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Effect of Scan Time on Neuro F-18-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning
DC Field | Value | Language |
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dc.contributor.author | Kim, Jaewon | - |
dc.contributor.author | Kang, Sungsik | - |
dc.contributor.author | Lee, Konsu | - |
dc.contributor.author | Jung, Jin Ho | - |
dc.contributor.author | Kim, Garam | - |
dc.contributor.author | Lim, Hyun Keong | - |
dc.contributor.author | Choi, Yong | - |
dc.contributor.author | Lee, Sangwon | - |
dc.contributor.author | Yun, Mijin | - |
dc.date.accessioned | 2021-03-31T01:31:34Z | - |
dc.date.available | 2021-03-31T01:31:34Z | - |
dc.date.created | 2021-02-22 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 2156-7018 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/181843 | - |
dc.description.abstract | The purpose of this study was to generate the PET images with high signal-to-noise ratio (SNR) acquired for typical scan durations (H-PET) from short scan time PET images with low SNR (L-PET) using deep learning and to evaluate the effect of scan time on the quality of predicted PET image. A convolutional neural network (CNN) with a concatenated connection and residual learning framework was implemented. PET data from 27 patients were acquired for 900 s, starting 60 minutes after the intravenous administration of FDG using a commercial PET/CT scanner. To investigate the effect of scan time on the quality of the predicted H-PETs, 10 s, 30 s, 60 s, and 120 s PET data were generated by sorting the 900 s LMF data into the LMF data acquired for each scan time. Twenty-three of the 27 patient images were used for training of the proposed CNN and the remaining four patient images were used for test of the CNN. The predicted H-PETs generated by the CNN were compared to ground-truth H-PETs, L-PETS, and filtered L-PETS processed with four commonly used denoising algorithms. The peak signal-to-noise ratios (PSNRs), normalized root mean square errors (NRMSEs), and average region-of-interest (ROI) differences as a function of scan time were calculated. The quality of the predicted H-PETs generated by the CNN was superior to that of the L-PETs and filtered L-PETs. Lower NRMSEs and higher PSNRs were also obtained from predicted H-PETs compared to the L-PETS and filtered L-PETS. ROI differences in the predicted H-PETs were smaller than those of the L-PETS. The quality of the predicted H-PETs gradually improved with increasing scan times. The lowest NRMSEs, highest PSNRs, and smallest ROI differences were obtained using the predicted H-PETs for 120 s. Various performance test results for the proposed CNN indicate that it is possible to generate H-PETs from neuro FDG L-PETS using the proposed CNN method, which might allow reductions in both scan time and injection dose. | - |
dc.language | 영어 | - |
dc.publisher | AMER SCIENTIFIC PUBLISHERS | - |
dc.relation.isPartOf | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS | - |
dc.title | Effect of Scan Time on Neuro F-18-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning | - |
dc.type | Article | - |
dc.contributor.googleauthor | Kim, Jaewon | - |
dc.contributor.googleauthor | Kang, Sungsik | - |
dc.contributor.googleauthor | Lee, Konsu | - |
dc.contributor.googleauthor | Jung, Jin Ho | - |
dc.contributor.googleauthor | Kim, Garam | - |
dc.contributor.googleauthor | Lim, Hyun Keong | - |
dc.contributor.googleauthor | Choi, Yong | - |
dc.contributor.googleauthor | Lee, Sangwon | - |
dc.contributor.googleauthor | Yun, Mijin | - |
dc.identifier.doi | 10.1166/jmihi.2021.3316 | - |
dc.relation.journalcode | J03359 | - |
dc.identifier.eissn | 2156-7026 | - |
dc.identifier.url | https://www.ingentaconnect.com/content/asp/jmihi/2021/00000011/00000003/art00004;jsessionid=236jqp8c5p2jl.x-ic-live-01 | - |
dc.subject.keyword | Deep Learning | - |
dc.subject.keyword | PET | - |
dc.subject.keyword | Scan Time Reduction | - |
dc.subject.keyword | CNN | - |
dc.subject.keyword | Denoising | - |
dc.subject.keyword | Human Brain | - |
dc.contributor.affiliatedAuthor | Yun, Mijin | - |
dc.identifier.wosid | 000603078500004 | - |
dc.citation.title | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 681 | - |
dc.citation.endPage | 687 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vol.11(3) : 681-687, 2021-03 | - |
dc.identifier.rimsid | 67565 | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | PET | - |
dc.subject.keywordAuthor | Scan Time Reduction | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | Denoising | - |
dc.subject.keywordAuthor | Human Brain | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | PET IMAGES | - |
dc.subject.keywordPlus | FDG-PET | - |
dc.subject.keywordPlus | PARCELLATION | - |
dc.subject.keywordPlus | TRANSFORM | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
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