Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조항준 | - |
dc.date.accessioned | 2021-10-28T05:27:20Z | - |
dc.date.available | 2021-10-28T05:27:20Z | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | COMPUTERS IN BIOLOGY AND MEDICINE, v. 120, article no. 103742 | en_US |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0010482520301220?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/165852 | - |
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. | en_US |
dc.description.sponsorship | This work was supported by NIMH and NINDS Intramural Research Programs, a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0218), and the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (NRF2017M3C9A6047623). This work was also supported by Hanyang University (HY-201900000002814). | en_US |
dc.language.iso | en | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.subject | Functional MRI | en_US |
dc.subject | Real-time fMRI | en_US |
dc.subject | Resting-state connectivity | en_US |
dc.subject | Sliding-windowed timeseries | en_US |
dc.subject | Online denoising | en_US |
dc.subject | Artifact detection | en_US |
dc.title | Fast detection and reduction of local transient artifacts in resting-state fMRI | en_US |
dc.type | Article | en_US |
dc.relation.volume | 120 | - |
dc.identifier.doi | 10.1016/j.compbiomed.2020.103742 | - |
dc.relation.page | 103742-103742 | - |
dc.relation.journal | COMPUTERS IN BIOLOGY AND MEDICINE | - |
dc.contributor.googleauthor | Jo, Hang Joon | - |
dc.contributor.googleauthor | Reynolds, Richard C. | - |
dc.contributor.googleauthor | Gotts, Stephen J. | - |
dc.contributor.googleauthor | Handwerker, Daniel A. | - |
dc.contributor.googleauthor | Balzekas, Irena | - |
dc.contributor.googleauthor | Martin, Alex | - |
dc.contributor.googleauthor | Cox, Robert W. | - |
dc.contributor.googleauthor | Bandettini, Peter A. | - |
dc.relation.code | 2020050891 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF MEDICINE[S] | - |
dc.sector.department | DEPARTMENT OF MEDICINE | - |
dc.identifier.pid | hangjoonjo | - |
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