252 203

Cited 2 times in

Metal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN)

DC Field Value Language
dc.contributor.author김동욱-
dc.contributor.author김진성-
dc.contributor.author김호진-
dc.contributor.author이호-
dc.contributor.author한민철-
dc.contributor.author홍채선-
dc.date.accessioned2023-03-10T01:20:32Z-
dc.date.available2023-03-10T01:20:32Z-
dc.date.issued2022-12-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/193121-
dc.description.abstractThis work attempted to construct a new metal artifact reduction (MAR) framework in kilo-voltage (kV) computed tomography (CT) images by combining (1) deep learning and (2) multi-modal imaging, defined as MARTIAN (Metal Artifact Reduction throughout Two-step sequentIAl deep convolutional neural Networks). Most CNNs under supervised learning require artifact-free images to artifact-contaminated images for artifact correction. Mega-voltage (MV) CT is insensitive to metal artifacts, unlike kV CT due to different physical characteristics, which can facilitate the generation of artifact-free synthetic kV CT images throughout the first network (Network 1). The pairs of true kV CT and artifact-free kV CT images after post-processing constructed a subsequent network (Network 2) to conduct the actual MAR process. The proposed framework was implemented by GAN from 90 scans for head-and-neck and brain radiotherapy and validated with 10 independent cases against commercial MAR software. The artifact-free kV CT images following Network 1 and post-processing led to structural similarity (SSIM) of 0.997, and mean-absolute-error (MAE) of 10.2 HU, relative to true kV CT. Network 2 in charge of actual MAR successfully suppressed metal artifacts, relative to commercial MAR, while retaining the detailed imaging information, yielding the SSIM of 0.995 against 0.997 from the commercial MAR.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHExtraterrestrial Environment*-
dc.subject.MESHMars*-
dc.subject.MESHMultimodal Imaging-
dc.subject.MESHNeural Networks, Computer-
dc.subject.MESHTomography, X-Ray Computed-
dc.titleMetal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN)-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiation Oncology (방사선종양학교실)-
dc.contributor.googleauthorHojin Kim-
dc.contributor.googleauthorSang Kyun Yoo-
dc.contributor.googleauthorDong Wook Kim-
dc.contributor.googleauthorHo Lee-
dc.contributor.googleauthorChae-Seon Hong-
dc.contributor.googleauthorMin Cheol Han-
dc.contributor.googleauthorJin Sung Kim-
dc.identifier.doi10.1038/s41598-022-25366-0-
dc.contributor.localIdA05710-
dc.contributor.localIdA04548-
dc.contributor.localIdA05970-
dc.contributor.localIdA03323-
dc.contributor.localIdA05870-
dc.contributor.localIdA05846-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid36460784-
dc.contributor.alternativeNameKim, Dong Wook-
dc.contributor.affiliatedAuthor김동욱-
dc.contributor.affiliatedAuthor김진성-
dc.contributor.affiliatedAuthor김호진-
dc.contributor.affiliatedAuthor이호-
dc.contributor.affiliatedAuthor한민철-
dc.contributor.affiliatedAuthor홍채선-
dc.citation.volume12-
dc.citation.number1-
dc.citation.startPage20823-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.12(1) : 20823, 2022-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.