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PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning

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dc.contributor.author김동우-
dc.contributor.author예병석-
dc.contributor.author유선국-
dc.contributor.author윤미진-
dc.date.accessioned2021-12-28T17:07:01Z-
dc.date.available2021-12-28T17:07:01Z-
dc.date.issued2021-03-
dc.identifier.issn0363-9762-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/186943-
dc.description.abstractPurpose: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans. Methods: In this retrospective study, we enrolled 22 cognitively normal subjects, 20 patients with mild cognitive impairment, and 42 patients with Alzheimer disease. Twenty minutes of list-mode PET/CT data were acquired and reconstructed as the ground-truth images. The short-time scans were made in either 1, 2, 3, 4, or 5 minutes. The CNN with a residual learning framework was implemented to predict the ground-truth images of 18F-FBB PET/CT using short-time scans with either a single-slice or a 3-slice input layer. Model performance was evaluated by quantitative and qualitative analyses. Additionally, we quantified the amyloid load in the ground-truth and predicted images using the SUV ratio. Results: On quantitative analyses, with increasing scan time, the normalized root-mean-squared error and the SUV ratio differences between predicted and ground-truth images gradually decreased, and the peak signal-to-noise ratio increased. On qualitative analysis, the predicted images from the 3-slice CNN model showed better image quality than those from the single-slice model. The 3-slice CNN model with a short-time scan of at least 2 minutes achieved comparable, quantitative prediction of full-time 18F-FBB PET/CT images, with adequate to excellent image quality. Conclusions: The 3-slice CNN model with a residual learning framework is promising for the prediction of full-time 18F-FBB PET/CT images from short-time scans.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherLippincott-
dc.relation.isPartOfCLINICAL NUCLEAR MEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAmyloid / metabolism-
dc.subject.MESHAniline Compounds-
dc.subject.MESHBrain / diagnostic imaging-
dc.subject.MESHBrain / metabolism-
dc.subject.MESHDeep Learning*-
dc.subject.MESHFeasibility Studies-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHImage Processing, Computer-Assisted / methods*-
dc.subject.MESHPositron Emission Tomography Computed Tomography*-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHSignal-To-Noise Ratio-
dc.subject.MESHStilbenes-
dc.titlePET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Nuclear Medicine (핵의학교실)-
dc.contributor.googleauthorSangwon Lee-
dc.contributor.googleauthorJin Ho Jung-
dc.contributor.googleauthorDongwoo Kim-
dc.contributor.googleauthorHyun Keong Lim-
dc.contributor.googleauthorMi-Ae Park-
dc.contributor.googleauthorGaram Kim-
dc.contributor.googleauthorMinjae So-
dc.contributor.googleauthorSun Kook Yoo-
dc.contributor.googleauthorByoung Seok Ye-
dc.contributor.googleauthorYong Choi-
dc.contributor.googleauthorMijin Yun-
dc.identifier.doi10.1097/RLU.0000000000003471-
dc.contributor.localIdA05304-
dc.contributor.localIdA04603-
dc.contributor.localIdA02471-
dc.contributor.localIdA02550-
dc.relation.journalcodeJ00595-
dc.identifier.eissn1536-0229-
dc.identifier.pmid33512838-
dc.identifier.urlhttps://journals.lww.com/nuclearmed/Fulltext/2021/03000/PET_CT_for_Brain_Amyloid__A_Feasibility_Study_for.27.aspx-
dc.contributor.alternativeNameKim, Dongwoo-
dc.contributor.affiliatedAuthor김동우-
dc.contributor.affiliatedAuthor예병석-
dc.contributor.affiliatedAuthor유선국-
dc.contributor.affiliatedAuthor윤미진-
dc.citation.volume46-
dc.citation.number3-
dc.citation.startPagee133-
dc.citation.endPagee140-
dc.identifier.bibliographicCitationCLINICAL NUCLEAR MEDICINE, Vol.46(3) : e133-e140, 2021-03-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers

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