If you are already familiar with some of the AFNI processing steps, you might want to use @auto_tlrc to put an anatomical, B0 or FA dataset in Talairach or MNI space and then use align_epi_anat.py to align the DWI or FA data to the anatomical and apply the combined transformation to put either in the standard space:
@auto_tlrc anat+orig
align_epi_anat.py -anat anat+orig -epi FA+orig -cost lpa -epi_base 0 _suffix _al2anat -tlrc_apar anat+tlrc -prep_off
or
# to do motion correction of DWI data at the same time
align_epi_anat.py -anat anat+orig -epi DWI+orig -cost lpa -epi_base 0 _suffix _al2anat -tlrc_apar anat+tlrc -volreg_method 3dAllineate
Note transforming the DWI data means you will have also have to rotate the gradient vectors before computing the tensor. See previous threads on how to do this:
[
afni.nimh.nih.gov]
<[
afni.nimh.nih.gov];
For you second question, use 3dUndump -srad to create spheres based on the anat+tlrc dataset, and then put back into original space using one of the methods suggested here:
[
afni.nimh.nih.gov]
<[
afni.nimh.nih.gov];
You can also check approaches like this one that combine AFNI with DTI-Query:
[
openwetware.org]
[
openwetware.org]
or the Tortoise package:
[
www.tortoisedti.org]
[
www.tortoisedti.org]
