we realized this has been a topic before, but we so far haven't found a satisfying procedure.
We have DTI data with interspersed b0 images (b0 - 20 x b1000 - b0 - 20 x b1000 - b0 - 20 x b1000 - 9 x b0).
In order to exploit all information we've available, we thought about first eddy correcting by splitting the dataset into three sets, and correct each with the nearest b0 before as a reference.
For this we would use the old eddy_correct (before doing a BET we'd like to eddy correct, and the new eddy needs a brain mask as input) .
Then we would merge the sets, and eddy correct the whole set with reference to the average b0 (taking all b0), applying the new eddy function.
We also thought about whether it's necessary to run a first eddy in the beginning with the first b0 as the reference.
The b0 are all phase encoded in the same direction, so we'd not use the topup-tool.
Does this make sense, is it not necessary or are there better ways to do this?
Any help is much appreciated!