Preprocess Rat Brains Structural
Your rat data may need preprocessing, therefore here are the preprocessing steps performed by the script mentioned here:
- flip
- reorder dimensions to standard "RMINC" mouse ordering
- center
- N4 bias field correct everything
- Otsu mask
- re-bias field correct with otsu mask
First, have your rat brains in MINC.
dcm2mnc -usecoordinates -anon -dname rat1 -fname rat1_t1 rat1 rat1_minc_folder
Use the tool rat-preprocessing.sh at the end of this page or inside the module minc-toolkit-extras.
Load the modules:
module load minc-toolkit/1.9.16 minc-stuffs/0.1.21 minc-toolkit-extras/1.9.16
The ways to use the script are either by file:
rat-preprocessing.sh input output
Example: rat-preprocessing.sh rat1_t1.mnc rat1_t1_preproc.mnc
Or, process in parallel.
for i in data/minc/*/*; do file=$(basename $i); echo rat-preprocessing.sh ${i} preproc/${file%.*}_pp.mnc; done > ratpreproc
qbatch -N fatrat_preproc --chunksize 1 --walltime=12:00:00 ratpreproc
Check the preprocessed files and that's it.
#!/bin/bash
#Script to do optimal preprocessing on in-vivo/ex-vivo structural scans
#Taken using the CIC Bruker 7T
#usage:
#mouse-preprocessing-v2.sh input.mnc output.mnc
#Operations
# centers brain in space
# denoises
# registers to Ratlas
# gets approximate brain mask from atlas
# expands mask
# Bias field correction with N4
set -euo pipefail
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=${THREADS_PER_COMMAND:-$(nproc)}
REGTARGET=/opt/quarantine/resources/Ratlas/cropped/rescale_centered.mnc
REGMASK=/opt/quarantine/resources/Ratlas/cropped/rat_mask_centered.mnc
tmpdir=$(mktemp -d)
input=$1
output=$2
set -x
maxval=$(mincstats -max -quiet ${input})
volflip -y $input $tmpdir/flip.mnc
volmash -swap zy $tmpdir/flip.mnc $tmpdir/mash.mnc
clean_and_center_minc.pl $tmpdir/mash.mnc $tmpdir/centered.mnc
#ImageMath 3 $tmpdir/centered.mnc RescaleImage $tmpdir/centered.mnc 0 32767
#minc_anlm --mt ${ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS} $tmpdir/centered.mnc $tmpdir/denoise.mnc
#cp -f $tmpdir/centered.mnc $tmpdir/denoise.mnc
minccalc -byte -unsigned -expression 'A[0]?1:1' $tmpdir/centered.mnc $tmpdir/fullmask.mnc
N4BiasFieldCorrection -d 3 -i $tmpdir/centered.mnc -b [30] -c [200x200x200,0.0] -r 0 -x $tmpdir/fullmask.mnc -o $tmpdir/corrected.mnc -s 4 --verbose
ThresholdImage 3 $tmpdir/corrected.mnc $tmpdir/otsu.mnc Otsu 1
N4BiasFieldCorrection -d 3 -i $tmpdir/centered.mnc -b [30] -c [200x200x200,0.0] -r 0 -w $tmpdir/otsu.mnc -x $tmpdir/fullmask.mnc -o $tmpdir/corrected.mnc -s 2 --verbose
#antsRegistration --dimensionality 3 --float 0 --collapse-output-transforms 1 --minc \
#--output $tmpdir/trans \
#--use-histogram-matching 0 \
#--transform Rigid[0.1] --metric Mattes[${REGTARGET},$tmpdir/corrected.mnc,1,32,Regular,0.25] --convergence [2000x2000x2000x2000x0,1e-6,10,1] --shrink-factors 8x6x4x2x1 --smoothing-sigmas 4x3x2x1x0 --masks [NULL,NULL] \
#--transform Similarity[0.1] --metric Mattes[${REGTARGET},$tmpdir/corrected.mnc,1,32,Regular,0.5] --convergence [2000x2000x2000,1e-6,10,1] --shrink-factors 6x4x2 --smoothing-sigmas 3x2x1 --masks [NULL,NULL] \
#--transform Affine[0.1] --metric Mattes[${REGTARGET},$tmpdir/corrected.mnc,1,32,Regular,1] --convergence [2000x2000x0,1e-6,10,1] --shrink-factors 4x2x1 --smoothing-sigmas 2x1x0 --masks [${REGMASK},NULL] --verbose
#antsApplyTransforms -d 3 -i ${REGMASK} -o $tmpdir/mask.mnc -t [$tmpdir/trans0_GenericAffine.xfm,1] -r $tmpdir/denoise.mnc -n NearestNeighbor
#mincmorph -3D26 -successive D $tmpdir/mask.mnc $tmpdir/weight.mnc
#ImageMath 3 $tmpdir/trunc.mnc TruncateImageIntensity $tmpdir/denoise.mnc 0.025 0.9995 1024 $tmpdir/weight.mnc
#N4BiasFieldCorrection -d 3 -i $tmpdir/trunc.mnc -b [30] -c [200x200x200,0.0] -w $tmpdir/weight.mnc -r 0 -x $tmpdir/fullmask.mnc -o $tmpdir/corrected.mnc -s 4 --verbose
#ImageMath 3 $tmpdir/norm.mnc Normalize $tmpdir/corrected.mnc
#ImageMath 3 $output m $tmpdir/norm.mnc ${maxval}
ImageMath 3 $output RescaleImage $tmpdir/corrected.mnc 0 ${maxval}
#cp -f $tmpdir/weight.mnc $(dirname $output)/$(basename $output .mnc)_mask.mnc
rm -rf $tmpdir```