def check_gif_input(image_path):
from mrtrix3 import image, MRtrixError
dim = image.Header(image_path).size()
if len(dim) < 4:
raise MRtrixError('Image \'' + image_path + '\' does not look like GIF segmentation (less than 4 spatial dimensions)')
if min(dim[:4]) == 1:
raise MRtrixError('Image \'' + image_path + '\' does not look like GIF segmentation (axis with size 1)')
def checkGIFinput(image_path):
from mrtrix3 import app, image
dim = image.Header(image_path).size()
if len(dim) < 4:
app.error('Image \'' + image_path + '\' does not look like GIF segmentation (less than 4 spatial dimensions)')
if min(dim[:4]) == 1:
app.error('Image \'' + image_path + '\' does not look like GIF segmentation (axis with size 1)')
def getScheme(arg): #pylint: disable=unused-variable
from mrtrix3 import app, image
if not isinstance(arg, image.Header):
if not isinstance(arg, str):
app.error('Error trying to derive phase-encoding scheme from \'' +
str(arg) + '\': Not an image header or file path')
arg = image.Header(arg)
if 'pe_scheme' in arg.keyval():
app.debug(str(arg.keyval()['pe_scheme']))
return arg.keyval()['pe_scheme']
if 'PhaseEncodingDirection' not in arg.keyval():
return None
line = direction(arg.keyval()['PhaseEncodingDirection'])
if 'TotalReadoutTime' in arg.keyval():
line = [float(value) for value in line]
line.append(float(arg.keyval()['TotalReadoutTime']))
num_volumes = 1 if len(arg.size()) < 4 else arg.size()[3]
app.debug(str(line) + ' x ' + str(num_volumes) + ' rows')
return [line] * num_volumes
DKI_root = os.path.abspath(os.path.join(designer_root, '..'))
app.makeTempDir()
fsl_suffix = fsl.suffix()
UserCpath = app.args.input.rsplit(',')
DWIlist = [os.path.realpath(i) for i in UserCpath]
isdicom = False
for i in DWIlist:
if not os.path.exists(i):
print('cannot find input ' + i)
quit()
if os.path.isdir(i):
format = image.Header(i).format()
if format == 'DICOM':
isdicom = True
else:
print('input is a directory but does not contain DICOMs, quitting')
quit()
DWIflist = [splitext_(i) for i in DWIlist]
DWInlist = [i[0] for i in DWIflist]
DWIext = [i[1] for i in DWIflist]
miflist = []
idxlist = []
dwi_ind_size = [[0, 0, 0, 0]]
if not app.args.fslbval:
bvallist = [i + '.bval' for i in DWInlist]
def runSubject(bids_dir, label, output_prefix):
output_dir = os.path.join(output_prefix, label)
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir)
os.makedirs(os.path.join(output_dir, 'connectome'))
os.makedirs(os.path.join(output_dir, 'dwi'))
fsl_path = os.environ.get('FSLDIR', '')
if not fsl_path:
app.error(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
flirt_cmd = fsl.exeName('flirt')
fslanat_cmd = fsl.exeName('fsl_anat')
fsl_suffix = fsl.suffix()
unring_cmd = 'unring.a64'
if not find_executable(unring_cmd):
app.console('Command \'' + unring_cmd +
'\' not found; cannot perform Gibbs ringing removal')
unring_cmd = ''
dwibiascorrect_algo = '-ants'
if not find_executable('N4BiasFieldCorrection'):
# Can't use findFSLBinary() here, since we want to proceed even if it's not found
if find_executable('fast') or find_executable('fsl5.0-fast'):
dwibiascorrect_algo = '-fsl'
app.console('Could not find ANTs program N4BiasFieldCorrection; '
'using FSL FAST for bias field correction')
else:
dwibiascorrect_algo = ''
app.warn(
'Could not find ANTs program \'N4BiasFieldCorrection\' or FSL program \'fast\'; '
'will proceed without performing DWI bias field correction')
if not app.args.parcellation:
app.error(
'For participant-level analysis, desired parcellation must be provided using the -parcellation option'
)
parc_image_path = ''
parc_lut_file = ''
mrtrix_lut_file = os.path.join(
os.path.dirname(os.path.abspath(app.__file__)), os.pardir, os.pardir,
'share', 'mrtrix3', 'labelconvert')
if app.args.parcellation == 'fs_2005' or app.args.parcellation == 'fs_2009':
if not 'FREESURFER_HOME' in os.environ:
app.error(
'Environment variable FREESURFER_HOME not set; please verify FreeSurfer installation'
)
if not find_executable('recon-all'):
app.error(
'Could not find FreeSurfer script recon-all; please verify FreeSurfer installation'
)
parc_lut_file = os.path.join(os.environ['FREESURFER_HOME'],
'FreeSurferColorLUT.txt')
if app.args.parcellation == 'fs_2005':
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'fs_default.txt')
else:
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'fs_a2009s.txt')
if app.args.parcellation == 'aal' or app.args.parcellation == 'aal2':
mni152_path = os.path.join(fsl_path, 'data', 'standard',
'MNI152_T1_1mm.nii.gz')
if not os.path.isfile(mni152_path):
app.error(
'Could not find MNI152 template image within FSL installation (expected location: '
+ mni152_path + ')')
if app.args.parcellation == 'aal':
parc_image_path = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V4.nii'))
parc_lut_file = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V4.txt'))
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'aal.txt')
else:
parc_image_path = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V5.nii'))
parc_lut_file = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V5.txt'))
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'aal2.txt')
if parc_image_path and not os.path.isfile(parc_image_path):
if app.args.atlas_path:
parc_image_path = [
parc_image_path,
os.path.join(os.path.dirname(app.args.atlas_path),
os.path.basename(parc_image_path))
]
if os.path.isfile(parc_image_path[1]):
parc_image_path = parc_image_path[1]
else:
app.error(
'Could not find parcellation image (tested locations: ' +
str(parc_image_path) + ')')
else:
app.error(
'Could not find parcellation image (expected location: ' +
parc_image_path + ')')
if not os.path.isfile(parc_lut_file):
if app.args.atlas_path:
parc_lut_file = [
parc_lut_file,
os.path.join(os.path.dirname(app.args.atlas_path),
os.path.basename(parc_lut_file))
]
if os.path.isfile(parc_lut_file[1]):
parc_lut_file = parc_lut_file[1]
else:
app.error(
'Could not find parcellation lookup table file (tested locations: '
+ str(parc_lut_file) + ')')
else:
app.error(
'Could not find parcellation lookup table file (expected location: '
+ parc_lut_file + ')')
if not os.path.exists(mrtrix_lut_file):
app.error(
'Could not find MRtrix3 connectome lookup table file (expected location: '
+ mrtrix_lut_file + ')')
app.makeTempDir()
# Need to perform an initial import of JSON data using mrconvert; so let's grab the diffusion gradient table as well
# If no bvec/bval present, need to go down the directory listing
# Only try to import JSON file if it's actually present
# direction in the acquisition they'll need to be split across multiple files
# May need to concatenate more than one input DWI, since if there's more than one phase-encode direction
# in the acquired DWIs (i.e. not just those used for estimating the inhomogeneity field), they will
# need to be stored as separate NIfTI files in the 'dwi/' directory.
dwi_image_list = glob.glob(
os.path.join(bids_dir, label, 'dwi', label) + '*_dwi.nii*')
dwi_index = 1
for entry in dwi_image_list:
# os.path.split() falls over with .nii.gz extensions; only removes the .gz
prefix = entry.split(os.extsep)[0]
if os.path.isfile(prefix + '.bval') and os.path.isfile(prefix +
'.bvec'):
prefix = prefix + '.'
else:
prefix = os.path.join(bids_dir, 'dwi')
if not (os.path.isfile(prefix + 'bval')
and os.path.isfile(prefix + 'bvec')):
app.error(
'Unable to locate valid diffusion gradient table for image \''
+ entry + '\'')
grad_import_option = ' -fslgrad ' + prefix + 'bvec ' + prefix + 'bval'
json_path = prefix + 'json'
if os.path.isfile(json_path):
json_import_option = ' -json_import ' + json_path
else:
json_import_option = ''
run.command('mrconvert ' + entry + grad_import_option +
json_import_option + ' ' +
path.toTemp('dwi' + str(dwi_index) + '.mif', True))
dwi_index += 1
# Go hunting for reversed phase-encode data dedicated to field map estimation
fmap_image_list = []
fmap_dir = os.path.join(bids_dir, label, 'fmap')
fmap_index = 1
if os.path.isdir(fmap_dir):
if app.args.preprocessed:
app.error('fmap/ directory detected for subject \'' + label +
'\' despite use of ' + option_prefix +
'preprocessed option')
fmap_image_list = glob.glob(
os.path.join(fmap_dir, label) + '_dir-*_epi.nii*')
for entry in fmap_image_list:
prefix = entry.split(os.extsep)[0]
json_path = prefix + '.json'
with open(json_path, 'r') as f:
json_elements = json.load(f)
if 'IntendedFor' in json_elements and not any(
i.endswith(json_elements['IntendedFor'])
for i in dwi_image_list):
app.console('Image \'' + entry +
'\' is not intended for use with DWIs; skipping')
continue
if os.path.isfile(json_path):
json_import_option = ' -json_import ' + json_path
# fmap files will not come with any gradient encoding in the JSON;
# therefore we need to add it manually ourselves so that mrcat / mrconvert can
# appropriately handle the table once these images are concatenated with the DWIs
fmap_image_size = image.Header(entry).size()
fmap_image_num_volumes = 1 if len(
fmap_image_size) == 3 else fmap_image_size[3]
run.command('mrconvert ' + entry + json_import_option +
' -set_property dw_scheme \"' +
'\\n'.join(['0,0,1,0'] * fmap_image_num_volumes) +
'\" ' +
path.toTemp('fmap' + str(fmap_index) +
'.mif', True))
fmap_index += 1
else:
app.warn('No corresponding .json file found for image \'' +
entry + '\'; skipping')
fmap_image_list = [
'fmap' + str(index) + '.mif' for index in range(1, fmap_index)
]
# If there's no data in fmap/ directory, need to check to see if there's any phase-encoding
# contrast within the input DWI(s)
elif len(dwi_image_list) < 2 and not app.args.preprocessed:
app.error(
'Inadequate data for pre-processing of subject \'' + label +
'\': No phase-encoding contrast in input DWIs or fmap/ directory')
dwi_image_list = [
'dwi' + str(index) + '.mif' for index in range(1, dwi_index)
]
# Import anatomical image
run.command('mrconvert ' +
os.path.join(bids_dir, label, 'anat', label + '_T1w.nii.gz') +
' ' + path.toTemp('T1.mif', True))
cwd = os.getcwd()
app.gotoTempDir()
dwipreproc_se_epi = ''
dwipreproc_se_epi_option = ''
