Cited 3 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 |
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dc.contributor.author | 김동욱 | - |
dc.contributor.author | 김진성 | - |
dc.contributor.author | 김호진 | - |
dc.contributor.author | 이호 | - |
dc.contributor.author | 한민철 | - |
dc.contributor.author | 홍채선 | - |
dc.date.accessioned | 2023-03-10T01:20:32Z | - |
dc.date.available | 2023-03-10T01:20:32Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193121 | - |
dc.description.abstract | This 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.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Extraterrestrial Environment* | - |
dc.subject.MESH | Mars* | - |
dc.subject.MESH | Multimodal Imaging | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | Tomography, X-Ray Computed | - |
dc.title | Metal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN) | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Hojin Kim | - |
dc.contributor.googleauthor | Sang Kyun Yoo | - |
dc.contributor.googleauthor | Dong Wook Kim | - |
dc.contributor.googleauthor | Ho Lee | - |
dc.contributor.googleauthor | Chae-Seon Hong | - |
dc.contributor.googleauthor | Min Cheol Han | - |
dc.contributor.googleauthor | Jin Sung Kim | - |
dc.identifier.doi | 10.1038/s41598-022-25366-0 | - |
dc.contributor.localId | A05710 | - |
dc.contributor.localId | A04548 | - |
dc.contributor.localId | A05970 | - |
dc.contributor.localId | A03323 | - |
dc.contributor.localId | A05870 | - |
dc.contributor.localId | A05846 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 36460784 | - |
dc.contributor.alternativeName | Kim, Dong Wook | - |
dc.contributor.affiliatedAuthor | 김동욱 | - |
dc.contributor.affiliatedAuthor | 김진성 | - |
dc.contributor.affiliatedAuthor | 김호진 | - |
dc.contributor.affiliatedAuthor | 이호 | - |
dc.contributor.affiliatedAuthor | 한민철 | - |
dc.contributor.affiliatedAuthor | 홍채선 | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 20823 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.12(1) : 20823, 2022-12 | - |
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