def execute():
import os, shutil
import lib.app
from lib.errorMessage import errorMessage
from lib.getHeaderInfo import getHeaderInfo
from lib.runCommand import runCommand
lmax_option = ''
if lib.app.args.lmax:
lmax_option = ' -lmax ' + lib.app.args.lmax
shells = [ int(round(float(x))) for x in getHeaderInfo('dwi.mif', 'shells').split() ]
# Do we have directions, or do we need to calculate them?
if not os.path.exists('dirs.mif'):
runCommand('dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif')
# Loop over shells
response = [ ]
max_length = 0
for index, b in enumerate(shells):
runCommand('dwiextract dwi.mif - -shell ' + str(b) + ' | amp2sh - - | sh2response - in_voxels.mif dirs.mif response_b' + str(b) + '.txt' + lmax_option)
shell_response = open('response_b' + str(b) + '.txt', 'r').read().split()
response.append(shell_response)
max_length = max(max_length, len(shell_response))
with open('response.txt', 'w') as f:
for line in response:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
shutil.copyfile('response.txt', os.path.join(lib.app.workingDir, lib.app.args.output))
shutil.copyfile('in_voxels.mif', 'voxels.mif')
def execute():
import os, shutil
import lib.app
from lib.errorMessage import errorMessage
from lib.getHeaderInfo import getHeaderInfo
from lib.getUserPath import getUserPath
from lib.runCommand import runCommand
shells = [
int(round(float(x)))
for x in getHeaderInfo('dwi.mif', 'shells').split()
]
# Get lmax information (if provided)
lmax = []
if lib.app.args.lmax:
lmax = [int(x.strip()) for x in lib.app.args.lmax.split(',')]
if not len(lmax) == len(shells):
errorMessage('Number of manually-defined lmax\'s (' +
str(len(lmax)) +
') does not match number of b-value shells (' +
str(len(shells)) + ')')
for l in lmax:
if l % 2:
errorMessage('Values for lmax must be even')
if l < 0:
errorMessage('Values for lmax must be non-negative')
# Do we have directions, or do we need to calculate them?
if not os.path.exists('dirs.mif'):
runCommand(
'dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif'
)
# Loop over shells
response = []
max_length = 0
for index, b in enumerate(shells):
lmax_option = ''
if lmax:
lmax_option = ' -lmax ' + str(lmax[index])
runCommand(
'dwiextract dwi.mif - -shell ' + str(b) +
' | amp2sh - - | sh2response - in_voxels.mif dirs.mif response_b' +
str(b) + '.txt' + lmax_option)
shell_response = open('response_b' + str(b) + '.txt',
'r').read().split()
response.append(shell_response)
max_length = max(max_length, len(shell_response))
with open('response.txt', 'w') as f:
for line in response:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
shutil.copyfile('response.txt', getUserPath(lib.app.args.output, False))
shutil.copyfile('in_voxels.mif', 'voxels.mif')
def imagesMatch(image_one, image_two):
import math
from lib.getHeaderInfo import getHeaderInfo
# Image dimensions
one_dim = [int(i) for i in getHeaderInfo(image_one, 'size').split()]
two_dim = [int(i) for i in getHeaderInfo(image_two, 'size').split()]
if not one_dim == two_dim:
return False
# Voxel size
one_spacing = [float(f) for f in getHeaderInfo(image_one, 'vox').split()]
two_spacing = [float(f) for f in getHeaderInfo(image_two, 'vox').split()]
for one, two in zip(one_spacing, two_spacing):
if one and two and not math.isnan(one) and not math.isnan(two):
if (abs(two - one) / (0.5 * (one + two))) > 1e-04:
return False
# Image transform
one_transform = [
float(f) for f in getHeaderInfo(image_one, 'transform').replace(
'\n', ' ').replace(',', ' ').split()
]
two_transform = [
float(f) for f in getHeaderInfo(image_two, 'transform').replace(
'\n', ' ').replace(',', ' ').split()
]
for one, two in zip(one_transform, two_transform):
if abs(one - two) > 1e-4:
return False
# Everything matches!
return True
def execute():
import os, shutil
import lib.app
from lib.errorMessage import errorMessage
from lib.getHeaderInfo import getHeaderInfo
from lib.getUserPath import getUserPath
from lib.runCommand import runCommand
shells = [ int(round(float(x))) for x in getHeaderInfo('dwi.mif', 'shells').split() ]
# Get lmax information (if provided)
lmax = [ ]
if lib.app.args.lmax:
lmax = [ int(x.strip()) for x in lib.app.args.lmax.split(',') ]
if not len(lmax) == len(shells):
errorMessage('Number of manually-defined lmax\'s (' + str(len(lmax)) + ') does not match number of b-value shells (' + str(len(shells)) + ')')
for l in lmax:
if l%2:
errorMessage('Values for lmax must be even')
if l<0:
errorMessage('Values for lmax must be non-negative')
# Do we have directions, or do we need to calculate them?
if not os.path.exists('dirs.mif'):
runCommand('dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif')
# Loop over shells
response = [ ]
max_length = 0
for index, b in enumerate(shells):
lmax_option = ''
if lmax:
lmax_option = ' -lmax ' + str(lmax[index])
runCommand('dwiextract dwi.mif - -shell ' + str(b) + ' | amp2sh - - | sh2response - in_voxels.mif dirs.mif response_b' + str(b) + '.txt' + lmax_option)
shell_response = open('response_b' + str(b) + '.txt', 'r').read().split()
response.append(shell_response)
max_length = max(max_length, len(shell_response))
with open('response.txt', 'w') as f:
for line in response:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
shutil.copyfile('response.txt', getUserPath(lib.app.args.output, False))
shutil.copyfile('in_voxels.mif', 'voxels.mif')
def execute():
import os, shutil
import lib.app
from lib.errorMessage import errorMessage
from lib.getHeaderInfo import getHeaderInfo
from lib.runCommand import runCommand
lmax_option = ''
if lib.app.args.lmax:
lmax_option = ' -lmax ' + lib.app.args.lmax
shells = [
int(round(float(x)))
for x in getHeaderInfo('dwi.mif', 'shells').split()
]
# Do we have directions, or do we need to calculate them?
if not os.path.exists('dirs.mif'):
runCommand(
'dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif'
)
# Loop over shells
response = []
max_length = 0
for index, b in enumerate(shells):
runCommand(
'dwiextract dwi.mif - -shell ' + str(b) +
' | amp2sh - - | sh2response - in_voxels.mif dirs.mif response_b' +
str(b) + '.txt' + lmax_option)
shell_response = open('response_b' + str(b) + '.txt',
'r').read().split()
response.append(shell_response)
max_length = max(max_length, len(shell_response))
with open('response.txt', 'w') as f:
for line in response:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
shutil.copyfile('response.txt',
os.path.join(lib.app.workingDir, lib.app.args.output))
shutil.copyfile('in_voxels.mif', 'voxels.mif')
def imagesMatch(image_one, image_two):
import math
from lib.getHeaderInfo import getHeaderInfo
# Image dimensions
one_dim = [ int(i) for i in getHeaderInfo(image_one, 'size').split() ]
two_dim = [ int(i) for i in getHeaderInfo(image_two, 'size').split() ]
if not one_dim == two_dim:
return False
# Voxel size
one_spacing = [ float(f) for f in getHeaderInfo(image_one, 'vox').split() ]
two_spacing = [ float(f) for f in getHeaderInfo(image_two, 'vox').split() ]
for one, two in zip(one_spacing, two_spacing):
if one and two and not math.isnan(one) and not math.isnan(two):
if (abs(two-one) / (0.5*(one+two))) > 1e-04:
return False
# Image transform
one_transform = [ float(f) for f in getHeaderInfo(image_one, 'transform').replace('\n', ' ').replace(',', ' ').split() ]
two_transform = [ float(f) for f in getHeaderInfo(image_two, 'transform').replace('\n', ' ').replace(',', ' ').split() ]
for one, two in zip(one_transform, two_transform):
if abs(one-two) > 1e-4:
return False
# Everything matches!
return True
def execute():