# For automated testing, down-sampled images are used. However, this invalidates the requirements of
# both MP-PCA denoising and Gibbs ringing removal. In addition, eddy can still take a long time
# despite the down-sampling. Therefore, provide images that have been pre-processed to the stage
# where it is still only DWI, JSON & bvecs/bvals that need to be provided.
if app.args.preprocessed:
if len(dwi_image_list) > 1:
app.error(
'If DWIs have been pre-processed, then only a single DWI file should need to be provided'
)
app.console(
'Skipping MP-PCA denoising, ' +
('Gibbs ringing removal, ' if unring_cmd else '') +
'distortion correction and bias field correction due to use of ' +
option_prefix + 'preprocessed option')
run.function(os.rename, dwi_image_list[0], 'dwi.mif')
else: # Do initial image pre-processing (denoising, Gibbs ringing removal if available, distortion correction & bias field correction) as normal
# Concatenate any SE EPI images with the DWIs before denoising (& unringing), then
# separate them again after the fact
dwidenoise_input = 'dwidenoise_input.mif'
fmap_num_volumes = 0
if fmap_image_list:
run.command('mrcat ' + ' '.join(fmap_image_list) +
' fmap_cat.mif -axis 3')
for i in fmap_image_list:
file.delTemporary(i)
fmap_num_volumes = image.Header('fmap_cat.mif').size()[3]
dwidenoise_input = 'all_cat.mif'
run.command('mrcat fmap_cat.mif ' + ' '.join(dwi_image_list) +
' ' + dwidenoise_input + ' -axis 3')
file.delTemporary('fmap_cat.mif')
else:
# Even if no explicit fmap images, may still need to concatenate multiple DWI inputs
if len(dwi_image_list) > 1:
run.command('mrcat ' + ' '.join(dwi_image_list) + ' ' +
dwidenoise_input + ' -axis 3')
else:
run.function(shutil.move, dwi_image_list[0], dwidenoise_input)
for i in dwi_image_list:
file.delTemporary(i)
# Step 1: Denoise
run.command('dwidenoise ' + dwidenoise_input + ' dwi_denoised.' +
('nii' if unring_cmd else 'mif'))
if unring_cmd:
run.command('mrinfo ' + dwidenoise_input +
' -json_keyval input.json')
file.delTemporary(dwidenoise_input)
# Step 2: Gibbs ringing removal (if available)
if unring_cmd:
run.command(unring_cmd + ' dwi_denoised.nii dwi_unring' +
fsl_suffix + ' -n 100')
file.delTemporary('dwi_denoised.nii')
unring_output_path = fsl.findImage('dwi_unring')
run.command('mrconvert ' + unring_output_path +
' dwi_unring.mif -json_import input.json')
file.delTemporary(unring_output_path)
file.delTemporary('input.json')
# If fmap images and DWIs have been concatenated, now is the time to split them back apart
dwipreproc_input = 'dwi_unring.mif' if unring_cmd else 'dwi_denoised.mif'
if fmap_num_volumes:
cat_input = 'dwi_unring.mif' if unring_cmd else 'dwi_denoised.mif'
dwipreproc_se_epi = 'se_epi.mif'
run.command('mrconvert ' + cat_input + ' ' + dwipreproc_se_epi +
' -coord 3 0:' + str(fmap_num_volumes - 1))
cat_num_volumes = image.Header(cat_input).size()[3]
run.command('mrconvert ' + cat_input +
' dwipreproc_in.mif -coord 3 ' +
str(fmap_num_volumes) + ':' + str(cat_num_volumes - 1))
file.delTemporary(dwipreproc_input)
dwipreproc_input = 'dwipreproc_in.mif'
dwipreproc_se_epi_option = ' -se_epi ' + dwipreproc_se_epi
# Step 3: Distortion correction
run.command('dwipreproc ' + dwipreproc_input +
' dwi_preprocessed.mif -rpe_header' +
dwipreproc_se_epi_option)
file.delTemporary(dwipreproc_input)
if dwipreproc_se_epi:
file.delTemporary(dwipreproc_se_epi)
# Step 4: Bias field correction
if dwibiascorrect_algo:
run.command('dwibiascorrect dwi_preprocessed.mif dwi.mif ' +
dwibiascorrect_algo)
file.delTemporary('dwi_preprocessed.mif')
else:
run.function(shutil.move, 'dwi_preprocessed.mif', 'dwi.mif')
# No longer branching based on whether or not -preprocessed was specified
# Step 5: Generate a brain mask for DWI
run.command('dwi2mask dwi.mif dwi_mask.mif')
# Step 6: Perform brain extraction on the T1 image in its original space
# (this is necessary for histogram matching prior to registration)
# Use fsl_anat script
run.command('mrconvert T1.mif T1.nii -stride -1,+2,+3')
run.command(fslanat_cmd + ' -i T1.nii --noseg --nosubcortseg')
run.command('mrconvert ' +
fsl.findImage('T1.anat' + os.sep + 'T1_biascorr_brain_mask') +
' T1_mask.mif -datatype bit')
run.command('mrconvert ' +
fsl.findImage('T1.anat' + os.sep + 'T1_biascorr_brain') +
' T1_biascorr_brain.mif')
file.delTemporary('T1.anat')
# Step 7: Generate target images for T1->DWI registration
run.command('dwiextract dwi.mif -bzero - | '
'mrcalc - 0.0 -max - | '
'mrmath - mean -axis 3 dwi_meanbzero.mif')
run.command(
'mrcalc 1 dwi_meanbzero.mif -div dwi_mask.mif -mult - | '
'mrhistmatch - T1_biascorr_brain.mif dwi_pseudoT1.mif -mask_input dwi_mask.mif -mask_target T1_mask.mif'
)
run.command(
'mrcalc 1 T1_biascorr_brain.mif -div T1_mask.mif -mult - | '
'mrhistmatch - dwi_meanbzero.mif T1_pseudobzero.mif -mask_input T1_mask.mif -mask_target dwi_mask.mif'
)
