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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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