import math, os, shutil
import lib.app
from lib.getHeaderInfo import getHeaderInfo
from lib.getImageStat import getImageStat
from lib.getUserPath import getUserPath
from lib.printMessage import printMessage
from lib.runCommand import runCommand
from lib.warnMessage import warnMessage
from lib.errorMessage import errorMessage
# Ideally want to use the oversampling-based regridding of the 5TT image from the SIFT model, not mrtransform
# May need to commit 5ttregrid...
# Verify input 5tt image
sizes = [ int(x) for x in getHeaderInfo('5tt.mif', 'size').split() ]
datatype = getHeaderInfo('5tt.mif', 'datatype')
if not len(sizes) == 4 or not sizes[3] == 5 or not datatype.startswith('Float'):
errorMessage('Imported anatomical image ' + os.path.basename(lib.app.args.in_5tt) + ' is not in the 5TT format')
# Get shell information
shells = [ int(round(float(x))) for x in getHeaderInfo('dwi.mif', 'shells').split() ]
if len(shells) < 3:
warnMessage('Less than three b-value shells; response functions will not be applicable in MSMT-CSD algorithm')
# Get lmax information (if provided)
wm_lmax = [ ]
if lib.app.args.lmax:
wm_lmax = [ int(x.strip()) for x in lib.app.args.lmax.split(',') ]
if not len(wm_lmax) == len(shells):
errorMessage('Number of manually-defined lmax\'s (' + str(len(wm_lmax)) + ') does not match number of b-value shells (' + str(len(shells)) + ')')
for l in wm_lmax:
if l%2:
errorMessage('Values for lmax must be even')
if l<0:
errorMessage('Values for lmax must be non-negative')
runCommand('dwi2tensor dwi.mif - -mask mask.mif | tensor2metric - -fa fa.mif -vector vector.mif')
if not os.path.exists('dirs.mif'):
shutil.copy('vector.mif', 'dirs.mif')
runCommand('mrtransform 5tt.mif 5tt_regrid.mif -template fa.mif -interp linear')
# Basic tissue masks
runCommand('mrconvert 5tt_regrid.mif - -coord 3 2 -axes 0,1,2 | mrcalc - ' + str(lib.app.args.pvf) + ' -gt mask.mif -mult wm_mask.mif')
runCommand('mrconvert 5tt_regrid.mif - -coord 3 0 -axes 0,1,2 | mrcalc - ' + str(lib.app.args.pvf) + ' -gt fa.mif ' + str(lib.app.args.fa) + ' -lt -mult mask.mif -mult gm_mask.mif')
runCommand('mrconvert 5tt_regrid.mif - -coord 3 3 -axes 0,1,2 | mrcalc - ' + str(lib.app.args.pvf) + ' -gt fa.mif ' + str(lib.app.args.fa) + ' -lt -mult mask.mif -mult csf_mask.mif')
# Revise WM mask to only include single-fibre voxels
printMessage('Calling dwi2response recursively to select WM single-fibre voxels using \'' + lib.app.args.wm_algo + '\' algorithm')
recursive_cleanup_option=''
if not lib.app.cleanup:
recursive_cleanup_option = ' -nocleanup'
runCommand('dwi2response -quiet -tempdir ' + lib.app.tempDir + recursive_cleanup_option + ' ' + lib.app.args.wm_algo + ' dwi.mif wm_ss_response.txt -mask wm_mask.mif -voxels wm_sf_mask.mif')
# Check for empty masks
wm_voxels = int(getImageStat('wm_sf_mask.mif', 'count', 'wm_sf_mask.mif'))
gm_voxels = int(getImageStat('gm_mask.mif', 'count', 'gm_mask.mif'))
csf_voxels = int(getImageStat('csf_mask.mif', 'count', 'csf_mask.mif'))
empty_masks = [ ]
if not wm_voxels:
empty_masks.append('WM')
if not gm_voxels:
empty_masks.append('GM')
if not csf_voxels:
empty_masks.append('CSF')
if empty_masks:
message = ','.join(empty_masks)
message += ' tissue mask'
if len(empty_masks) > 1:
message += 's'
message += ' empty; cannot estimate response function'
if len(empty_masks) > 1:
message += 's'
errorMessage(message)
# For each of the three tissues, generate a multi-shell response
# Since here we're guaranteeing that GM and CSF will be isotropic in all shells, let's use mrstats rather than sh2response (seems a bit weird passing a directions file to sh2response with lmax=0...)
