Fast detection and reduction of local transient artifacts in resting-state fMRI
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Hang Joon | - |
dc.contributor.author | Reynolds, Richard C. | - |
dc.contributor.author | Gotts, Stephen J. | - |
dc.contributor.author | Handwerker, Daniel A. | - |
dc.contributor.author | Balzekas, Irena | - |
dc.contributor.author | Martin, Alex | - |
dc.contributor.author | Cox, Robert W. | - |
dc.contributor.author | Bandettini, Peter A. | - |
dc.date.accessioned | 2022-07-08T03:31:02Z | - |
dc.date.available | 2022-07-08T03:31:02Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145736 | - |
dc.description.abstract | Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Fast detection and reduction of local transient artifacts in resting-state fMRI | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jo, Hang Joon | - |
dc.identifier.doi | 10.1016/j.compbiomed.2020.103742 | - |
dc.identifier.scopusid | 2-s2.0-85083039076 | - |
dc.identifier.wosid | 000532824300029 | - |
dc.identifier.bibliographicCitation | COMPUTERS IN BIOLOGY AND MEDICINE, v.120, pp.1 - 12 | - |
dc.relation.isPartOf | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.citation.title | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.citation.volume | 120 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | MOTION | - |
dc.subject.keywordPlus | REGISTRATION | - |
dc.subject.keywordAuthor | Functional MRI | - |
dc.subject.keywordAuthor | Real-time fMRI | - |
dc.subject.keywordAuthor | Resting-state connectivity | - |
dc.subject.keywordAuthor | Sliding-windowed timeseries | - |
dc.subject.keywordAuthor | Online denoising | - |
dc.subject.keywordAuthor | Artifact detection | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0010482520301220?via%3Dihub | - |
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