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Deep-learned short tau inversion recovery imaging using multi-contrast MR images

DC FieldValueLanguage
dc.contributor.author이영한-
dc.date.accessioned2021-01-19T08:02:44Z-
dc.date.available2021-01-19T08:02:44Z-
dc.date.issued2020-12-
dc.identifier.issn0740-3194-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181436-
dc.description.abstractPurpose: 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.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherWiley-
dc.relation.isPartOfMAGNETIC RESONANCE IN MEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep-learned short tau inversion recovery imaging using multi-contrast MR images-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSewon Kim-
dc.contributor.googleauthorHanbyol Jang-
dc.contributor.googleauthorJinseong Jang-
dc.contributor.googleauthorYoung Han Lee-
dc.contributor.googleauthorDosik Hwang-
dc.identifier.doi10.1002/mrm.28327-
dc.contributor.localIdA02967-
dc.relation.journalcodeJ02179-
dc.identifier.eissn1522-2594-
dc.identifier.pmid32479671-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/10.1002/mrm.28327-
dc.subject.keyworddeep learning-
dc.subject.keywordimage synthesis-
dc.subject.keywordknee-
dc.subject.keywordmagnetic resonance imaging-
dc.subject.keywordneural network-
dc.subject.keywordshort tau inversion recovery-
dc.subject.keywordshort-TI inversion recovery-
dc.contributor.alternativeNameLee, Young Han-
dc.contributor.affiliatedAuthor이영한-
dc.citation.volume84-
dc.citation.number6-
dc.citation.startPage2994-
dc.citation.endPage3008-
dc.identifier.bibliographicCitationMAGNETIC RESONANCE IN MEDICINE, Vol.84(6) : 2994-3008, 2020-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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