# Step 8: Perform T1->DWI registration
# Note that two registrations are performed: Even though we have a symmetric registration,
# generation of the two histogram-matched images means that you will get slightly different
# answers depending on which synthesized image & original image you use.
run.command(
'mrregister T1_biascorr_brain.mif dwi_pseudoT1.mif -type rigid -mask1 T1_mask.mif -mask2 dwi_mask.mif -rigid rigid_T1_to_pseudoT1.txt'
)
file.delTemporary('T1_biascorr_brain.mif')
run.command(
'mrregister T1_pseudobzero.mif dwi_meanbzero.mif -type rigid -mask1 T1_mask.mif -mask2 dwi_mask.mif -rigid rigid_pseudobzero_to_bzero.txt'
)
file.delTemporary('dwi_meanbzero.mif')
run.command(
'transformcalc rigid_T1_to_pseudoT1.txt rigid_pseudobzero_to_bzero.txt average rigid_T1_to_dwi.txt'
)
file.delTemporary('rigid_T1_to_pseudoT1.txt')
file.delTemporary('rigid_pseudobzero_to_bzero.txt')
run.command(
'mrtransform T1.mif T1_registered.mif -linear rigid_T1_to_dwi.txt')
file.delTemporary('T1.mif')
# Note: Since we're using a mask from fsl_anat (which crops the FoV), but using it as input to 5ttge fsl
# (which is receiving the raw T1), we need to resample in order to have the same dimensions between these two
run.command(
'mrtransform T1_mask.mif T1_mask_registered.mif -linear rigid_T1_to_dwi.txt -template T1_registered.mif -interp nearest'
)
file.delTemporary('T1_mask.mif')
# Step 9: Generate 5TT image for ACT
run.command(
'5ttgen fsl T1_registered.mif 5TT.mif -mask T1_mask_registered.mif')
file.delTemporary('T1_mask_registered.mif')
# Step 10: Estimate response functions for spherical deconvolution
run.command(
'dwi2response dhollander dwi.mif response_wm.txt response_gm.txt response_csf.txt -mask dwi_mask.mif'
)
# Step 11: Determine whether we are working with single-shell or multi-shell data
shells = [
int(round(float(value)))
for value in image.mrinfo('dwi.mif', 'shellvalues').strip().split()
]
multishell = (len(shells) > 2)
# Step 12: Perform spherical deconvolution
# Use a dilated mask for spherical deconvolution as a 'safety margin' -
# ACT should be responsible for stopping streamlines before they reach the edge of the DWI mask
run.command('maskfilter dwi_mask.mif dilate dwi_mask_dilated.mif -npass 3')
if multishell:
run.command(
'dwi2fod msmt_csd dwi.mif response_wm.txt FOD_WM.mif response_gm.txt FOD_GM.mif response_csf.txt FOD_CSF.mif '
'-mask dwi_mask_dilated.mif -lmax 10,0,0')
file.delTemporary('FOD_GM.mif')
file.delTemporary('FOD_CSF.mif')
else:
# Still use the msmt_csd algorithm with single-shell data: Use hard non-negativity constraint
# Also incorporate the CSF response to provide some fluid attenuation
run.command(
'dwi2fod msmt_csd dwi.mif response_wm.txt FOD_WM.mif response_csf.txt FOD_CSF.mif '
'-mask dwi_mask_dilated.mif -lmax 10,0')
file.delTemporary('FOD_CSF.mif')
# Step 13: Generate the grey matter parcellation
# The necessary steps here will vary significantly depending on the parcellation scheme selected
run.command(
'mrconvert T1_registered.mif T1_registered.nii -stride +1,+2,+3')
if app.args.parcellation == 'fs_2005' or app.args.parcellation == 'fs_2009':
# Run FreeSurfer pipeline on this subject's T1 image
run.command('recon-all -sd ' + app.tempDir +
' -subjid freesurfer -i T1_registered.nii')
run.command('recon-all -sd ' + app.tempDir +
' -subjid freesurfer -all')
# Grab the relevant parcellation image and target lookup table for conversion
parc_image_path = os.path.join('freesurfer', 'mri')
if app.args.parcellation == 'fs_2005':
parc_image_path = os.path.join(parc_image_path, 'aparc+aseg.mgz')
else:
parc_image_path = os.path.join(parc_image_path,
'aparc.a2009s+aseg.mgz')
# Perform the index conversion
run.command('labelconvert ' + parc_image_path + ' ' + parc_lut_file +
' ' + mrtrix_lut_file + ' parc_init.mif')
if app.cleanup:
run.function(shutil.rmtree, 'freesurfer')
# Fix the sub-cortical grey matter parcellations using FSL FIRST
run.command('labelsgmfix parc_init.mif T1_registered.mif ' +
mrtrix_lut_file + ' parc.mif')
file.delTemporary('parc_init.mif')
elif app.args.parcellation == 'aal' or app.args.parcellation == 'aal2':
# Can use MNI152 image provided with FSL for registration
run.command(flirt_cmd + ' -ref ' + mni152_path +
' -in T1_registered.nii -omat T1_to_MNI_FLIRT.mat -dof 12')
run.command('transformconvert T1_to_MNI_FLIRT.mat T1_registered.nii ' +
mni152_path + ' flirt_import T1_to_MNI_MRtrix.mat')
file.delTemporary('T1_to_MNI_FLIRT.mat')
run.command(
'transformcalc T1_to_MNI_MRtrix.mat invert MNI_to_T1_MRtrix.mat')
file.delTemporary('T1_to_MNI_MRtrix.mat')
run.command('mrtransform ' + parc_image_path +
' AAL.mif -linear MNI_to_T1_MRtrix.mat '
'-template T1_registered.mif -interp nearest')
file.delTemporary('MNI_to_T1_MRtrix.mat')
run.command('labelconvert AAL.mif ' + parc_lut_file + ' ' +
mrtrix_lut_file + ' parc.mif')
file.delTemporary('AAL.mif')
else:
app.error('Unknown parcellation scheme requested: ' +
app.args.parcellation)
file.delTemporary('T1_registered.nii')
# Step 14: Generate the tractogram
# If not manually specified, determine the appropriate number of streamlines based on the number of nodes in the parcellation:
# mean edge weight of 1,000 streamlines
# A smaller FOD amplitude threshold of 0.06 (default 0.1) is used for tracking due to the use of the msmt_csd
# algorithm, which imposes a hard rather than soft non-negativity constraint
num_nodes = int(image.statistic('parc.mif', 'max'))
num_streamlines = 1000 * num_nodes * num_nodes
if app.args.streamlines:
num_streamlines = app.args.streamlines
run.command(
'tckgen FOD_WM.mif tractogram.tck -act 5TT.mif -backtrack -crop_at_gmwmi -cutoff 0.06 -maxlength 250 -power 0.33 '
'-select ' + str(num_streamlines) + ' -seed_dynamic FOD_WM.mif')
# Step 15: Use SIFT2 to determine streamline weights
fd_scale_gm_option = ''
if not multishell:
fd_scale_gm_option = ' -fd_scale_gm'
run.command(
'tcksift2 tractogram.tck FOD_WM.mif weights.csv -act 5TT.mif -out_mu mu.txt'
+ fd_scale_gm_option)
# Step 16: Generate a TDI (to verify that SIFT2 has worked correctly)
with open('mu.txt', 'r') as f:
mu = float(f.read())
run.command(
'tckmap tractogram.tck -tck_weights_in weights.csv -template FOD_WM.mif -precise - | '
'mrcalc - ' + str(mu) + ' -mult tdi.mif')
# Step 17: Generate the connectome
# Only provide the standard density-weighted connectome for now
run.command(
'tck2connectome tractogram.tck parc.mif connectome.csv -tck_weights_in weights.csv'
)
file.delTemporary('weights.csv')
# Move necessary files to output directory
run.function(
shutil.copy, 'connectome.csv',
os.path.join(output_dir, 'connectome', label + '_connectome.csv'))
run.command('mrconvert dwi.mif ' +
os.path.join(output_dir, 'dwi', label + '_dwi.nii.gz') +
' -export_grad_fsl ' +
os.path.join(output_dir, 'dwi', label + '_dwi.bvec') + ' ' +
os.path.join(output_dir, 'dwi', label + '_dwi.bval') +
' -json_export ' +
os.path.join(output_dir, 'dwi', label + '_dwi.json'))
run.command('mrconvert tdi.mif ' +
os.path.join(output_dir, 'dwi', label + '_tdi.nii.gz'))
run.function(shutil.copy, 'mu.txt',
os.path.join(output_dir, 'connectome', label + '_mu.txt'))
run.function(shutil.copy, 'response_wm.txt',
os.path.join(output_dir, 'dwi', label + '_response.txt'))
# Manually wipe and zero the temp directory (since we might be processing more than one subject)
os.chdir(cwd)
if app.cleanup:
app.console('Deleting temporary directory ' + app.tempDir)
# Can't use run.function() here; it'll try to write to the log file that resides in the temp directory just deleted
shutil.rmtree(app.tempDir)
else:
app.console('Contents of temporary directory kept, location: ' +
app.tempDir)
app.tempDir = ''
def execute(): #pylint: disable=unused-variable
import math, os
from distutils.spawn import find_executable
from mrtrix3 import app, fsl, image, MRtrixError, path, run, utils
if utils.is_windows():
raise MRtrixError(
'\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows'
)
fsl_path = os.environ.get('FSLDIR', '')
if not fsl_path:
raise MRtrixError(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
bet_cmd = fsl.exe_name('bet')
fast_cmd = fsl.exe_name('fast')
first_cmd = fsl.exe_name('run_first_all')
ssroi_cmd = fsl.exe_name('standard_space_roi')
first_atlas_path = os.path.join(fsl_path, 'data', 'first',
'models_336_bin')
if not os.path.isdir(first_atlas_path):
raise MRtrixError(
'Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager'
)
fsl_suffix = fsl.suffix()
sgm_structures = [
'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta',
'R_Puta', 'L_Thal', 'R_Thal'
]
if app.ARGS.sgm_amyg_hipp:
sgm_structures.extend(['L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp'])
t1_spacing = image.Header('input.mif').spacing()
upsample_for_first = False
# If voxel size is 1.25mm or larger, make a guess that the user has erroneously re-gridded their data
if math.pow(t1_spacing[0] * t1_spacing[1] * t1_spacing[2],
1.0 / 3.0) > 1.225:
app.warn(
'Voxel size larger than expected for T1-weighted images (' +
str(t1_spacing) + '); '
'note that ACT does not require re-gridding of T1 image to DWI space, and indeed '
'retaining the original higher resolution of the T1 image is preferable'
)
upsample_for_first = True
run.command('mrconvert input.mif T1.nii -strides -1,+2,+3')
fast_t1_input = 'T1.nii'
fast_t2_input = ''
# Decide whether or not we're going to do any brain masking
if os.path.exists('mask.mif'):
fast_t1_input = 'T1_masked' + fsl_suffix
# Check to see if the mask matches the T1 image
if image.match('T1.nii', 'mask.mif'):
run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input)
mask_path = 'mask.mif'
else:
app.warn('Mask image does not match input image - re-gridding')
run.command(
'mrtransform mask.mif mask_regrid.mif -template T1.nii -datatype bit'
