align_epi_anat.py
-anat AK_expvol+orig
-epi AK_expvol02_deobl+orig
-epi_base 5
-epi2anat
-volreg off
-anat_has_skull yes
-deoblique off
-suffix _al_aligned
As the script depicts, a partial 3d image (TSE, AK_expvol02) was deoblique processed. And then, it was aligned to the T1 image (AK_expvol+orig). However, the alignment was not successful, as you can see in the screenshot. In this case, what can I try to align perfectly?
As you can see in some screenshots, the two 3d images have a different gap between the voxels. Is the different size of the voxel gap across two different 3d images the matter?
Partial images can be particularly difficult. If the data starts out this far apart, it seems there is something off in the coordinates. Try “-giant_move” to overcome that and allow movement a long distance. Cost functions like lpc+ZZ, lpc and nmi may be used too (-cost …). There are options for partial coverage, but that assumes movement mostly within the planes of the acquisition, and that doesn’t appear to be the case. I would remove the deobliquing step except as a test to check for appropriate coordinates and use the original data to avoid interpolating the EPI data twice.
Unfortunately, changing cost functions and several different options of move (cmass, big, giant, and ginormous) also failed to align the partial anatomical 3d image. But, the alignment process was a success by changing another option. In the original script that I used before, I differentiated the whole brain 3d and the partial image by using ‘-anat’ and ‘-epi’ though both images are 3d. After I changed the parts to ‘-dset1 and -dset2’, I think the alignment was successful, as you can see in the attached image. I do not know why the difference was made by changing the parts. Anyway, I would like to appreciate your help, and I hope my experience will be helpful to other AFNI users. I also attached the original and new script.
I’m glad you got it worked out. The dset1,2 terminology is a shortcut for a couple things, but mostly the effect here is probably from a change in the default cost function to lpa.
The
National Institute of Mental Health (NIMH) is part of the National Institutes of
Health (NIH), a component of the U.S. Department of Health and Human
Services.
