"The goodness of the alignment should always be assessed visually" !
With this sentence keeping in mind, I am wondering if there is an AFNI method conveniently available in assisting this assessment, particularly with a relatively "big" sample size. For example, I received data from several hundreds of subjects with their alignments of "anat2epi" already completed (done by someone else in the past with align_epi_anat.py), so each subject has a file pair of "epi+orig" and "anat_al+orig". Now I want to check the goodness of these previous alignments, but it is going to be time comsuming considering that each subject also has multiple scanning sessions. In this situation, I am wondering if I can simply skip the alignment procedure in align_epi_anat.py or 3dAllineate but just run their cost functions; something like:
for each subjects
3dAllineate -skip_alignment -base epi+orig -input anat_al+orig -cost lpa >> cost_file.1D
This would allow me to collect the "cost" values of each subject in the "cost_file.1D"; then I may just visually inspect individuals with a high, say the top 10%, cost value; and if those top costs (worst alignments) can pass my visual inspections, I can reasonably assume that all alignments are fine.
Am I correct? What can I do to realize the option of "-skip_alignment"?
