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Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study

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dc.contributor.author김성준-
dc.contributor.author송호택-
dc.contributor.author이영한-
dc.contributor.author이주희-
dc.contributor.author정민-
dc.contributor.author박지우-
dc.contributor.author이님-
dc.date.accessioned2023-11-28T03:16:16Z-
dc.date.available2023-11-28T03:16:16Z-
dc.date.issued2023-12-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196778-
dc.description.abstractIn this prospective, multi-reader, multi-vendor study, we evaluated the performance of a commercially available deep neural network (DNN)-based MR image reconstruction in enabling accelerated 2D fast spin-echo (FSE) knee imaging. Forty-five subjects were prospectively enrolled and randomly divided into three 3T MRIs. Conventional 2D FSE and accelerated 2D FSE sequences were acquired for each subject, and the accelerated FSE images were reconstructed and enhanced with DNN-based reconstruction software (FSE-DNN). Quantitative assessments and diagnostic performances were independently evaluated by three musculoskeletal radiologists. For statistical analyses, paired t-tests, and Pearson's correlation were used for image quality comparison and inter-reader agreements. Accelerated FSE-DNN reduced scan times by 41.0% on average. FSE-DNN showed better SNR and CNR (p < 0.001). Overall image quality of FSE-DNN was comparable (p > 0.05), and diagnostic performances of FSE-DNN showed comparable lesion detection. Two of cartilage lesions were under-graded or over-graded (n = 2) while there was no significant difference in other image sets (n = 43). Overall inter-reader agreement between FSE-conventional and FSE-DNN showed good agreement (R2 = 0.76; p < 0.001). In conclusion, DNN-based reconstruction can be applied to accelerated knee imaging in multi-vendor MRI scanners, with reduced scan time and comparable image quality. This study suggests the potential for DNN-accelerated knee MRI in clinical practice.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHHumans-
dc.subject.MESHKnee Joint* / diagnostic imaging-
dc.subject.MESHMagnetic Resonance Imaging* / methods-
dc.subject.MESHProspective Studies-
dc.subject.MESHSensitivity and Specificity-
dc.titleHighly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJoohee Lee-
dc.contributor.googleauthorMin Jung-
dc.contributor.googleauthorJiwoo Park-
dc.contributor.googleauthorSungjun Kim-
dc.contributor.googleauthorYunjin Im-
dc.contributor.googleauthorNim Lee-
dc.contributor.googleauthorHo-Taek Song-
dc.contributor.googleauthorYoung Han Lee-
dc.identifier.doi10.1038/s41598-023-44248-7-
dc.contributor.localIdA00585-
dc.contributor.localIdA02080-
dc.contributor.localIdA02967-
dc.contributor.localIdA04786-
dc.contributor.localIdA03605-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid37828048-
dc.contributor.alternativeNameKim, Sungjun-
dc.contributor.affiliatedAuthor김성준-
dc.contributor.affiliatedAuthor송호택-
dc.contributor.affiliatedAuthor이영한-
dc.contributor.affiliatedAuthor이주희-
dc.contributor.affiliatedAuthor정민-
dc.citation.volume13-
dc.citation.number1-
dc.citation.startPage17264-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.13(1) : 17264, 2023-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Orthopedic Surgery (정형외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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