Hi AFNI experts,
I have some confusions about “auto_warp.py” and “3dQwarp”. Does “auto_warp.py” perform the same things as “3dQwarp”?
If I want to do nonlinear normalization, should I use “auto_warp.py” instead of “3dQwarp”?
Thank you!
–Dan
Hi-
The auto_warp.py function is a wrapper for 3dQwarp, but it does a bit of pre-processing on the input volume before aligning it using 3dSkullstrip and 3dUnifize. So, in that sense, it might be preferable to use auto_warp.py to start with (I find it’s often helpful to ‘polish’ up images for helping alignment, so single spikes in values in CSF or edges don’t ruin everything). In the history of your output from auto_warp.py, you will see the full set of 3d* commands, including 3dQwarp.
–pt
Also auto_warp.py includes an affine alignment step by default, which may be something you want; it’s often a good starting point. It call @auto_tlrc to do that step. 3dQwarp has a -allineate option that can do something similar. The transformations are concatenated together in that script. Another difference is auto_warp.py puts all its output into a separate directory. It’s probably just easier to let auto_warp.py worry about the details unless you need to do some more tweaking of the options.
Here is a script I have used recently to analyze a lot (198) of datasets. This script was used to warp the Cambridge subset of the FCON-1000 collection. It uses auto_warp.py, since that makes it easy to use the warped results with afni_proc.py in turn. The script below takes on the command line one argument, the subject ID; e.g, the command
tcsh warper.csh sub00156
will work on dataset anat_sub00156.nii.gz. I then submitted 198 runs of this script to the NIH Linux cluster.
The base.nii dataset to which the anat dataset is aligned was a 3dUnifize-d version of the MNI152_T1_2009c+tlrc.HEAD that is supplied with AFNI. This dataset is a nonlinearly registered version of the MNI template, and was created by MNI, not by the AFNI empire. This dataset was created with the command
3dUnifize -prefix base.nii -input MNI152_T1_2009c+tlrc -GM
I wrote this script partly because the default parameters to 3dSkullStrip didn’t work well with these datasets, which have had part of the face region simply zero-ed out, which caused funny things to happen in some cases. The 3dSkullStrip parameters used below worked well for these cases, but of course you should check your results!
Note: running to minpatch=11 (as set at the top of the script) will take longer than the default 25 – probably about twice as long. As I recall (this was months ago), each subject took about 2 hours to run – which is why learning to use a cluster is handy, so the jobs can run in parallel.
#!/bin/tcsh
# set the subject ID and the minimum warp patch size
set sub = $argv[1]
set minp = 11
# go to data directory
set topdir = /data/NIMH_SSCC/fcon1000.perm.test/Cambridge
cd $topdir/anat_orig
# create final output directory if needed
mkdir -p $topdir/anat_warped
# create temporary directory to hold the work, and copy data there
mkdir -p temp_$sub
cp anat_$sub.nii.gz base.nii temp_$sub
cd temp_$sub
# uniformize the T1 intensity
3dUnifize -prefix anatU_$sub.nii -input anat_$sub.nii.gz -GM
# strip skull
3dSkullStrip -input anatU_$sub.nii \
-prefix anatS_$sub.nii \
-ld 33 -niter 777 -shrink_fac_bot_lim 0.777 -exp_frac 0.0666
# warp to the base dataset
auto_warp.py \
-base base.nii \
-input anatS_$sub.nii \
-skull_strip_base no \
-skull_strip_input no \
-unifize_input no \
-qw_opts -noneg -pblur -minpatch $minp -workhard:0:4
# compress output datasets and move to final output directory
\rm awpy/base.nii
gzip awpy/*.nii
mv awpy $topdir/anat_warped/$sub.awpy
# delete the temporary directory
cd ..
\rm -rf temp_$sub