Cited 5 times in
Application of sigmoidal optimization to reconstruct nuclear medicine image: Comparison with filtered back projection and iterative reconstruction method
DC Field | Value | Language |
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dc.contributor.author | 김동욱 | - |
dc.date.accessioned | 2022-11-24T00:54:48Z | - |
dc.date.available | 2022-11-24T00:54:48Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.issn | 1738-5733 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/191074 | - |
dc.description.abstract | High levels for noise and a loss of true signal make the quantitative interpretation of nuclear medicine (NM) images difficult. An application of profile optimization using a sigmoidal function in this study was used to acquire the NM images with high quality. And the images were acquired by using three kinds of reconstruction method using each same sinogram: a standard filtered back-projection (FBP), an iterative reconstruction (IR) technique, and the sigmoidal function profile optimization (SFPO). Comparison of image according to reconstruction method was performed to show a superiority of the SFPO for imaging. The images reconstructed by using the SFPO showed an average of 1.49 times and of 1.17 times better in contrast than the results obtained using the standard FBP and the IR technique, respectively. Higher signal to noise ratios were obtained as an average of 12.30 times and of 3.77 times than results obtained using the standard FBP and the IR technique, respectively. This study confirms that reconstruction with SFPO (vs FBP and vs IR) can lead to better lesion detectability and characterization with noise reduction. It can be developed for future reconstruction technique for the NM imaging. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Korea Nuclear Society | - |
dc.relation.isPartOf | NUCLEAR ENGINEERING AND TECHNOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Application of sigmoidal optimization to reconstruct nuclear medicine image: Comparison with filtered back projection and iterative reconstruction method | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Han-Back Shin | - |
dc.contributor.googleauthor | Moo-Sub Kim | - |
dc.contributor.googleauthor | Martin Law | - |
dc.contributor.googleauthor | Shih-Kien Djeng | - |
dc.contributor.googleauthor | Min-Geon Choi | - |
dc.contributor.googleauthor | ByungWook Choi | - |
dc.contributor.googleauthor | Sungmin Kang | - |
dc.contributor.googleauthor | Dong-Wook Kim | - |
dc.contributor.googleauthor | Tae Suk Suh | - |
dc.contributor.googleauthor | Do-Kun Yoon | - |
dc.identifier.doi | 10.1016/j.net.2020.06.029 | - |
dc.contributor.localId | A05710 | - |
dc.relation.journalcode | J03972 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1738573320302710 | - |
dc.subject.keyword | Sigmoid function | - |
dc.subject.keyword | Profile optimization | - |
dc.subject.keyword | PET | - |
dc.subject.keyword | SPECT | - |
dc.subject.keyword | Monte Carlo simulation | - |
dc.contributor.affiliatedAuthor | 김동욱 | - |
dc.citation.volume | 53 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 258 | - |
dc.citation.endPage | 265 | - |
dc.identifier.bibliographicCitation | NUCLEAR ENGINEERING AND TECHNOLOGY, Vol.53(1) : 258-265, 2021-01 | - |
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