My guess is that there is no problem here. If the data is oblique, then you can do any of these steps

1. Warp one dataset to match the other - this doesn't explicitly align the data, but they should appear to be roughly aligned in the afni GUI.
2. Deoblique both datasets or just the oblique dataset. If the origins are the same, then that should also show close alignment.
3. Align the original data with align_epi_anat.py or with afni_proc.py. That will automatically include the obliquity transformations to match one dataset to the other and compute an alignment too. Both transformations and motion correction transformations will be combined. That helps reduced smoothing from multiple interpolations.
4. Ignore or remove the obliquity information and align with a -giant_move or even -ginormous_move. Because the alignment allows for large distances and up to 45 degree rotations, oblique datasets can be aligned without more information. For datasets with good structural contrast in the EPI data, this works well, and it's often useful for datasets with questionable or missing NIFTI headers.

You can examine a specific dataset with 3dinfo to see if it is considered oblique. Again warnings are just warnings. If you've taken these into account using one of the methods, then you're most likely fine. You can turn off or limit warnings in the afni GUI with the environment variables, AFNI_NO_OBLIQUE_WARNING and AFNI_ONE_OBLIQUE_WARNING. It's easiest to set those in your ~/.afnirc file. There is a brief description of obliquity handling here:

[afni.nimh.nih.gov]



Edited 1 time(s). Last edit at 12/15/2016 04:18PM by Daniel Glen.