US_to_MRI.sh

#!/bin/sh

# Input arguments

case_num=${1-"1"}
image_folder=${2:-"bucket/RESECT/RESECT/NIFTI/Case${case_num}"}
us_image=${3:-"$image_folder/US/Case${case_num}-US-before.nii.gz"}
mri_image=${4:-"$image_folder/MRI/Case${case_num}-FLAIR.nii.gz"}
tag_file=${5:-"$image_folder/Landmarks/Case${case_num}-MRI-beforeUS.tag"}

# Create directory to store the outputs
mkdir -p $image_folder/output/deeds

output_folder="$image_folder/output/deeds"

vox_size=0.5

# Resample images into a common reference frame and isotropic voxel size of 0.5x0.5x0.5 mm
c3d $us_image $mri_image -reslice-identity -resample-mm ${vox_size}x${vox_size}x${vox_size}mm -o $output_folder/Case${case_num}-MRI_in_US.nii.gz
c3d $us_image -resample-mm ${vox_size}x${vox_size}x${vox_size}mm -o $output_folder/Case${case_num}-US.nii.gz


# Calculate linear rigid pre-registration using deeds
./linearBCV -F $output_folder/Case${case_num}-MRI_in_US.nii.gz -M $output_folder/Case${case_num}-US.nii.gz -R 1 -O $output_folder/affine

# Generate 2 text files containing landmarks
python3 ./landmarks_split_txt.py --inputtag $tag_file --savetxt $output_folder/Case${case_num}_lm

# Generate landmark segmentations as a NIFTI file
c3d $output_folder/Case${case_num}-MRI_in_US.nii.gz -scale 0 -landmarks-to-spheres $output_folder/Case${case_num}_lm_mri.txt 1 -o $output_folder/Case${case_num}-MRI-landmarks.nii.gz
c3d $output_folder/Case${case_num}-US.nii.gz -scale 0 -landmarks-to-spheres $output_folder/Case${case_num}_lm_us.txt 1 -o $output_folder/Case${case_num}-US-landmarks.nii.gz


# Apply linear preregistration to landmarks
python3 ./generate_zero_displacements.py $output_folder $output_folder/Case${case_num}-US.nii.gz

./applyBCV -M $output_folder/Case${case_num}-US-landmarks.nii.gz \
-O $output_folder/zero \
-D $output_folder/Case${case_num}-US-landmarks_linear.nii.gz \
-A $output_folder/affine_matrix.txt

# Perform non linear registration
./deedsBCV -M $output_folder/Case${case_num}-US.nii.gz \
-F $output_folder/Case${case_num}-MRI_in_US.nii.gz \
-O $output_folder/Case${case_num}-deeds \
-S $output_folder/Case${case_num}-US-landmarks.nii.gz \
-A $output_folder/affine_matrix.txt \
-a 1.6 # make a smoother but less accurate registration

# Perform fitting step
./linearBCV -F $output_folder/Case${case_num}-MRI_in_US.nii.gz -M $output_folder/Case${case_num}-deeds_deformed.nii.gz -R 1 -O $output_folder/affine_fit


# Apply fitting step
./applyBCV -M $output_folder/Case${case_num}-deeds_deformed_seg.nii.gz \
-O $output_folder/zero \
-D $output_folder/Case${case_num}-US-landmarks_fitted.nii.gz \
-A $output_folder/affine_fit_matrix.txt


# Calculate mTRE
python3 ./landmarks_centre_mass.py \
--inputnii $output_folder/Case${case_num}-MRI-landmarks.nii.gz \
--movingnii $output_folder/Case${case_num}-US-landmarks_fitted.nii.gz \
--savetxt $output_folder/Case${case_num}-results

# Remove unnecessary files
rm $output_folder/mask_US.nii.gz
rm $output_folder/Case${case_num}_lm*
rm $output_folder/Case${case_num}-results*