)
run.command('mrcalc T1.nii mask_regrid.mif -mult ' + fast_t1_input)
mask_path = 'mask_regrid.mif'
if os.path.exists('T2.nii'):
fast_t2_input = 'T2_masked' + fsl_suffix
run.command('mrcalc T2.nii ' + mask_path + ' -mult ' +
fast_t2_input)
elif app.ARGS.premasked:
fast_t1_input = 'T1.nii'
if os.path.exists('T2.nii'):
fast_t2_input = 'T2.nii'
else:
# Use FSL command standard_space_roi to do an initial masking of the image before BET
# Also reduce the FoV of the image
# Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation
mni_mask_path = os.path.join(fsl_path, 'data', 'standard',
'MNI152_T1_1mm_brain_mask_dil.nii.gz')
mni_mask_dilation = 0
if os.path.exists(mni_mask_path):
mni_mask_dilation = 4
else:
mni_mask_path = os.path.join(
fsl_path, 'data', 'standard',
'MNI152_T1_2mm_brain_mask_dil.nii.gz')
if os.path.exists(mni_mask_path):
mni_mask_dilation = 2
try:
if mni_mask_dilation:
run.command('maskfilter ' + mni_mask_path +
' dilate mni_mask.nii -npass ' +
str(mni_mask_dilation))
if app.ARGS.nocrop:
ssroi_roi_option = ' -roiNONE'
else:
ssroi_roi_option = ' -roiFOV'
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix +
' -maskMASK mni_mask.nii' + ssroi_roi_option)
else:
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix +
' -b')
except run.MRtrixCmdError:
pass
try:
pre_bet_image = fsl.find_image('T1_preBET')
except MRtrixError:
app.warn('FSL script \'standard_space_roi\' did not complete successfully' + \
('' if find_executable('dc') else ' (possibly due to program \'dc\' not being installed') + '; ' + \
'attempting to continue by providing un-cropped image to BET')
pre_bet_image = 'T1.nii'
# BET
run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix +
' -f 0.15 -R')
fast_t1_input = fsl.find_image('T1_BET' + fsl_suffix)
if os.path.exists('T2.nii'):
if app.ARGS.nocrop:
fast_t2_input = 'T2.nii'
else:
# Just a reduction of FoV, no sub-voxel interpolation going on
run.command('mrtransform T2.nii T2_cropped.nii -template ' +
fast_t1_input + ' -interp nearest')
fast_t2_input = 'T2_cropped.nii'
# Finish branching based on brain masking
# FAST
if fast_t2_input:
run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input)
else:
run.command(fast_cmd + ' ' + fast_t1_input)
# FIRST
first_input = 'T1.nii'
if upsample_for_first:
app.warn(
'Generating 1mm isotropic T1 image for FIRST in hope of preventing failure, since input image is of lower resolution'
)
run.command('mrgrid T1.nii regrid T1_1mm.nii -voxel 1.0 -interp sinc')
first_input = 'T1_1mm.nii'
first_brain_extracted_option = ''
if app.ARGS.premasked:
first_brain_extracted_option = ' -b'
first_debug_option = ''
if not app.DO_CLEANUP:
first_debug_option = ' -d'
first_verbosity_option = ''
if app.VERBOSITY == 3:
first_verbosity_option = ' -v'
run.command(first_cmd + ' -m none -s ' + ','.join(sgm_structures) +
' -i ' + first_input + ' -o first' +
first_brain_extracted_option + first_debug_option +
first_verbosity_option)
fsl.check_first('first', sgm_structures)
# Convert FIRST meshes to partial volume images
pve_image_list = []
progress = app.ProgressBar(
'Generating partial volume images for SGM structures',
len(sgm_structures))
for struct in sgm_structures:
pve_image_path = 'mesh2voxel_' + struct + '.mif'
vtk_in_path = 'first-' + struct + '_first.vtk'
vtk_temp_path = struct + '.vtk'
run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path +
' -transform first2real ' + first_input)
run.command('mesh2voxel ' + vtk_temp_path + ' ' + fast_t1_input + ' ' +
pve_image_path)
pve_image_list.append(pve_image_path)
progress.increment()
progress.done()
run.command(['mrmath', pve_image_list, 'sum', '-', '|', \
'mrcalc', '-', '1.0', '-min', 'all_sgms.mif'])
# Combine the tissue images into the 5TT format within the script itself
fast_output_prefix = fast_t1_input.split('.')[0]
fast_csf_output = fsl.find_image(fast_output_prefix + '_pve_0')
fast_gm_output = fsl.find_image(fast_output_prefix + '_pve_1')
fast_wm_output = fsl.find_image(fast_output_prefix + '_pve_2')
# Step 1: Run LCC on the WM image
run.command(
'mrthreshold ' + fast_wm_output +
' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit'
)
# Step 2: Generate the images in the same fashion as the old 5ttgen binary used to:
# - Preserve CSF as-is
# - Preserve SGM, unless it results in a sum of volume fractions greater than 1, in which case clamp
# - Multiply the FAST volume fractions of GM and CSF, so that the sum of CSF, SGM, CGM and WM is 1.0
run.command('mrcalc ' + fast_csf_output +
' remove_unconnected_wm_mask.mif -mult csf.mif')
run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif')
run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_gm_output +
' ' + fast_wm_output + ' -add -div multiplier.mif')
run.command(
'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif'
)
run.command(
'mrcalc ' + fast_gm_output +
' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif'
)
run.command(
'mrcalc ' + fast_wm_output +
' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif'
)
run.command('mrcalc 0 wm.mif -min path.mif')
run.command(
'mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -strides +2,+3,+4,+1'
)
# Crop to reduce file size (improves caching of image data during tracking)
if app.ARGS.nocrop:
run.function(os.rename, 'combined_precrop.mif', 'result.mif')
else:
run.command(
'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrgrid combined_precrop.mif crop result.mif -mask -'
)
run.command('mrconvert result.mif ' + path.from_user(app.ARGS.output),
mrconvert_keyval=path.from_user(app.ARGS.input),
force=app.FORCE_OVERWRITE)
def execute(): #pylint: disable=unused-variable
subject_dir = os.path.abspath(path.from_user(app.ARGS.input, False))
if not os.path.isdir(subject_dir):
raise MRtrixError('Input to hsvs algorithm must be a directory')
surf_dir = os.path.join(subject_dir, 'surf')
mri_dir = os.path.join(subject_dir, 'mri')
check_dir(surf_dir)
check_dir(mri_dir)
#aparc_image = os.path.join(mri_dir, 'aparc+aseg.mgz')
aparc_image = 'aparc.mif'
mask_image = os.path.join(mri_dir, 'brainmask.mgz')
reg_file = os.path.join(mri_dir, 'transforms', 'talairach.xfm')
check_file(aparc_image)
check_file(mask_image)
check_file(reg_file)
template_image = 'template.mif' if app.ARGS.template else aparc_image
have_first = False
have_fast = False
fsl_path = os.environ.get('FSLDIR', '')
if fsl_path:
# Use brain-extracted, bias-corrected image for FSL tools
norm_image = os.path.join(mri_dir, 'norm.mgz')
check_file(norm_image)
run.command('mrconvert ' + norm_image + ' T1.nii -stride -1,+2,+3')
# Verify FAST availability
try:
fast_cmd = fsl.exe_name('fast')
except MRtrixError:
fast_cmd = None
if fast_cmd:
have_fast = True
if fast_cmd == 'fast':
fast_suffix = fsl.suffix()
else:
fast_suffix = '.nii.gz'
else:
app.warn('Could not find FSL program fast; script will not use fast for cerebellar tissue segmentation')
# Verify FIRST availability
try:
first_cmd = fsl.exe_name('run_first_all')
except MRtrixError:
first_cmd = None
first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin')
have_first = first_cmd and os.path.isdir(first_atlas_path)
else:
app.warn('Environment variable FSLDIR is not set; script will run without FSL components')
acpc_string = 'anterior ' + ('& posterior commissures' if ATTEMPT_PC else 'commissure')
have_acpcdetect = bool(find_executable('acpcdetect')) and 'ARTHOME' in os.environ
if have_acpcdetect:
if have_fast:
app.console('ACPCdetect and FSL FAST will be used for explicit segmentation of ' + acpc_string)
else:
app.warn('ACPCdetect is installed, but FSL FAST not found; cannot segment ' + acpc_string)
have_acpcdetect = False
else:
app.warn('ACPCdetect not installed; cannot segment ' + acpc_string)
# Need to perform a better search for hippocampal subfield output: names & version numbers may change
have_hipp_subfields = False
hipp_subfield_has_amyg = False
# Could result in multiple matches
hipp_subfield_regex = re.compile(r'^[lr]h\.hippo[a-zA-Z]*Labels-[a-zA-Z0-9]*\.v[0-9]+\.?[a-zA-Z0-9]*\.mg[hz]$')
hipp_subfield_all_images = sorted(list(filter(hipp_subfield_regex.match, os.listdir(mri_dir))))
# Remove any images that provide segmentations in FreeSurfer voxel space; we want the high-resolution versions
hipp_subfield_all_images = [ item for item in hipp_subfield_all_images if 'FSvoxelSpace' not in item ]
# Arrange the images into lr pairs
hipp_subfield_paired_images = [ ]
for lh_filename in [ item for item in hipp_subfield_all_images if item[0] == 'l' ]:
if 'r' + lh_filename[1:] in hipp_subfield_all_images:
hipp_subfield_paired_images.append(lh_filename[1:])
# Choose which of these image pairs we are going to use
for code in [ '.CA.', '.FS60.' ]:
if any(code in filename for filename in hipp_subfield_paired_images):
hipp_subfield_image_suffix = [ filename for filename in hipp_subfield_paired_images if code in filename ][0]
have_hipp_subfields = True
break
# Choose the pair with the shortest filename string if we have no other criteria
if not have_hipp_subfields and hipp_subfield_paired_images:
hipp_subfield_paired_images = sorted(hipp_subfield_paired_images, key=len)
if hipp_subfield_paired_images:
hipp_subfield_image_suffix = hipp_subfield_paired_images[0]
have_hipp_subfields = True
if have_hipp_subfields:
hipp_subfield_has_amyg = 'Amyg' in hipp_subfield_image_suffix
# Perform a similar search for thalamic nuclei submodule output
thal_nuclei_image = None
thal_nuclei_regex = re.compile(r'^ThalamicNuclei\.v[0-9]+\.?[a-zA-Z0-9]*.mg[hz]$')
thal_nuclei_all_images = sorted(list(filter(thal_nuclei_regex.match, os.listdir(mri_dir))))
thal_nuclei_all_images = [ item for item in thal_nuclei_all_images if 'FSvoxelSpace' not in item ]
if thal_nuclei_all_images:
if len(thal_nuclei_all_images) == 1:
thal_nuclei_image = thal_nuclei_all_images[0]
else:
