Abstract #2562
An automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR)
Chung-Ki Wong 1 , Vadim Zotev 1 , Han Yuan 1 , Masaya Misaki 1 , Raquel Phillips 1 , Qingfei Luo 1 , and Jerzy Bodurka 1,2
1
Laureate Institute for Brain Research,
Tulsa, Oklahoma, United States,
2
College
of Engineering, University of Oklahoma, Norman,
Oklahoma, United States
Head motion during fMRI impairs data quality.
EEG-assisted retrospective motion correction (E-REMCOR),
which utilizes EEG data to correct for head movements in
fMRI on a slice-by-slice basis, was shown to be capable
of substantially removing movement in fMRI datasets. To
enhance E-REMCOR usability, especially for the rapidly
growing interest in concurrent EEG and fMRI
measurements, we developed automatic E-REMCOR
(aE-REMCOR) for head motion correction. aE-REMCOR was
applied to 305 fMRI scans at 3 Tesla. The average change
of TSNR over the brain goes up to 24%. The largest 10
percent of TSNR improvement reaches over 43%.
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