def getInputs():
import os
from mrtrix3 import app, image, path, run
if app.args.mask:
run.command('mrconvert ' + path.fromUser(app.args.mask, True) + ' ' + path.toTemp('mask.mif', True) + ' -datatype bit -stride -1,+2,+3')
if app.args.t2:
if not image.match(app.args.input, app.args.t2):
app.error('Provided T2 image does not match input T1 image')
run.command('mrconvert ' + path.fromUser(app.args.t2, True) + ' ' + path.toTemp('T2.nii', True) + ' -stride -1,+2,+3')
def get_inputs(): #pylint: disable=unused-variable
image.check_3d_nonunity(path.from_user(app.ARGS.input, False))
run.command('mrconvert ' + path.from_user(app.ARGS.input) + ' ' + path.to_scratch('input.mif'))
if app.ARGS.mask:
run.command('mrconvert ' + path.from_user(app.ARGS.mask) + ' ' + path.to_scratch('mask.mif') + ' -datatype bit -strides -1,+2,+3')
if app.ARGS.t2:
if not image.match(path.from_user(app.ARGS.input, False), path.from_user(app.ARGS.t2, False)):
raise MRtrixError('Provided T2 image does not match input T1 image')
run.command('mrconvert ' + path.from_user(app.ARGS.t2) + ' ' + path.to_scratch('T2.nii') + ' -strides -1,+2,+3')
def getInputs(): #pylint: disable=unused-variable
from mrtrix3 import app, image, path, run
image.check3DNonunity(path.fromUser(app.args.input, False))
run.command('mrconvert ' + path.fromUser(app.args.input, True) + ' ' + path.toTemp('input.mif', True))
if app.args.mask:
run.command('mrconvert ' + path.fromUser(app.args.mask, True) + ' ' + path.toTemp('mask.mif', True) + ' -datatype bit -strides -1,+2,+3')
if app.args.t2:
if not image.match(app.args.input, app.args.t2):
app.error('Provided T2 image does not match input T1 image')
run.command('mrconvert ' + path.fromUser(app.args.t2, True) + ' ' + path.toTemp('T2.nii', True) + ' -strides -1,+2,+3')
def getInputs(): #pylint: disable=unused-variable
from mrtrix3 import app, image, path, run
image.check3DNonunity(path.fromUser(app.args.input, False))
run.command('mrconvert ' + path.fromUser(app.args.input, True) + ' ' +
path.toTemp('input.mif', True))
if app.args.mask:
run.command('mrconvert ' + path.fromUser(app.args.mask, True) + ' ' +
path.toTemp('mask.mif', True) +
' -datatype bit -strides -1,+2,+3')
if app.args.t2:
if not image.match(app.args.input, app.args.t2):
app.error('Provided T2 image does not match input T1 image')
run.command('mrconvert ' + path.fromUser(app.args.t2, True) + ' ' +
path.toTemp('T2.nii', True) + ' -strides -1,+2,+3')
def execute():
import os
from distutils.spawn import find_executable
from mrtrix3 import app, file, fsl, image, run
if app.isWindows():
app.error(
'\'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:
app.error(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
ssroi_cmd = 'standard_space_roi'
if not find_executable(ssroi_cmd):
ssroi_cmd = 'fsl5.0-standard_space_roi'
if not find_executable(ssroi_cmd):
app.error(
'Could not find FSL program standard_space_roi; please verify FSL install'
)
bet_cmd = 'bet'
if not find_executable(bet_cmd):
bet_cmd = 'fsl5.0-bet'
if not find_executable(bet_cmd):
app.error(
'Could not find FSL program bet; please verify FSL install')
fast_cmd = 'fast'
if not find_executable(fast_cmd):
fast_cmd = 'fsl5.0-fast'
if not find_executable(fast_cmd):
app.error(
'Could not find FSL program fast; please verify FSL install')
first_cmd = 'run_first_all'
if not find_executable(first_cmd):
first_cmd = "fsl5.0-run_first_all"
if not find_executable(first_cmd):
app.error(
'Could not find FSL program run_first_all; please verify FSL install'
)
first_atlas_path = os.path.join(fsl_path, 'data', 'first',
'models_336_bin')
if not os.path.isdir(first_atlas_path):
app.error(
'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'])
run.command('mrconvert input.mif T1.nii -stride -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')
run.command('mrcalc T1.nii mask_regrid.mif ' + 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
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, False)
else:
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b',