# How to choose which version to use?
# Start with software version
thal_nuclei_versions = [ int(item.split('.')[1].lstrip('v')) for item in thal_nuclei_all_images ]
thal_nuclei_all_images = [ filepath for filepath, version_number in zip(thal_nuclei_all_images, thal_nuclei_versions) if version_number == max(thal_nuclei_versions) ]
if len(thal_nuclei_all_images) == 1:
thal_nuclei_image = thal_nuclei_all_images[0]
else:
# Revert to filename length
thal_nuclei_all_images = sorted(thal_nuclei_all_images, key=len)
thal_nuclei_image = thal_nuclei_all_images[0]
# If particular hippocampal segmentation method is requested, make sure we can perform such;
# if not, decide how to segment hippocampus based on what's available
hippocampi_method = app.ARGS.hippocampi
if hippocampi_method:
if hippocampi_method == 'subfields':
if not have_hipp_subfields:
raise MRtrixError('Could not isolate hippocampal subfields module output (candidate images: ' + str(hipp_subfield_all_images) + ')')
elif hippocampi_method == 'first':
if not have_first:
raise MRtrixError('Cannot use "first" method for hippocampi segmentation; check FSL installation')
else:
if have_hipp_subfields:
hippocampi_method = 'subfields'
app.console('Hippocampal subfields module output detected; will utilise for hippocampi '
+ ('and amygdalae ' if hipp_subfield_has_amyg else '')
+ 'segmentation')
elif have_first:
hippocampi_method = 'first'
app.console('No hippocampal subfields module output detected, but FSL FIRST is installed; '
'will utilise latter for hippocampi segmentation')
else:
hippocampi_method = 'aseg'
app.console('Neither hippocampal subfields module output nor FSL FIRST detected; '
'FreeSurfer aseg will be used for hippocampi segmentation')
if hippocampi_method == 'subfields':
if 'FREESURFER_HOME' not in os.environ:
raise MRtrixError('FREESURFER_HOME environment variable not set; required for use of hippocampal subfields module')
freesurfer_lut_file = os.path.join(os.environ['FREESURFER_HOME'], 'FreeSurferColorLUT.txt')
check_file(freesurfer_lut_file)
hipp_lut_file = os.path.join(path.shared_data_path(), path.script_subdir_name(), 'hsvs', 'HippSubfields.txt')
check_file(hipp_lut_file)
if hipp_subfield_has_amyg:
amyg_lut_file = os.path.join(path.shared_data_path(), path.script_subdir_name(), 'hsvs', 'AmygSubfields.txt')
check_file(amyg_lut_file)
if app.ARGS.sgm_amyg_hipp:
app.warn('Option -sgm_amyg_hipp ignored '
'(hsvs algorithm always assigns hippocampi & ampygdalae as sub-cortical grey matter)')
# Similar logic for thalami
thalami_method = app.ARGS.thalami
if thalami_method:
if thalami_method == 'nuclei':
if not thal_nuclei_image:
raise MRtrixError('Could not find thalamic nuclei module output')
elif thalami_method == 'first':
if not have_first:
raise MRtrixError('Cannot use "first" method for thalami segmentation; check FSL installation')
else:
# Not happy with outputs of thalamic nuclei submodule; default to FIRST
if have_first:
thalami_method = 'first'
if thal_nuclei_image:
app.console('Thalamic nuclei submodule output ignored in favour of FSL FIRST '
'(can override using -thalami option)')
else:
app.console('Will utilise FSL FIRST for thalami segmentation')
elif thal_nuclei_image:
thalami_method = 'nuclei'
app.console('Will utilise detected thalamic nuclei submodule output')
else:
thalami_method = 'aseg'
app.console('Neither thalamic nuclei module output nor FSL FIRST detected; '
'FreeSurfer aseg will be used for thalami segmentation')
###########################
# Commencing segmentation #
###########################
tissue_images = [ [ 'lh.pial.mif', 'rh.pial.mif' ],
[],
[ 'lh.white.mif', 'rh.white.mif' ],
[],
[] ]
# Get the main cerebrum segments; these are already smooth
progress = app.ProgressBar('Mapping FreeSurfer cortical reconstruction to partial volume images', 8)
for hemi in [ 'lh', 'rh' ]:
for basename in [ hemi+'.white', hemi+'.pial' ]:
filepath = os.path.join(surf_dir, basename)
check_file(filepath)
transformed_path = basename + '_realspace.obj'
run.command('meshconvert ' + filepath + ' ' + transformed_path + ' -binary -transform fs2real ' + aparc_image)
progress.increment()
run.command('mesh2voxel ' + transformed_path + ' ' + template_image + ' ' + basename + '.mif')
app.cleanup(transformed_path)
progress.increment()
progress.done()
# Get other structures that need to be converted from the aseg voxel image
from_aseg = list(ASEG_STRUCTURES)
if hippocampi_method == 'subfields':
if not hipp_subfield_has_amyg and not have_first:
from_aseg.extend(AMYG_ASEG)
elif hippocampi_method == 'aseg':
from_aseg.extend(HIPP_ASEG)
from_aseg.extend(AMYG_ASEG)
if thalami_method == 'aseg':
from_aseg.extend(THAL_ASEG)
if not have_first:
from_aseg.extend(OTHER_SGM_ASEG)
progress = app.ProgressBar('Smoothing non-cortical structures segmented by FreeSurfer', len(from_aseg) + 2)
for (index, tissue, name) in from_aseg:
init_mesh_path = name + '_init.vtk'
smoothed_mesh_path = name + '.vtk'
run.command('mrcalc ' + aparc_image + ' ' + str(index) + ' -eq - | voxel2mesh - -threshold 0.5 ' + init_mesh_path)
run.command('meshfilter ' + init_mesh_path + ' smooth ' + smoothed_mesh_path)
app.cleanup(init_mesh_path)
run.command('mesh2voxel ' + smoothed_mesh_path + ' ' + template_image + ' ' + name + '.mif')
app.cleanup(smoothed_mesh_path)
tissue_images[tissue-1].append(name + '.mif')
progress.increment()
# Lateral ventricles are separate as we want to combine with choroid plexus prior to mesh conversion
for hemi_index, hemi_name in enumerate(['Left', 'Right']):
name = hemi_name + '_LatVent_ChorPlex'
init_mesh_path = name + '_init.vtk'
smoothed_mesh_path = name + '.vtk'
run.command('mrcalc ' + ' '.join(aparc_image + ' ' + str(index) + ' -eq' for index, tissue, name in VENTRICLE_CP_ASEG[hemi_index]) + ' -add - | '
+ 'voxel2mesh - -threshold 0.5 ' + init_mesh_path)
run.command('meshfilter ' + init_mesh_path + ' smooth ' + smoothed_mesh_path)
app.cleanup(init_mesh_path)
run.command('mesh2voxel ' + smoothed_mesh_path + ' ' + template_image + ' ' + name + '.mif')
app.cleanup(smoothed_mesh_path)
tissue_images[3].append(name + '.mif')
progress.increment()
progress.done()
# Combine corpus callosum segments before smoothing
progress = app.ProgressBar('Combining and smoothing corpus callosum segmentation', len(CORPUS_CALLOSUM_ASEG) + 3)
for (index, name) in CORPUS_CALLOSUM_ASEG:
run.command('mrcalc ' + aparc_image + ' ' + str(index) + ' -eq ' + name + '.mif -datatype bit')
progress.increment()
cc_init_mesh_path = 'combined_corpus_callosum_init.vtk'
cc_smoothed_mesh_path = 'combined_corpus_callosum.vtk'
run.command('mrmath ' + ' '.join([ name + '.mif' for (index, name) in CORPUS_CALLOSUM_ASEG ]) + ' sum - | voxel2mesh - -threshold 0.5 ' + cc_init_mesh_path)
for name in [ n for _, n in CORPUS_CALLOSUM_ASEG ]:
app.cleanup(name + '.mif')
progress.increment()
run.command('meshfilter ' + cc_init_mesh_path + ' smooth ' + cc_smoothed_mesh_path)
app.cleanup(cc_init_mesh_path)
progress.increment()
run.command('mesh2voxel ' + cc_smoothed_mesh_path + ' ' + template_image + ' combined_corpus_callosum.mif')
app.cleanup(cc_smoothed_mesh_path)
progress.done()
tissue_images[2].append('combined_corpus_callosum.mif')
# Deal with brain stem, including determining those voxels that should
# be erased from the 5TT image in order for streamlines traversing down
# the spinal column to be terminated & accepted
bs_fullmask_path = 'brain_stem_init.mif'
bs_cropmask_path = ''
progress = app.ProgressBar('Segmenting and cropping brain stem', 5)
run.command('mrcalc ' + aparc_image + ' ' + str(BRAIN_STEM_ASEG[0][0]) + ' -eq '
+ ' -add '.join([ aparc_image + ' ' + str(index) + ' -eq' for index, name in BRAIN_STEM_ASEG[1:] ]) + ' -add '
+ bs_fullmask_path + ' -datatype bit')
progress.increment()
bs_init_mesh_path = 'brain_stem_init.vtk'
