afni.nimh.nih.gov

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`"




# ==========================================================================
# script generated by the command:
#
# afni_proc.py -subj_id sub03 -dsets dmeanf_sub03.nii -blocks align volreg \
# -volreg_align_e2a -copy_anat T1.nii