False)
# For whatever reason, the output file from standard_space_roi may not be
# completed before BET is run
file.waitFor('T1_preBET' + fsl_suffix)
# BET
fast_t1_input = 'T1_BET' + fsl_suffix
run.command(bet_cmd + ' T1_preBET' + fsl_suffix + ' ' + fast_t1_input +
' -f 0.15 -R')
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)
fast_output_prefix = fast_t1_input.split('.')[0]
# FIRST
first_input_is_brain_extracted = ''
if app.args.premasked:
first_input_is_brain_extracted = ' -b'
run.command(first_cmd + ' -s ' + ','.join(sgm_structures) +
' -i T1.nii -o first' + first_input_is_brain_extracted)
# Test to see whether or not FIRST has succeeded
# However if the expected image is absent, it may be due to FIRST being run
# on SGE; in this case it is necessary to wait and see if the file appears.
# But even in this case, FIRST may still fail, and the file will never appear...
combined_image_path = 'first_all_none_firstseg' + fsl_suffix
if not os.path.isfile(combined_image_path):
if 'SGE_ROOT' in os.environ:
app.console(
'FSL FIRST job has been submitted to SGE; awaiting completion')
app.console(
'(note however that FIRST may fail, and hence this script may hang indefinitely)'
)
file.waitFor(combined_image_path)
else:
app.error(
'FSL FIRST has failed; not all structures were segmented successfully (check '
+ path.toTemp('first.logs', False) + ')')
# Convert FIRST meshes to partial volume images
pve_image_list = []
for struct in sgm_structures:
pve_image_path = 'mesh2pve_' + 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 T1.nii')
run.command('mesh2pve ' + vtk_temp_path + ' ' + fast_t1_input + ' ' +
pve_image_path)
pve_image_list.append(pve_image_path)
pve_cat = ' '.join(pve_image_list)
run.command('mrmath ' + pve_cat +
' sum - | mrcalc - 1.0 -min all_sgms.mif')
# Looks like FAST in 5.0 ignores FSLOUTPUTTYPE when writing the PVE images
# Will have to wait and see whether this changes, and update the script accordingly
if fast_cmd == 'fast':
fast_suffix = fsl_suffix
else:
fast_suffix = '.nii.gz'
# Combine the tissue images into the 5TT format within the script itself
# Step 1: Run LCC on the WM image
run.command(
'mrthreshold ' + fast_output_prefix + '_pve_2' + fast_suffix +
' - -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 5ttgen command
run.command('mrcalc ' + fast_output_prefix + '_pve_0' + fast_suffix +
' 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_output_prefix +
'_pve_1' + fast_suffix + ' ' + fast_output_prefix + '_pve_2' +
fast_suffix + ' -add -div multiplier.mif')
run.command(
'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif'
)
run.command(
'mrcalc ' + fast_output_prefix + '_pve_1' + fast_suffix +
' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif'
)
run.command(
'mrcalc ' + fast_output_prefix + '_pve_2' + fast_suffix +
' 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 -stride +2,+3,+4,+1'
)
# Use mrcrop to reduce file size (improves caching of image data during tracking)
if app.args.nocrop:
run.command('mrconvert combined_precrop.mif result.mif')
else:
run.command(
'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -'
)
def execute(): #pylint: disable=unused-variable
import math, os
from mrtrix3 import app, fsl, image, run
if app.isWindows():
app.error('\'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:
app.error('Environment variable FSLDIR is not set; please run appropriate FSL configuration script')
bet_cmd = fsl.exeName('bet')
fast_cmd = fsl.exeName('fast')
first_cmd = fsl.exeName('run_first_all')
ssroi_cmd = fsl.exeName('standard_space_roi')
first_atlas_path = os.path.join(fsl_path, 'data', 'first', 'models_336_bin')
if not os.path.isdir(first_atlas_path):
app.error('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
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, False)
else:
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b', False)
pre_bet_image = fsl.findImage('T1_preBET')
# BET
run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix + ' -f 0.15 -R')
