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Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction

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dc.contributor.authorOtgonbaatar, Chuluunbaatar-
dc.contributor.authorJeon, Sang-Hyun-
dc.contributor.authorCha, Sung-Jin-
dc.contributor.authorShim, Hackjoon-
dc.contributor.authorKim, Jin Woo-
dc.contributor.authorAhn, Jhii-Hyun-
dc.date.accessioned2025-10-24T03:54:10Z-
dc.date.available2025-10-24T03:54:10Z-
dc.date.created2025-10-14-
dc.date.issued2025-07-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/207847-
dc.description.abstractTo assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age +/- standard deviation, 63 +/- 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 +/- 1.75) and SilverBeam with DLR (10.60 +/- 1.08) showed no significant difference in image noise (p=0.99). In the obese group (BMI>25 kg/m(2)), there were no significant differences in SNRs of the liver, pancreas, and spleen between standard dose with IR and SilverBeam with DLR. SilverBeam with DLR+CE-boost demonstrated significantly better SNRs and CNRs, compared with standard dose with IR and SilverBeam with DLR. DLR combined with silver filter is effective for routine abdominopelvic CT, achieving a clearly reduced radiation dose while providing image quality that is non-inferior to standard dose with IR.-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.subject.MESHAbdomen* / diagnostic imaging-
dc.subject.MESHAdult-
dc.subject.MESHAged-
dc.subject.MESHDeep Learning*-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHImage Processing, Computer-Assisted* / methods-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHPelvis* / diagnostic imaging-
dc.subject.MESHRadiation Dosage*-
dc.subject.MESHRadiographic Image Interpretation, Computer-Assisted / methods-
dc.subject.MESHRadiography, Abdominal* / methods-
dc.subject.MESHSilver-
dc.subject.MESHTomography, X-Ray Computed* / methods-
dc.titleImage quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction-
dc.typeArticle-
dc.contributor.googleauthorOtgonbaatar, Chuluunbaatar-
dc.contributor.googleauthorJeon, Sang-Hyun-
dc.contributor.googleauthorCha, Sung-Jin-
dc.contributor.googleauthorShim, Hackjoon-
dc.contributor.googleauthorKim, Jin Woo-
dc.contributor.googleauthorAhn, Jhii-Hyun-
dc.identifier.doi10.1038/s41598-025-11184-7-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid40670595-
dc.subject.keywordReduced-dose CT-
dc.subject.keywordAbdominopelvic CT-
dc.subject.keywordImage quality-
dc.subject.keywordDeep learning image reconstruction-
dc.subject.keywordSilverBeam filter-
dc.contributor.affiliatedAuthorShim, Hackjoon-
dc.identifier.scopusid2-s2.0-105010723950-
dc.identifier.wosid001571978900018-
dc.citation.volume15-
dc.citation.number1-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.15(1), 2025-07-
dc.identifier.rimsid89785-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorReduced-dose CT-
dc.subject.keywordAuthorAbdominopelvic CT-
dc.subject.keywordAuthorImage quality-
dc.subject.keywordAuthorDeep learning image reconstruction-
dc.subject.keywordAuthorSilverBeam filter-
dc.subject.keywordPlusSTATISTICAL ITERATIVE RECONSTRUCTION-
dc.subject.keywordPlusBACK-PROJECTION-
dc.subject.keywordPlusRISK-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryMultidisciplinary Sciences-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.identifier.articleno25757-
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers

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