def move_to_MNI_space(input_file, bvals, bvecs, brain_mask, output_dir):
print("Moving input to MNI space...")
os.system("calc_FA -i " + input_file + " -o " + output_dir +
"/FA.nii.gz --bvals " + bvals + " --bvecs " + bvecs +
" --brain_mask " + brain_mask)
dwi_spacing = img_utils.get_image_spacing(input_file)
template_path = resource_filename('resources', 'MNI_FA_template.nii.gz')
os.system("flirt -ref " + template_path + " -in " + output_dir +
"/FA.nii.gz -out " + output_dir + "/FA_MNI.nii.gz -omat " +
output_dir +
"/FA_2_MNI.mat -dof 6 -cost mutualinfo -searchcost mutualinfo")
os.system("flirt -ref " + template_path + " -in " + input_file + " -out " +
output_dir + "/Diffusion_MNI.nii.gz -applyisoxfm " +
dwi_spacing + " -init " + output_dir + "/FA_2_MNI.mat -dof 6")
os.system("cp " + bvals + " " + output_dir + "/Diffusion_MNI.bvals")
os.system("cp " + bvecs + " " + output_dir + "/Diffusion_MNI.bvecs")
new_input_file = join(output_dir, "Diffusion_MNI.nii.gz")
bvecs = join(output_dir, "Diffusion_MNI.bvecs")
bvals = join(output_dir, "Diffusion_MNI.bvals")
brain_mask = create_brain_mask(new_input_file, output_dir)
return new_input_file, bvals, bvecs, brain_mask
def move_to_subject_space(output_dir,
bundles,
experiment_type,
output_subdir,
output_float=False):
#This is not working for TOM (because of processing in parts) and requirement of bundle_segmentations as input
print("Moving output to subject space...")
# For peaks we can not use linear/spline interpolation. We have to use nn or convert to tensor and then
# resample with linear interpolation.
interp = "nearestneighbour" if experiment_type == "peak_regression" else "trilinear"
os.system("mkdir -p " + output_dir + "/" + output_subdir + "_MNI")
os.system("mv " + output_dir + "/" + output_subdir + "/* " + output_dir +
"/" + output_subdir + "_MNI")
os.system("convert_xfm -omat " + output_dir + "/MNI_2_FA.mat -inverse " +
output_dir + "/FA_2_MNI.mat")
for bundle in bundles:
file_path_in = output_dir + "/" + output_subdir + "_MNI/" + bundle + ".nii.gz"
file_path_out = output_dir + "/" + output_subdir + "/" + bundle + ".nii.gz"
dwi_spacing = img_utils.get_image_spacing(file_path_in)
os.system("flirt -ref " + output_dir + "/FA.nii.gz -in " +
file_path_in + " -out " + file_path_out + " -applyisoxfm " +
dwi_spacing + " -init " + output_dir +
"/MNI_2_FA.mat -dof 6" + " -interp " + interp)
if not output_float:
os.system("fslmaths " + file_path_out + " -thr 0.5 -bin " +
file_path_out)
def move_to_subject_space(output_dir):
print("Moving input to subject space...")
file_path_in = output_dir + "/bundle_segmentations.nii.gz"
file_path_out = output_dir + "/bundle_segmentations_subjectSpace.nii.gz"
dwi_spacing = img_utils.get_image_spacing(file_path_in)
os.system("convert_xfm -omat " + output_dir + "/MNI_2_FA.mat -inverse " + output_dir + "/FA_2_MNI.mat")
os.system("flirt -ref " + output_dir + "/FA.nii.gz -in " + file_path_in + " -out " + file_path_out +
" -applyisoxfm " + dwi_spacing + " -init " + output_dir + "/MNI_2_FA.mat -dof 6")
os.system("fslmaths " + file_path_out + " -thr 0.5 -bin " + file_path_out)
def move_to_subject_space_single_file(output_dir, experiment_type, output_subdir, output_float=False):
print("Moving output to subject space...")
os.system("mv " + output_dir + "/" + output_subdir + ".nii.gz " + output_dir + "/" + output_subdir + "_MNI.nii.gz")
file_path_in = output_dir + "/" + output_subdir + "_MNI.nii.gz"
file_path_out = output_dir + "/" + output_subdir + ".nii.gz"
dwi_spacing = img_utils.get_image_spacing(file_path_in)
os.system("convert_xfm -omat " + output_dir + "/MNI_2_FA.mat -inverse " + output_dir + "/FA_2_MNI.mat")
os.system("flirt -ref " + output_dir + "/FA.nii.gz -in " + file_path_in + " -out " + file_path_out +
" -applyisoxfm " + dwi_spacing + " -init " + output_dir + "/MNI_2_FA.mat -dof 6" +
" -interp trilinear")
if not output_float:
os.system("fslmaths " + file_path_out + " -thr 0.5 -bin " + file_path_out)
def move_to_subject_space(output_dir,
bundles,
experiment_type,
output_subdir,
output_float=False):
print("Moving output to subject space...")
os.system("mkdir -p " + output_dir + "/" + output_subdir + "_MNI")
os.system("mv " + output_dir + "/" + output_subdir + "/* " + output_dir +
"/" + output_subdir + "_MNI")
os.system("convert_xfm -omat " + output_dir + "/MNI_2_FA.mat -inverse " +
output_dir + "/FA_2_MNI.mat")
for bundle in tqdm(bundles):
file_path_in = output_dir + "/" + output_subdir + "_MNI/" + bundle + ".nii.gz"
file_path_out = output_dir + "/" + output_subdir + "/" + bundle + ".nii.gz"
dwi_spacing = img_utils.get_image_spacing(file_path_in)
if experiment_type == "peak_regression":
os.system("flip_peaks -i " + file_path_in + " -o " +
file_path_in[:-7] +
"_flip.nii.gz -a x") # flip to fsl format
os.system("vecreg -i " + file_path_in[:-7] + "_flip.nii.gz -o " +
file_path_out + " -r " + output_dir + "/FA.nii.gz -t " +
output_dir +
"/MNI_2_FA.mat") # Use vecreg to transform peaks
os.system("flip_peaks -i " + file_path_out + " -o " +
file_path_out + " -a x") # flip back to mrtrix format
os.system("rm " + file_path_in[:-7] +
"_flip.nii.gz") # remove flipped tmp file
else:
# do not use spline interpolation because makes a lot of holes into masks
os.system("flirt -ref " + output_dir + "/FA.nii.gz -in " +
file_path_in + " -out " + file_path_out +
" -applyisoxfm " + dwi_spacing + " -init " + output_dir +
"/MNI_2_FA.mat -dof 6" + " -interp trilinear")
if not output_float:
os.system("fslmaths " + file_path_out + " -thr 0.5 -bin " +
file_path_out)