run.command('voxel2mesh ' + bs_fullmask_path + ' ' + bs_init_mesh_path)
progress.increment()
bs_smoothed_mesh_path = 'brain_stem.vtk'
run.command('meshfilter ' + bs_init_mesh_path + ' smooth ' + bs_smoothed_mesh_path)
app.cleanup(bs_init_mesh_path)
progress.increment()
run.command('mesh2voxel ' + bs_smoothed_mesh_path + ' ' + template_image + ' brain_stem.mif')
app.cleanup(bs_smoothed_mesh_path)
progress.increment()
fourthventricle_zmin = min([ int(line.split()[2]) for line in run.command('maskdump 4th-Ventricle.mif')[0].splitlines() ])
if fourthventricle_zmin:
bs_cropmask_path = 'brain_stem_crop.mif'
run.command('mredit brain_stem.mif - ' + ' '.join([ '-plane 2 ' + str(index) + ' 0' for index in range(0, fourthventricle_zmin) ]) + ' | '
'mrcalc brain_stem.mif - -sub 1e-6 -gt ' + bs_cropmask_path + ' -datatype bit')
app.cleanup(bs_fullmask_path)
progress.done()
if hippocampi_method == 'subfields':
progress = app.ProgressBar('Using detected FreeSurfer hippocampal subfields module output',
64 if hipp_subfield_has_amyg else 32)
subfields = [ ( hipp_lut_file, 'hipp' ) ]
if hipp_subfield_has_amyg:
subfields.append(( amyg_lut_file, 'amyg' ))
for subfields_lut_file, structure_name in subfields:
for hemi, filename in zip([ 'Left', 'Right'], [ prefix + hipp_subfield_image_suffix for prefix in [ 'l', 'r' ] ]):
# Extract individual components from image and assign to different tissues
subfields_all_tissues_image = hemi + '_' + structure_name + '_subfields.mif'
run.command('labelconvert ' + os.path.join(mri_dir, filename) + ' ' + freesurfer_lut_file + ' ' + subfields_lut_file + ' ' + subfields_all_tissues_image)
progress.increment()
for tissue in range(0, 5):
init_mesh_path = hemi + '_' + structure_name + '_subfield_' + str(tissue) + '_init.vtk'
smooth_mesh_path = hemi + '_' + structure_name + '_subfield_' + str(tissue) + '.vtk'
subfield_tissue_image = hemi + '_' + structure_name + '_subfield_' + str(tissue) + '.mif'
run.command('mrcalc ' + subfields_all_tissues_image + ' ' + str(tissue+1) + ' -eq - | ' + \
'voxel2mesh - ' + init_mesh_path)
progress.increment()
# Since the hippocampal subfields segmentation can include some fine structures, reduce the extent of smoothing
run.command('meshfilter ' + init_mesh_path + ' smooth ' + smooth_mesh_path + ' -smooth_spatial 2 -smooth_influence 2')
app.cleanup(init_mesh_path)
progress.increment()
run.command('mesh2voxel ' + smooth_mesh_path + ' ' + template_image + ' ' + subfield_tissue_image)
app.cleanup(smooth_mesh_path)
progress.increment()
tissue_images[tissue].append(subfield_tissue_image)
app.cleanup(subfields_all_tissues_image)
progress.done()
if thalami_method == 'nuclei':
progress = app.ProgressBar('Using detected FreeSurfer thalamic nuclei module output', 6)
for hemi in ['Left', 'Right']:
thal_mask_path = hemi + '_Thalamus_mask.mif'
init_mesh_path = hemi + '_Thalamus_init.vtk'
smooth_mesh_path = hemi + '_Thalamus.vtk'
thalamus_image = hemi + '_Thalamus.mif'
if hemi == 'Right':
run.command('mrthreshold ' + os.path.join(mri_dir, thal_nuclei_image) + ' -abs 8200 ' + thal_mask_path)
else:
run.command('mrcalc ' + os.path.join(mri_dir, thal_nuclei_image) + ' 0 -gt '
+ os.path.join(mri_dir, thal_nuclei_image) + ' 8200 -lt '
+ '-mult ' + thal_mask_path)
run.command('voxel2mesh ' + thal_mask_path + ' ' + init_mesh_path)
app.cleanup(thal_mask_path)
progress.increment()
run.command('meshfilter ' + init_mesh_path + ' smooth ' + smooth_mesh_path + ' -smooth_spatial 2 -smooth_influence 2')
app.cleanup(init_mesh_path)
progress.increment()
run.command('mesh2voxel ' + smooth_mesh_path + ' ' + template_image + ' ' + thalamus_image)
app.cleanup(smooth_mesh_path)
progress.increment()
tissue_images[1].append(thalamus_image)
progress.done()
if have_first:
app.console('Running FSL FIRST to segment sub-cortical grey matter structures')
from_first = SGM_FIRST_MAP.copy()
if hippocampi_method == 'subfields':
from_first = { key: value for key, value in from_first.items() if 'Hippocampus' not in value }
if hipp_subfield_has_amyg:
from_first = { key: value for key, value in from_first.items() if 'Amygdala' not in value }
elif hippocampi_method == 'aseg':
from_first = { key: value for key, value in from_first.items() if 'Hippocampus' not in value and 'Amygdala' not in value }
if thalami_method != 'first':
from_first = { key: value for key, value in from_first.items() if 'Thalamus' not in value }
run.command(first_cmd + ' -s ' + ','.join(from_first.keys()) + ' -i T1.nii -b -o first')
fsl.check_first('first', from_first.keys())
app.cleanup(glob.glob('T1_to_std_sub.*'))
progress = app.ProgressBar('Mapping FIRST segmentations to image', 2*len(from_first))
for key, value in from_first.items():
vtk_in_path = 'first-' + key + '_first.vtk'
vtk_converted_path = 'first-' + key + '_transformed.vtk'
run.command('meshconvert ' + vtk_in_path + ' ' + vtk_converted_path + ' -transform first2real T1.nii')
app.cleanup(vtk_in_path)
progress.increment()
run.command('mesh2voxel ' + vtk_converted_path + ' ' + template_image + ' ' + value + '.mif')
app.cleanup(vtk_converted_path)
tissue_images[1].append(value + '.mif')
progress.increment()
if not have_fast:
app.cleanup('T1.nii')
app.cleanup(glob.glob('first*'))
progress.done()
# Run ACPCdetect, use results to draw spherical ROIs on T1 that will be fed to FSL FAST,
# the WM components of which will then be added to the 5TT
if have_acpcdetect:
progress = app.ProgressBar('Using ACPCdetect and FAST to segment ' + acpc_string, 5)
# ACPCdetect requires input image to be 16-bit
# We also want to realign to RAS beforehand so that we can interpret the output voxel locations properly
acpcdetect_input_image = 'T1RAS_16b.nii'
run.command('mrconvert ' + norm_image + ' -datatype uint16 -stride +1,+2,+3 ' + acpcdetect_input_image)
progress.increment()
run.command('acpcdetect -i ' + acpcdetect_input_image)
progress.increment()
# We need the header in order to go from voxel coordinates to scanner coordinates
acpcdetect_input_header = image.Header(acpcdetect_input_image)
acpcdetect_output_path = os.path.splitext(acpcdetect_input_image)[0] + '_ACPC.txt'
app.cleanup(acpcdetect_input_image)
with open(acpcdetect_output_path, 'r') as acpc_file:
acpcdetect_output_data = acpc_file.read().splitlines()
app.cleanup(glob.glob(os.path.splitext(acpcdetect_input_image)[0] + "*"))
# Need to scan through the contents of this file,
# isolating the AC and PC locations
ac_voxel = pc_voxel = None
for index, line in enumerate(acpcdetect_output_data):
if 'AC' in line and 'voxel location' in line:
ac_voxel = [float(item) for item in acpcdetect_output_data[index+1].strip().split()]
elif 'PC' in line and 'voxel location' in line:
pc_voxel = [float(item) for item in acpcdetect_output_data[index+1].strip().split()]
if not ac_voxel or not pc_voxel:
raise MRtrixError('Error parsing text file from "acpcdetect"')
def voxel2scanner(voxel, header):
return [ voxel[0]*header.spacing()[0]*header.transform()[axis][0]
+ voxel[1]*header.spacing()[1]*header.transform()[axis][1]
+ voxel[2]*header.spacing()[2]*header.transform()[axis][2]
+ header.transform()[axis][3]
for axis in range(0,3) ]
ac_scanner = voxel2scanner(ac_voxel, acpcdetect_input_header)
pc_scanner = voxel2scanner(pc_voxel, acpcdetect_input_header)
# Generate the mask image within which FAST will be run
acpc_prefix = 'ACPC' if ATTEMPT_PC else 'AC'
acpc_mask_image = acpc_prefix + '_FAST_mask.mif'
run.command('mrcalc ' + template_image + ' nan -eq - | '
'mredit - ' + acpc_mask_image + ' -scanner '
'-sphere ' + ','.join(str(value) for value in ac_scanner) + ' 8 1 '
+ ('-sphere ' + ','.join(str(value) for value in pc_scanner) + ' 5 1' if ATTEMPT_PC else ''))
progress.increment()
acpc_t1_masked_image = acpc_prefix + '_T1.nii'
run.command('mrtransform ' + norm_image + ' -template ' + template_image + ' - | '
'mrcalc - ' + acpc_mask_image + ' -mult ' + acpc_t1_masked_image)
