Hi AFNIers.
I'm running AFNI version=AFNI_18.1.01 (Apr 4 2018) [64-bit]
I have two nonlinear warps that I want to concatenate using 3dNwarpCat:
$ 3dNwarpCat -warp1 A-warp_for.nii.gz -warp2 B-warp_for.nii.gz -prefix AB-warp_for.nii.gz
Here, warp B maps the original anatomical to an intermediate, subject-specific template; warp A maps the subject-specific template to an MNI template.
I get the following error:
** ERROR: warp from dataset 'B-warp_for.nii.gz' doesn't match earlier input geometry
However, the program continues to run and it outputs the file AB-warp_for.nii.gz.
The subject-specific template does not lie on the MNI grid, which is what may be triggering this error. The documentation for 3dNwarpCat states:
* If any of the input warps are datasets, they must all be defined on the same 3D grid!
However, if I understand correctly, 3dNwarpApply, with the -nwarp option, does not have this limitation. Given that my run of 3dNwarpCat does not abort and indeed outputs a (correct?) warp, I'm wondering if the ERROR message above is a vestigial warning left over from a previous, less sophisticated version of 3dNwarpCat, and that the current version uses the same mechanism as 3dNwarpApply to interpolate coordinates between non-coincident warp chains.
I would use 3dNwarpApply directly, but I aim to generate the concatenated warp's jacobian (via the -bulk option of 3dNwarpFuncs) of the concatenated warp. (Note that 3dNwarpFuncs has the -nwarp option as well, but it generates the same error message as 3dNwarpCat listed above.)
So, I guess my question is: do I trust the warp provided by 3dNwarpCat despite the error message it gives?
%%% -
Just as a follow-up: I've used 3dNwarpApply with the warp computed by 3dNwarpCat (as above) and using the concatenation approach with 3dNwarpApply -nwarp option. The results are similar, but not identical: the two volumes seem aligned, but the voxel values are within 10%, not as close as I'd be comfortable with.
Any insight you might have into this would be most welcome. Thank you very much!
- Daniel