wm_responses = [ ]
gm_responses = [ ]
csf_responses = [ ]
max_length = 0
for index, b in enumerate(shells):
dwi_path = 'dwi_b' + str(b) + '.mif'
# dwiextract will yield a 4D image, even if there's only a single volume in a shell
runCommand('dwiextract dwi.mif -shell ' + str(b) + ' ' + dwi_path)
this_b_lmax_option = ''
if wm_lmax:
this_b_lmax_option = ' -lmax ' + str(wm_lmax[index])
runCommand('amp2sh ' + dwi_path + ' - | sh2response - wm_sf_mask.mif dirs.mif wm_response_b' + str(b) + '.txt' + this_b_lmax_option)
wm_response = open('wm_response_b' + str(b) + '.txt', 'r').read().split()
wm_responses.append(wm_response)
max_length = max(max_length, len(wm_response))
mean_dwi_path = 'dwi_b' + str(b) + '_mean.mif'
runCommand('mrmath ' + dwi_path + ' mean ' + mean_dwi_path + ' -axis 3')
gm_mean = float(getImageStat(mean_dwi_path, 'mean', 'gm_mask.mif'))
csf_mean = float(getImageStat(mean_dwi_path, 'mean', 'csf_mask.mif'))
gm_responses .append( str(gm_mean * math.sqrt(4.0 * math.pi)) )
csf_responses.append( str(csf_mean * math.sqrt(4.0 * math.pi)) )
with open('wm.txt', 'w') as f:
for line in wm_responses:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
with open('gm.txt', 'w') as f:
for line in gm_responses:
f.write(line + '\n')
with open('csf.txt', 'w') as f:
for line in csf_responses:
f.write(line + '\n')
shutil.copyfile('wm.txt', getUserPath(lib.app.args.out_wm, False))
shutil.copyfile('gm.txt', getUserPath(lib.app.args.out_gm, False))
shutil.copyfile('csf.txt', getUserPath(lib.app.args.out_csf, False))
# Generate output 4D binary image with voxel selections; RGB as in MSMT-CSD paper
runCommand('mrcat csf_mask.mif gm_mask.mif wm_sf_mask.mif voxels.mif -axis 3')
def execute():
import os
import lib.app
from lib.binaryInPath import binaryInPath
from lib.errorMessage import errorMessage
from lib.getFSLSuffix import getFSLSuffix
from lib.getHeaderInfo import getHeaderInfo
from lib.isWindows import isWindows
from lib.runCommand import runCommand
from lib.warnMessage import warnMessage
if isWindows():
errorMessage('Script cannot run in FSL mode on Windows')
fsl_path = os.environ.get('FSLDIR', '')
if not fsl_path:
errorMessage('Environment variable FSLDIR is not set; please run appropriate FSL configuration script')
ssroi_cmd = 'standard_space_roi'
if not binaryInPath(ssroi_cmd):
ssroi_cmd = 'fsl5.0-standard_space_roi'
if not binaryInPath(ssroi_cmd):
errorMessage('Could not find FSL program standard_space_roi; please verify FSL install')
bet_cmd = 'bet'
if not binaryInPath(bet_cmd):
bet_cmd = 'fsl5.0-bet'
if not binaryInPath(bet_cmd):
errorMessage('Could not find FSL program bet; please verify FSL install')
fast_cmd = 'fast'
if not binaryInPath(fast_cmd):
fast_cmd = 'fsl5.0-fast'
if not binaryInPath(fast_cmd):
errorMessage('Could not find FSL program fast; please verify FSL install')
first_cmd = 'run_first_all'
if not binaryInPath(first_cmd):
first_cmd = "fsl5.0-run_first_all"
if not binaryInPath(first_cmd):
errorMessage('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):
errorMessage('Atlases required for FSL\'s FIRST program not installed;\nPlease install fsl-first-data using your relevant package manager')
fsl_suffix = getFSLSuffix()
sgm_structures = [ 'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal' ]
runCommand('mrconvert input.mif T1.nii')
# Decide whether or not we're going to do any brain masking
if os.path.exists('mask.mif'):