fast_t1_input = fsl.findImage('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('mrresize T1.nii T1_1mm.nii -voxel 1.0 -interp sinc')
first_input = 'T1_1mm.nii'
first_input_brain_extracted_option = ''
if app.args.premasked:
first_input_brain_extracted_option = ' -b'
first_debug_option = ''
if not app.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_input_brain_extracted_option + first_debug_option + first_verbosity_option)
fsl.checkFirst('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 ' + ' '.join(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.findImage(fast_output_prefix + '_pve_0')
fast_gm_output = fsl.findImage(fast_output_prefix + '_pve_1')
fast_wm_output = fsl.findImage(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')
# Use mrcrop to reduce file size (improves caching of image data during tracking)
if app.args.nocrop:
run.command('mrconvert combined_precrop.mif result.mif')
else:
run.command('mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -')
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():
import os
from distutils.spawn import find_executable
from mrtrix3 import app, file, fsl, image, path, run
if app.isWindows():
app.error(
'\'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:
app.error(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
ssroi_cmd = 'standard_space_roi'
if not find_executable(ssroi_cmd):
ssroi_cmd = 'fsl5.0-standard_space_roi'
if not find_executable(ssroi_cmd):
app.error(
'Could not find FSL program standard_space_roi; please verify FSL install'
)
bet_cmd = 'bet'
if not find_executable(bet_cmd):
bet_cmd = 'fsl5.0-bet'
if not find_executable(bet_cmd):
app.error(
'Could not find FSL program bet; please verify FSL install')
fast_cmd = 'fast'
if not find_executable(fast_cmd):
fast_cmd = 'fsl5.0-fast'
if not find_executable(fast_cmd):
app.error(
'Could not find FSL program fast; please verify FSL install')
first_cmd = 'run_first_all'
if not find_executable(first_cmd):
first_cmd = "fsl5.0-run_first_all"
if not find_executable(first_cmd):
app.error(
'Could not find FSL program run_first_all; please verify FSL install'
)
first_atlas_path = os.path.join(fsl_path, 'data', 'first',
'models_336_bin')
if not os.path.isdir(first_atlas_path):
app.error(
'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'])
run.command('mrconvert input.mif T1.nii -stride -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')
run.command('mrcalc T1.nii mask_regrid.mif ' + 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
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, False)
else:
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b',
False)
pre_bet_image = fsl.findImage('T1_preBET')
# BET
run.command(bet_cmd + ' ' + pre_bet_image + ' T1_BET' + fsl_suffix +
' -f 0.15 -R')
fast_t1_input = fsl.findImage('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_is_brain_extracted = ''
if app.args.premasked:
first_input_is_brain_extracted = ' -b'
run.command(first_cmd + ' -m none -s ' + ','.join(sgm_structures) +
' -i T1.nii -o first' + first_input_is_brain_extracted)
fsl.checkFirst('first', sgm_structures)
# Convert FIRST meshes to partial volume images
pve_image_list = []
for struct in sgm_structures:
pve_image_path = 'mesh2pve_' + 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 T1.nii')
run.command('mesh2pve ' + vtk_temp_path + ' ' + fast_t1_input + ' ' +
pve_image_path)
pve_image_list.append(pve_image_path)
pve_cat = ' '.join(pve_image_list)
run.command('mrmath ' + pve_cat +
' 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.findImage(fast_output_prefix + '_pve_0')
fast_gm_output = fsl.findImage(fast_output_prefix + '_pve_1')
fast_wm_output = fsl.findImage(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 -stride +2,+3,+4,+1'
)
# Use mrcrop to reduce file size (improves caching of image data during tracking)
if app.args.nocrop:
run.command('mrconvert combined_precrop.mif result.mif')
else:
run.command(
'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -'
)