app.cleanup(acpc_mask_image)
progress.increment()
run.command(fast_cmd + ' -N ' + acpc_t1_masked_image)
app.cleanup(acpc_t1_masked_image)
progress.increment()
# Ideally don't want to have to add these manually; instead add all outputs from FAST
# to the 5TT (both cerebellum and AC / PC) in a single go
# This should involve grabbing just the WM component of these images
# Actually, in retrospect, it may be preferable to do the AC PC segmentation
# earlier on, and simply add them to the list of WM structures
acpc_wm_image = acpc_prefix + '.mif'
run.command('mrconvert ' + fsl.find_image(acpc_prefix + '_T1_pve_2') + ' ' + acpc_wm_image)
tissue_images[2].append(acpc_wm_image)
app.cleanup(glob.glob(os.path.splitext(acpc_t1_masked_image)[0] + '*'))
progress.done()
# If we don't have FAST, do cerebellar segmentation in a comparable way to the cortical GM / WM:
# Generate one 'pial-like' surface containing the GM and WM of the cerebellum,
# and another with just the WM
if not have_fast:
progress = app.ProgressBar('Adding FreeSurfer cerebellar segmentations directly', 6)
for hemi in [ 'Left-', 'Right-' ]:
wm_index = [ index for index, tissue, name in CEREBELLUM_ASEG if name.startswith(hemi) and 'White' in name ][0]
gm_index = [ index for index, tissue, name in CEREBELLUM_ASEG if name.startswith(hemi) and 'Cortex' in name ][0]
run.command('mrcalc ' + aparc_image + ' ' + str(wm_index) + ' -eq ' + aparc_image + ' ' + str(gm_index) + ' -eq -add - | ' + \
'voxel2mesh - ' + hemi + 'cerebellum_all_init.vtk')
progress.increment()
run.command('mrcalc ' + aparc_image + ' ' + str(gm_index) + ' -eq - | ' + \
'voxel2mesh - ' + hemi + 'cerebellum_grey_init.vtk')
progress.increment()
for name, tissue in { 'all':2, 'grey':1 }.items():
run.command('meshfilter ' + hemi + 'cerebellum_' + name + '_init.vtk smooth ' + hemi + 'cerebellum_' + name + '.vtk')
app.cleanup(hemi + 'cerebellum_' + name + '_init.vtk')
progress.increment()
run.command('mesh2voxel ' + hemi + 'cerebellum_' + name + '.vtk ' + template_image + ' ' + hemi + 'cerebellum_' + name + '.mif')
app.cleanup(hemi + 'cerebellum_' + name + '.vtk')
progress.increment()
tissue_images[tissue].append(hemi + 'cerebellum_' + name + '.mif')
progress.done()
# Construct images with the partial volume of each tissue
progress = app.ProgressBar('Combining segmentations of all structures corresponding to each tissue type', 5)
for tissue in range(0,5):
run.command('mrmath ' + ' '.join(tissue_images[tissue]) + (' brain_stem.mif' if tissue == 2 else '') + ' sum - | mrcalc - 1.0 -min tissue' + str(tissue) + '_init.mif')
app.cleanup(tissue_images[tissue])
progress.increment()
progress.done()
# This can hopefully be done with a connected-component analysis: Take just the WM image, and
# fill in any gaps (i.e. select the inverse, select the largest connected component, invert again)
# Make sure that floating-point values are handled appropriately
# Combine these images together using the appropriate logic in order to form the 5TT image
progress = app.ProgressBar('Modulating segmentation images based on other tissues', 9)
tissue_images = [ 'tissue0.mif', 'tissue1.mif', 'tissue2.mif', 'tissue3.mif', 'tissue4.mif' ]
run.function(os.rename, 'tissue4_init.mif', 'tissue4.mif')
progress.increment()
run.command('mrcalc tissue3_init.mif tissue3_init.mif ' + tissue_images[4] + ' -add 1.0 -sub 0.0 -max -sub 0.0 -max ' + tissue_images[3])
app.cleanup('tissue3_init.mif')
progress.increment()
run.command('mrmath ' + ' '.join(tissue_images[3:5]) + ' sum tissuesum_34.mif')
progress.increment()
run.command('mrcalc tissue1_init.mif tissue1_init.mif tissuesum_34.mif -add 1.0 -sub 0.0 -max -sub 0.0 -max ' + tissue_images[1])
app.cleanup('tissue1_init.mif')
app.cleanup('tissuesum_34.mif')
progress.increment()
run.command('mrmath ' + tissue_images[1] + ' ' + ' '.join(tissue_images[3:5]) + ' sum tissuesum_134.mif')
progress.increment()
run.command('mrcalc tissue2_init.mif tissue2_init.mif tissuesum_134.mif -add 1.0 -sub 0.0 -max -sub 0.0 -max ' + tissue_images[2])
app.cleanup('tissue2_init.mif')
app.cleanup('tissuesum_134.mif')
progress.increment()
run.command('mrmath ' + ' '.join(tissue_images[1:5]) + ' sum tissuesum_1234.mif')
progress.increment()
run.command('mrcalc tissue0_init.mif tissue0_init.mif tissuesum_1234.mif -add 1.0 -sub 0.0 -max -sub 0.0 -max ' + tissue_images[0])
app.cleanup('tissue0_init.mif')
app.cleanup('tissuesum_1234.mif')
progress.increment()
tissue_sum_image = 'tissuesum_01234.mif'
run.command('mrmath ' + ' '.join(tissue_images) + ' sum ' + tissue_sum_image)
progress.done()
if app.ARGS.template:
run.command('mrtransform ' + mask_image + ' -template template.mif - | mrthreshold - brainmask.mif -abs 0.5')
mask_image = 'brainmask.mif'
# Branch depending on whether or not FSL fast will be used to re-segment the cerebellum
if have_fast:
# How to support -template option?
# - Re-grid norm.mgz to template image before running FAST
# - Re-grid FAST output to template image
# Consider splitting, including initial mapping of cerebellar regions:
# - If we're not using a separate template image, just map cerebellar regions to voxels to
# produce a mask, and run FAST within that mask
# - If we have a template, combine cerebellar regions, convert to surfaces (one per hemisphere),
# map these to the template image, run FIRST on a binary mask from this, then
# re-combine this with the tissue maps from other sources based on the estimated PVF of
# cerebellum meshes
cerebellum_volume_image = 'Cerebellum_volume.mif'
cerebellum_mask_image = 'Cerebellum_mask.mif'
t1_cerebellum_masked = 'T1_cerebellum_precrop.mif'
if app.ARGS.template:
# If this is the case, then we haven't yet performed any cerebellar segmentation / meshing
# What we want to do is: for each hemisphere, combine all three "cerebellar" segments from FreeSurfer,
# convert to a surface, map that surface to the template image
progress = app.ProgressBar('Preparing images of cerebellum for intensity-based segmentation', 9)
cerebellar_hemi_pvf_images = [ ]
for hemi in [ 'Left', 'Right' ]:
init_mesh_path = hemi + '-Cerebellum-All-Init.vtk'
smooth_mesh_path = hemi + '-Cerebellum-All-Smooth.vtk'
pvf_image_path = hemi + '-Cerebellum-PVF-Template.mif'
cerebellum_aseg_hemi = [ entry for entry in CEREBELLUM_ASEG if hemi in entry[2] ]
run.command('mrcalc ' + aparc_image + ' ' + str(cerebellum_aseg_hemi[0][0]) + ' -eq ' + \
' -add '.join([ aparc_image + ' ' + str(index) + ' -eq' for index, tissue, name in cerebellum_aseg_hemi[1:] ]) + ' -add - | ' + \
'voxel2mesh - ' + init_mesh_path)
progress.increment()
run.command('meshfilter ' + init_mesh_path + ' smooth ' + smooth_mesh_path)
app.cleanup(init_mesh_path)
progress.increment()
run.command('mesh2voxel ' + smooth_mesh_path + ' ' + template_image + ' ' + pvf_image_path)
app.cleanup(smooth_mesh_path)
cerebellar_hemi_pvf_images.append(pvf_image_path)
progress.increment()
# Combine the two hemispheres together into:
# - An image in preparation for running FAST
# - A combined total partial volume fraction image that will be later used for tissue recombination
run.command('mrcalc ' + ' '.join(cerebellar_hemi_pvf_images) + ' -add 1.0 -min ' + cerebellum_volume_image)
app.cleanup(cerebellar_hemi_pvf_images)
progress.increment()
run.command('mrthreshold ' + cerebellum_volume_image + ' ' + cerebellum_mask_image + ' -abs 1e-6')
progress.increment()
run.command('mrtransform ' + norm_image + ' -template ' + template_image + ' - | ' + \
'mrcalc - ' + cerebellum_mask_image + ' -mult ' + t1_cerebellum_masked)
progress.done()
else:
app.console('Preparing images of cerebellum for intensity-based segmentation')
run.command('mrcalc ' + aparc_image + ' ' + str(CEREBELLUM_ASEG[0][0]) + ' -eq ' + \
' -add '.join([ aparc_image + ' ' + str(index) + ' -eq' for index, tissue, name in CEREBELLUM_ASEG[1:] ]) + ' -add ' + \
