Hello,
I was hoping to get some expert opinions on correcting EPI datasets using estimated bias field maps from FAST (or any other segmentation software). The article "Effects of image contrast on functional MRI image registration," by Gonzalez-Castillo et al., 2013 examines the impact of correcting EPI datasets on motion correction algorithms. Surely acquired field maps would be better suited for correcting the EPI datasets, but in the event those weren't acquired, I saw a couple of previous posts on the forum:
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=spm;d7dc8031.1605 and https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=fsl;4281da36.131
that speak to a simple multiplication or division of the EPI dataset by the estimated bias field. First, is it a correct assumption that you would divide the EPI dataset by the estimated bias field, such that voxels exhibiting higher intensities would be reduced? Second, is this a common practice in data pre-processing? Related to this question, in the above mentioned article, bias field estimation is performed on the 6th EPI volume. Is it advantageous to take that approach, or use the bias field estimated by FAST and transform that into native functional space? Or should they essentially be identical?
My last question is related to the impact of correcting the EPI dataset on beta-estimates after statistical modeling. Is there any reason to believe the distribution of beta's would change, i.e., increased accuracy, because of this technique?
Thanks in advance for any insight,
Michael Riedel
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