Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Yoonmi | - |
dc.contributor.author | Kim, Jaeil | - |
dc.contributor.author | Chen, Geng | - |
dc.contributor.author | Lin, Weili | - |
dc.contributor.author | Yap, Pew-Thian | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-08-31T22:52:55Z | - |
dc.date.available | 2021-08-31T22:52:55Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/61473 | - |
dc.description.abstract | Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | NEONATAL BRAIN | - |
dc.title | Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/TMI.2019.2911203 | - |
dc.identifier.scopusid | 2-s2.0-85068232902 | - |
dc.identifier.wosid | 000510688600001 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MEDICAL IMAGING, v.38, no.12, pp.2717 - 2725 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.citation.title | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.citation.volume | 38 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 2717 | - |
dc.citation.endPage | 2725 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | NEONATAL BRAIN | - |
dc.subject.keywordAuthor | Graph CNN | - |
dc.subject.keywordAuthor | diffusion MRI | - |
dc.subject.keywordAuthor | adversarial learning | - |
dc.subject.keywordAuthor | longitudinal prediction | - |
dc.subject.keywordAuthor | early brain development | - |
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