cerebellum_volume_image)
cerebellum_mask_image = cerebellum_volume_image
run.command('mrcalc T1.nii ' + cerebellum_mask_image + ' -mult ' + t1_cerebellum_masked)
app.cleanup('T1.nii')
# Any code below here should be compatible with cerebellum_volume_image.mif containing partial volume fractions
# (in the case of no explicit template image, it's a mask, but the logic still applies)
app.console('Running FSL fast to segment the cerebellum based on intensity information')
# Run FSL FAST just within the cerebellum
# FAST memory usage can also be huge when using a high-resolution template image:
# Crop T1 image around the cerebellum before feeding to FAST, then re-sample to full template image FoV
fast_input_image = 'T1_cerebellum.nii'
run.command('mrgrid ' + t1_cerebellum_masked + ' crop -mask ' + cerebellum_mask_image + ' ' + fast_input_image)
app.cleanup(t1_cerebellum_masked)
# Cleanup of cerebellum_mask_image:
# May be same image as cerebellum_volume_image, which is required later
if cerebellum_mask_image != cerebellum_volume_image:
app.cleanup(cerebellum_mask_image)
run.command(fast_cmd + ' -N ' + fast_input_image)
app.cleanup(fast_input_image)
# Use glob to clean up unwanted FAST outputs
fast_output_prefix = os.path.splitext(fast_input_image)[0]
fast_pve_output_prefix = fast_output_prefix + '_pve_'
app.cleanup([ entry for entry in glob.glob(fast_output_prefix + '*') if not fast_pve_output_prefix in entry ])
progress = app.ProgressBar('Introducing intensity-based cerebellar segmentation into the 5TT image', 10)
fast_outputs_cropped = [ fast_pve_output_prefix + str(n) + fast_suffix for n in range(0,3) ]
fast_outputs_template = [ 'FAST_' + str(n) + '.mif' for n in range(0,3) ]
for inpath, outpath in zip(fast_outputs_cropped, fast_outputs_template):
run.command('mrtransform ' + inpath + ' -interp nearest -template ' + template_image + ' ' + outpath)
app.cleanup(inpath)
progress.increment()
if app.ARGS.template:
app.cleanup(template_image)
# Generate the revised tissue images, using output from FAST inside the cerebellum and
# output from previous processing everywhere else
# Note that the middle intensity (grey matter) in the FAST output here gets assigned
# to the sub-cortical grey matter component
# Some of these voxels may have existing non-zero tissue components.
# In that case, let's find a multiplier to apply to cerebellum tissues such that the
# sum does not exceed 1.0
new_tissue_images = [ 'tissue0_fast.mif', 'tissue1_fast.mif', 'tissue2_fast.mif', 'tissue3_fast.mif', 'tissue4_fast.mif' ]
new_tissue_sum_image = 'tissuesum_01234_fast.mif'
cerebellum_multiplier_image = 'Cerebellar_multiplier.mif'
run.command('mrcalc ' + cerebellum_volume_image + ' ' + tissue_sum_image + ' -add 0.5 -gt 1.0 ' + tissue_sum_image + ' -sub 0.0 -if ' + cerebellum_multiplier_image)
app.cleanup(cerebellum_volume_image)
progress.increment()
run.command('mrconvert ' + tissue_images[0] + ' ' + new_tissue_images[0])
app.cleanup(tissue_images[0])
progress.increment()
run.command('mrcalc ' + tissue_images[1] + ' ' + cerebellum_multiplier_image + ' ' + fast_outputs_template[1] + ' -mult -add ' + new_tissue_images[1])
app.cleanup(tissue_images[1])
app.cleanup(fast_outputs_template[1])
progress.increment()
run.command('mrcalc ' + tissue_images[2] + ' ' + cerebellum_multiplier_image + ' ' + fast_outputs_template[2] + ' -mult -add ' + new_tissue_images[2])
app.cleanup(tissue_images[2])
app.cleanup(fast_outputs_template[2])
progress.increment()
run.command('mrcalc ' + tissue_images[3] + ' ' + cerebellum_multiplier_image + ' ' + fast_outputs_template[0] + ' -mult -add ' + new_tissue_images[3])
app.cleanup(tissue_images[3])
app.cleanup(fast_outputs_template[0])
app.cleanup(cerebellum_multiplier_image)
progress.increment()
run.command('mrconvert ' + tissue_images[4] + ' ' + new_tissue_images[4])
app.cleanup(tissue_images[4])
progress.increment()
run.command('mrmath ' + ' '.join(new_tissue_images) + ' sum ' + new_tissue_sum_image)
app.cleanup(tissue_sum_image)
progress.done()
tissue_images = new_tissue_images
tissue_sum_image = new_tissue_sum_image
# For all voxels within FreeSurfer's brain mask, add to the CSF image in order to make the sum 1.0
progress = app.ProgressBar('Performing fill operations to preserve unity tissue volume', 2)
# Some voxels may get a non-zero cortical GM fraction due to native use of the surface representation, yet
# these voxels are actually outside FreeSurfer's own provided brain mask. So what we need to do here is
# get the union of the tissue sum nonzero image and the mask image, and use that at the -mult step of the
# mrcalc call.
# Required image: (tissue_sum_image > 0.0) || mask_image
# tissue_sum_image 0.0 -gt mask_image -add 1.0 -min
new_tissue_images = [ tissue_images[0],
tissue_images[1],
tissue_images[2],
os.path.splitext(tissue_images[3])[0] + '_filled.mif',
tissue_images[4] ]
csf_fill_image = 'csf_fill.mif'
run.command('mrcalc 1.0 ' + tissue_sum_image + ' -sub ' + tissue_sum_image + ' 0.0 -gt ' + mask_image + ' -add 1.0 -min -mult 0.0 -max ' + csf_fill_image)
app.cleanup(tissue_sum_image)
# If no template is specified, this file is part of the FreeSurfer output; hence don't modify
if app.ARGS.template:
app.cleanup(mask_image)
progress.increment()
run.command('mrcalc ' + tissue_images[3] + ' ' + csf_fill_image + ' -add ' + new_tissue_images[3])
app.cleanup(csf_fill_image)
app.cleanup(tissue_images[3])
progress.done()
tissue_images = new_tissue_images
# Move brain stem from white matter to pathology at final step:
# this prevents the brain stem segmentation from overwriting other
# structures that it otherwise wouldn't if it were written to WM
if not app.ARGS.white_stem:
progress = app.ProgressBar('Moving brain stem to volume index 4', 3)
new_tissue_images = [ tissue_images[0],
tissue_images[1],
os.path.splitext(tissue_images[2])[0] + '_no_brainstem.mif',
tissue_images[3],
os.path.splitext(tissue_images[4])[0] + '_with_brainstem.mif' ]
run.command('mrcalc ' + tissue_images[2] + ' brain_stem.mif -min brain_stem_white_overlap.mif')
app.cleanup('brain_stem.mif')
progress.increment()
run.command('mrcalc ' + tissue_images[2] + ' brain_stem_white_overlap.mif -sub ' + new_tissue_images[2])
app.cleanup(tissue_images[2])
progress.increment()
run.command('mrcalc ' + tissue_images[4] + ' brain_stem_white_overlap.mif -add ' + new_tissue_images[4])
app.cleanup(tissue_images[4])
app.cleanup('brain_stem_white_overlap.mif')
progress.done()
tissue_images = new_tissue_images
# Finally, concatenate the volumes to produce the 5TT image
app.console('Concatenating tissue volumes into 5TT format')
precrop_result_image = '5TT.mif'
if bs_cropmask_path:
run.command('mrcat ' + ' '.join(tissue_images) + ' - -axis 3 | ' + \
'5ttedit - ' + precrop_result_image + ' -none ' + bs_cropmask_path)
app.cleanup(bs_cropmask_path)
else:
run.command('mrcat ' + ' '.join(tissue_images) + ' ' + precrop_result_image + ' -axis 3')
app.cleanup(tissue_images)
# Maybe don't go off all tissues here, since FreeSurfer's mask can be fairly liberal;
# instead get just a voxel clearance from all other tissue types (maybe two)
if app.ARGS.nocrop:
run.function(os.rename, precrop_result_image, 'result.mif')
else:
app.console('Cropping final 5TT image')
crop_mask_image = 'crop_mask.mif'
run.command('mrconvert ' + precrop_result_image + ' -coord 3 0,1,2,4 - | mrmath - sum - -axis 3 | mrthreshold - - -abs 0.001 | maskfilter - dilate ' + crop_mask_image)
run.command('mrgrid ' + precrop_result_image + ' crop result.mif -mask ' + crop_mask_image)
app.cleanup(crop_mask_image)
app.cleanup(precrop_result_image)
run.command('mrconvert result.mif ' + path.from_user(app.ARGS.output),
mrconvert_keyval=path.from_user(os.path.join(app.ARGS.input, 'mri', 'aparc+aseg.mgz'), True),
force=app.FORCE_OVERWRITE)
