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Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

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dc.contributor.author심학준-
dc.date.accessioned2023-03-21T07:23:22Z-
dc.date.available2023-03-21T07:23:22Z-
dc.date.issued2022-11-
dc.identifier.issn1229-6929-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/193342-
dc.description.abstractObjective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. Materials and methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. Results: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. Conclusion: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherKorean Society of Radiology-
dc.relation.isPartOfKOREAN JOURNAL OF RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAlgorithms-
dc.subject.MESHComputed Tomography Angiography* / methods-
dc.subject.MESHCoronary Angiography / methods-
dc.subject.MESHCoronary Vessels / diagnostic imaging-
dc.subject.MESHDeep Learning*-
dc.subject.MESHHumans-
dc.subject.MESHMale-
dc.subject.MESHRadiation Dosage-
dc.subject.MESHRadiographic Image Interpretation, Computer-Assisted / methods-
dc.subject.MESHStents-
dc.titleImprovement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentYonsei Biomedical Research Center (연세의생명연구원)-
dc.contributor.googleauthorChuluunbaatar Otgonbaatar-
dc.contributor.googleauthorJae-Kyun Ryu-
dc.contributor.googleauthorJaemin Shin-
dc.contributor.googleauthorJi Young Woo-
dc.contributor.googleauthorJung Wook Seo-
dc.contributor.googleauthorHackjoon Shim-
dc.contributor.googleauthorDae Hyun Hwang-
dc.identifier.doi10.3348/kjr.2022.0127-
dc.contributor.localIdA02215-
dc.relation.journalcodeJ02884-
dc.identifier.eissn2005-8330-
dc.identifier.pmid36196766-
dc.subject.keywordCoronary stent-
dc.subject.keywordDeep learning reconstruction-
dc.subject.keywordFiltered-back projection-
dc.subject.keywordHybrid iterative reconstruction-
dc.subject.keywordValve apparatus-
dc.contributor.alternativeNameShim, Hack Joon-
dc.contributor.affiliatedAuthor심학준-
dc.citation.volume23-
dc.citation.number11-
dc.citation.startPage1044-
dc.citation.endPage1054-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF RADIOLOGY, Vol.23(11) : 1044-1054, 2022-11-
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers

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