Hi,
I have a afni_proc command to do the alignment from epi to anat:
afni_proc.py -subj_id sub03 -dsets dmeanf_sub03.nii -blocks align volreg -volreg_align_e2a -copy_anat T1.nii
the resulted proc.sub03 script has the align block codes like this:
# ================================= align ==================================
# for e2a: compute anat alignment transformation to EPI registration base
# (new anat will be intermediate, stripped, T1_ns+orig)
align_epi_anat.py -anat2epi -anat T1+orig \
-save_skullstrip -suffix _al_junk \
-epi vr_base+orig -epi_base 0 \
-epi_strip 3dAutomask \
-volreg off -tshift off
This codes work perfectly for my data. But just out of curiosity, the comments and the parameter (-anat2epi) both showed that it seems like the matrix is from anat to epi? Am i ignoring something very obvious?
Thanks a lot! Have a good weekends!
Mengxing
below is the full codes of proc.sub03
#!/bin/tcsh -xef
echo “auto-generated by afni_proc.py, Sat Jul 20 11:17:59 2019”
echo “(version 6.45, July 19, 2019)”
echo “execution started: date”
to execute via tcsh:
tcsh -xef proc.sub03 |& tee output.proc.sub03
to execute via bash:
tcsh -xef proc.sub03 2>&1 | tee output.proc.sub03
=========================== auto block: setup ============================
script setup
take note of the AFNI version
afni -ver
check that the current AFNI version is recent enough
afni_history -check_date 27 Jun 2019
if ( $status ) then
echo “** this script requires newer AFNI binaries (than 27 Jun 2019)”
echo " (consider: @update.afni.binaries -defaults)"
exit
endif
the user may specify a single subject to run with
if ( $#argv > 0 ) then
set subj = $argv[1]
else
set subj = sub03
endif
assign output directory name
set output_dir = $subj.results
verify that the results directory does not yet exist
if ( -d $output_dir ) then
echo output dir “$subj.results” already exists
exit
endif
set list of runs
set runs = (count -digits 2 1 1)
create results and stimuli directories
mkdir $output_dir
mkdir $output_dir/stimuli
copy anatomy to results dir
3dcopy T1.nii $output_dir/T1
============================ auto block: tcat ============================
apply 3dTcat to copy input dsets to results dir,
while removing the first 0 TRs
3dTcat -prefix $output_dir/pb00.$subj.r01.tcat dmeanf_sub03.nii’[0…$]’
and make note of repetitions (TRs) per run
set tr_counts = ( 1 )
-------------------------------------------------------
enter the results directory (can begin processing data)
cd $output_dir
--------------------------------
extract volreg registration base
3dbucket -prefix vr_base pb00.$subj.r01.tcat+orig"[0]"
================================= align ==================================
for e2a: compute anat alignment transformation to EPI registration base
(new anat will be intermediate, stripped, T1_ns+orig)
align_epi_anat.py -anat2epi -anat T1+orig
-save_skullstrip -suffix _al_junk
-epi vr_base+orig -epi_base 0
-epi_strip 3dAutomask
-volreg off -tshift off
================================= volreg =================================
align each dset to base volume, to anat
register and warp
foreach run ( $runs )
# register each volume to the base image
3dvolreg -verbose -zpad 1 -base vr_base+orig
-1Dfile dfile.r$run.1D -prefix rm.epi.volreg.r$run
-cubic
-1Dmatrix_save mat.r$run.vr.aff12.1D
pb00.$subj.r$run.tcat+orig
# create an all-1 dataset to mask the extents of the warp
3dcalc -overwrite -a pb00.$subj.r$run.tcat+orig -expr 1 \
-prefix rm.epi.all1
# catenate volreg/epi2anat xforms
cat_matvec -ONELINE \
T1_al_junk_mat.aff12.1D -I \
mat.r$run.vr.aff12.1D > mat.r$run.warp.aff12.1D
# apply catenated xform: volreg/epi2anat
3dAllineate -base T1_ns+orig \
-input pb00.$subj.r$run.tcat+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 2 \
-prefix rm.epi.nomask.r$run
# warp the all-1 dataset for extents masking
3dAllineate -base T1_ns+orig \
-input rm.epi.all1+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 2 -final NN -quiet \
-prefix rm.epi.1.r$run
# make an extents intersection mask of this run
3dTstat -min -prefix rm.epi.min.r$run rm.epi.1.r$run+orig
end
make a single file of registration params
cat dfile.r*.1D > dfile_rall.1D
compute motion magnitude time series: the Euclidean norm
(sqrt(sum squares)) of the motion parameter derivatives
1d_tool.py -infile dfile_rall.1D -set_nruns 1
-derivative -collapse_cols euclidean_norm
-write motion_${subj}_enorm.1D
----------------------------------------
create the extents mask: mask_epi_extents+orig
(this is a mask of voxels that have valid data at every TR)
(only 1 run, so just use 3dcopy to keep naming straight)
3dcopy rm.epi.min.r01+orig mask_epi_extents
and apply the extents mask to the EPI data
(delete any time series with missing data)
foreach run ( $runs )
3dcalc -a rm.epi.nomask.r$run+orig -b mask_epi_extents+orig
-expr ‘a*b’ -prefix pb01.$subj.r$run.volreg
end
warp the volreg base EPI dataset to make a final version
cat_matvec -ONELINE T1_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D
3dAllineate -base T1_ns+orig
-input vr_base+orig
-1Dmatrix_apply mat.basewarp.aff12.1D
-mast_dxyz 2
-prefix final_epi_vr_base
create an anat_final dataset, aligned with stats
3dcopy T1_ns+orig anat_final.$subj
record final registration costs
3dAllineate -base final_epi_vr_base+orig -allcostX
-input anat_final.$subj+orig |& tee out.allcostX.txt
-----------------------------------------
warp anat follower datasets (identity: resample)
================== auto block: generate review scripts ===================
generate a review script for the unprocessed EPI data
gen_epi_review.py -script @epi_review.$subj
-dsets pb00.$subj.r*.tcat+orig.HEAD
========================== auto block: finalize ==========================
remove temporary files
\rm -f rm.*
if the basic subject review script is here, run it
(want this to be the last text output)
if ( -e @ss_review_basic ) then
./@ss_review_basic |& tee out.ss_review.$subj.txt
# generate html ss review pages
# (akin to static images from running @ss_review_driver)
apqc_make_tcsh.py -review_style basic -subj_dir . \
-uvar_json out.ss_review_uvars.json
tcsh @ss_review_html |& tee out.review_html
apqc_make_html.py -qc_dir QC_$subj
echo "\nconsider running: \n\n afni_open -b $subj.results/QC_$subj/index.html\n"
endif
return to parent directory (just in case…)
cd …
echo “execution finished: date”