Hi all - quick update - the container is currently rebuilding to (1) output the field map directly and also to (2) enable a user-defined acqparams file (actually just a TotalReadoutTime variable) so that the field map has meaningful units. We may actually kick off another docker build to also allow a user defined .cnf file if one chooses to modify some of the topup parameters. We want to run a quick couple of tests but will hopefully upload this by the weekend or early next week. This will let me run some tests to see why our field map looked a bit unconventional (higher frequency + streaks?).
@mblesac - One could argue that you should do whatever is most conventional for a traditional analysis as if you did have field maps of reverse PE available. In most cases (just browsing some datasets from openneuro) it seems only a single field map or single reverse PE pair is acquired prior to all FMRI runs. So I believe it would be common to use the field map (or field map estimated from the rPE pair) for all subsequent scans, assuming contrast and bandwidth stay the same. However, if you have reason to believe that the field map may change over a period of time across runs (movement? hardware drift?) then I see no reason not to estimate the field for each run separately - although I have no expertise in FMRI and preprocessing!
One important note: if you do have reverse PE or field maps available we still suggest using these! It’s not that we don’t believe in our algorithm, we are just trying to make it clear that we aren’t claiming we are “better” in any way. In fact, we are just using tricks to estimate a field map through image processing rather than image acquisition (and use topup itself in doing so)!
Could you explain the intensity differences you are seeing? I see bias field present in both distorted and corrected images (which make sense because we don’t correct for this at this point). Applying the topup field map will also correct small signal stretch/pile-up, although this is much less noticeable in gradient echo images than in the diffusion spin echo I’m used to working with! Finally, we have the --scale option in topup set to 1, where topup will rescale each image to the individual mean. This could also cause an intensity difference, although it would be a single multiplicative factor for the entire volume.
@mblesac and @jsein are the field map visualizations and alignment with t1 + edges a QA output of fmriprep? These are outstanding QA visualizations and have convinced me to make sure any future updates are easily integrated with other packages!