
dc.date.available | 2021-01-19T08:02:44Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 0740-3194 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/181436 | - |
dc.description.abstract | Purpose: To generate short tau, or short inversion time (TI), inversion recovery (STIR) images from three multi-contrast MR images, without additional scanning, using a deep neural network. Methods: For simulation studies, we used multi-contrast simulation images. For in-vivo studies, we acquired knee MR images including 288 slices of T1 -weighted (T1 -w), T2 -weighted (T2 -w), gradient-recalled echo (GRE), and STIR images taken from 12 healthy volunteers. Our MR image synthesis method generates a new contrast MR image from multi-contrast MR images. We used a deep neural network to identify the complex relationships between MR images that show various contrasts for the same tissues. Our contrast-conversion deep neural network (CC-DNN) is an end-to-end architecture that trains the model to create one image from three (T1 -w, T2 -w, and GRE images). We propose a new loss function to take into account intensity differences, misregistration, and local intensity variations. The CC-DNN-generated STIR images were evaluated with four quantitative evaluation metrics, including mean squared error, peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and multi-scale SSIM (MS-SSIM). Furthermore, a subjective evaluation was performed by musculoskeletal radiologists. Results: Our method showed improved results in all quantitative evaluations compared with other methods and received the highest scores in subjective evaluations by musculoskeletal radiologists. Conclusion: This study suggests the feasibility of our method for generating STIR sequence images without additional scanning that offered a potential alternative to the STIR pulse sequence when additional scanning is limited or STIR artifacts are severe. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Wiley | - |
dc.relation.isPartOf | MAGNETIC RESONANCE IN MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Deep-learned short tau inversion recovery imaging using multi-contrast MR images | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Sewon Kim | - |
dc.contributor.googleauthor | Hanbyol Jang | - |
dc.contributor.googleauthor | Jinseong Jang | - |
dc.contributor.googleauthor | Young Han Lee | - |
dc.contributor.googleauthor | Dosik Hwang | - |
dc.identifier.doi | 10.1002/mrm.28327 | - |
dc.contributor.localId | A02967 | - |
dc.relation.journalcode | J02179 | - |
dc.identifier.eissn | 1522-2594 | - |
dc.identifier.pmid | 32479671 | - |
dc.identifier.url | https://onlinelibrary.wiley.com/doi/10.1002/mrm.28327 | - |
dc.subject.keyword | deep learning | - |
dc.subject.keyword | image synthesis | - |
dc.subject.keyword | knee | - |
dc.subject.keyword | magnetic resonance imaging | - |
dc.subject.keyword | neural network | - |
dc.subject.keyword | short tau inversion recovery | - |
dc.subject.keyword | short-TI inversion recovery | - |
dc.contributor.alternativeName | Lee, Young Han | - |
dc.contributor.affiliatedAuthor | 이영한 | - |
dc.citation.volume | 84 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 2994 | - |
dc.citation.endPage | 3008 | - |
dc.identifier.bibliographicCitation | MAGNETIC RESONANCE IN MEDICINE, Vol.84(6) : 2994-3008, 2020-12 | - |
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