Cited 0 times in
PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김동우 | - |
dc.contributor.author | 예병석 | - |
dc.contributor.author | 유선국 | - |
dc.contributor.author | 윤미진 | - |
dc.date.accessioned | 2021-12-28T17:07:01Z | - |
dc.date.available | 2021-12-28T17:07:01Z | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 0363-9762 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/186943 | - |
dc.description.abstract | Purpose: 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.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Lippincott | - |
dc.relation.isPartOf | CLINICAL NUCLEAR MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Amyloid / metabolism | - |
dc.subject.MESH | Aniline Compounds | - |
dc.subject.MESH | Brain / diagnostic imaging | - |
dc.subject.MESH | Brain / metabolism | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Feasibility Studies | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Image Processing, Computer-Assisted / methods* | - |
dc.subject.MESH | Positron Emission Tomography Computed Tomography* | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Signal-To-Noise Ratio | - |
dc.subject.MESH | Stilbenes | - |
dc.title | PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Nuclear Medicine (핵의학교실) | - |
dc.contributor.googleauthor | Sangwon Lee | - |
dc.contributor.googleauthor | Jin Ho Jung | - |
dc.contributor.googleauthor | Dongwoo Kim | - |
dc.contributor.googleauthor | Hyun Keong Lim | - |
dc.contributor.googleauthor | Mi-Ae Park | - |
dc.contributor.googleauthor | Garam Kim | - |
dc.contributor.googleauthor | Minjae So | - |
dc.contributor.googleauthor | Sun Kook Yoo | - |
dc.contributor.googleauthor | Byoung Seok Ye | - |
dc.contributor.googleauthor | Yong Choi | - |
dc.contributor.googleauthor | Mijin Yun | - |
dc.identifier.doi | 10.1097/RLU.0000000000003471 | - |
dc.contributor.localId | A05304 | - |
dc.contributor.localId | A04603 | - |
dc.contributor.localId | A02471 | - |
dc.contributor.localId | A02550 | - |
dc.relation.journalcode | J00595 | - |
dc.identifier.eissn | 1536-0229 | - |
dc.identifier.pmid | 33512838 | - |
dc.identifier.url | https://journals.lww.com/nuclearmed/Fulltext/2021/03000/PET_CT_for_Brain_Amyloid__A_Feasibility_Study_for.27.aspx | - |
dc.contributor.alternativeName | Kim, Dongwoo | - |
dc.contributor.affiliatedAuthor | 김동우 | - |
dc.contributor.affiliatedAuthor | 예병석 | - |
dc.contributor.affiliatedAuthor | 유선국 | - |
dc.contributor.affiliatedAuthor | 윤미진 | - |
dc.citation.volume | 46 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | e133 | - |
dc.citation.endPage | e140 | - |
dc.identifier.bibliographicCitation | CLINICAL NUCLEAR MEDICINE, Vol.46(3) : e133-e140, 2021-03 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.