A dual echo approach to removing motion artefacts in fMRI time series
In fMRI, subject motion can severely affect data quality. This is a particular problem when movement is correlated with the experimental paradigm as this potentially causes artefactual activation. A method is presented that uses linear regression, to utilise the time course of an image acquired at v...
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Published in | NMR in biomedicine Vol. 22; no. 5; pp. 551 - 560 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
01.06.2009
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Subjects | |
Online Access | Get full text |
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Summary: | In fMRI, subject motion can severely affect data quality. This is a particular problem when movement is correlated with the experimental paradigm as this potentially causes artefactual activation. A method is presented that uses linear regression, to utilise the time course of an image acquired at very short echo time (TE) as a voxel‐wise regressor for a second image in the same echo train, that is acquired with high BOLD sensitivity. The value of this approach is demonstrated using task‐locked motion combined with visual stimulation. Results obtained at both 1.5 and 3 T show improvements in functional activation maps for individual subjects. The method is straightforward to implement, does not require extra scan time and can easily be embedded in a multi‐echo acquisition framework. Copyright © 2009 John Wiley & Sons, Ltd.
Subject motion can severely affect fMRI data quality. A method is presented that utilises the time course of an image acquired at short echo time as regressor for a second image in the same echo train that has high BOLD sensitivity. Results at 1.5 and 3 T show improvements in activation for individual subjects in a paradigm combining visual stimulation with task‐locked motion. The method is straightforward to implement and does not require extra scan time. |
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Bibliography: | Dutch Technology Foundation (STW) - No. NGT.6154 ark:/67375/WNG-8W7ZFK8G-4 ArticleID:NBM1371 istex:EBEB6558E26B05B3A312A59941B3026B68AB6488 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 0952-3480 1099-1492 |
DOI: | 10.1002/nbm.1371 |