What is recommended way to distortion correct multiecho data using the blipup blupdown (In my case AP-PA).
I have fmri time series data with 3 echo’s. Currently, I use the AP epi file from the first echo in the fmap folder and pointed it to all echo time series in the func folder using the IntendedFor field. I reasoned that the same transformation on all 3 echo’s would be best for meica/tedana analyses.
Is this correct? Or should I use 3 separate AP epi files; one for each echo?
I’d recommend applying the same spatial transform to each echo. A fundamental assumption of multi-echo analyses is that the data in each voxel represents the same underlying anatomical volume, just at a different echo time. Applying different spatial transforms to each echo would violate that assumption.
That said, spatial distortion can increase with echo time, which would also violate that assumption. A blipup blipdown correct might be able to correct for this issue and actually result in more spatial similarity within each voxel. One would need to set constraints to make sure the variation in distortion corrections across echos is plausible and the interpolation method doesn’t cause additional problems. I don’t know of anyone who has specifically looked into this in the context of multi-echo fMRI denoising. It could be a promising methodological research direction, but I wouldn’t recommend applying echo-specific distortion corrections without first doing such an evaluation.
Thanks so much for your answer Dan! The possible relation between distortion and echo time was indeed underlying my question here.
I’ll keep using one PA file then.
The amount of distortion depends on the B0 field and the total readout time. Unless your multi-echo sequence is a really fancy one, all the echoes will have the same readout, so the amount of distortion is the same for all of them. What changes with the echo time is the amount of dropout.