# Check to see if the dimensions match the T1 image
T1_size = getHeaderInfo('input.mif', 'size')
mask_size = getHeaderInfo('mask.mif', 'size')
if mask_size == T1_size:
runCommand('mrcalc input.mif mask.mif -mult T1_masked.nii')
else:
runCommand('mrtransform mask.mif mask_regrid.mif -template input.mif')
runCommand('mrcalc input.mif mask_regrid.mif -mult T1_masked.nii')
elif lib.app.args.premasked:
runCommand('mrconvert input.mif T1_masked.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;
# standard_space_roi can sometimes crash; if this happens, try to allow the script to continue
if mni_mask_dilation:
runCommand('maskfilter ' + mni_mask_path + ' dilate mni_mask.nii -npass ' + str(mni_mask_dilation))
if lib.app.args.nocrop:
ssroi_roi_option = ' -roiNONE'
else:
ssroi_roi_option = ' -roiFOV'
runCommand(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -maskMASK mni_mask.nii' + ssroi_roi_option, False)
else:
runCommand(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b', False)
if not os.path.isfile('T1_preBET' + fsl_suffix):
warnMessage('FSL command ' + ssroi_cmd + ' appears to have failed; passing T1 directly to BET')
runCommand('mrconvert input.mif T1_preBET' + fsl_suffix)
# BET
runCommand(bet_cmd + ' T1_preBET' + fsl_suffix + ' T1_masked' + fsl_suffix + ' -f 0.15 -R')
# Finish branching based on brain masking
# FAST
runCommand(fast_cmd + ' T1_masked' + fsl_suffix)
# FIRST
first_input_is_brain_extracted = ''
if lib.app.args.premasked:
first_input_is_brain_extracted = ' -b'
runCommand(first_cmd + ' -s ' + ','.join(sgm_structures) + ' -i T1.nii -o first' + first_input_is_brain_extracted)
# Convert FIRST meshes to partial volume images
pve_image_list = [ ]
for struct in sgm_structures:
pve_image_path = 'mesh2pve_' + struct + '.nii'
vtk_in_path = 'first-' + struct + '_first.vtk'
vtk_temp_path = struct + '.vtk'
if not os.path.exists(vtk_in_path):
errorMessage('Missing .vtk file for structure ' + struct + '; run_first_all must have failed')
runCommand('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path + ' -transform_first2real input.mif')
runCommand('mesh2pve ' + vtk_temp_path + ' T1_preBET' + fsl_suffix + ' ' + pve_image_path)
pve_image_list.append(pve_image_path)
pve_cat = ' '.join(pve_image_list)
runCommand('mrmath ' + pve_cat + ' sum - | mrcalc - 1.0 -min all_sgms.nii')
# 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
runCommand('mrthreshold T1_masked_pve_2' + fast_suffix + ' - -abs 0.001 | maskfilter - connect wm_mask.mif -largest')
# Step 2: Generate the images in the same fashion as the 5ttgen command
runCommand('mrconvert T1_masked_pve_0' + fast_suffix + ' csf.mif')
runCommand('mrcalc 1 csf.mif -sub all_sgms.nii -min sgm.mif')
runCommand('mrcalc 1.0 csf.mif sgm.mif -add -sub T1_masked_pve_1' + fast_suffix + ' T1_masked_pve_2' + fast_suffix + ' -add -div multiplier.mif')
runCommand('mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif')
runCommand('mrcalc T1_masked_pve_1' + fast_suffix + ' multiplier_noNAN.mif -mult cgm.mif')
runCommand('mrcalc T1_masked_pve_2' + fast_suffix + ' multiplier_noNAN.mif -mult wm_mask.mif -mult wm.mif')
runCommand('mrcalc 0 wm.mif -min path.mif')
runCommand('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 lib.app.args.nocrop:
runCommand('mrconvert combined_precrop.mif result.mif')
else:
runCommand('mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -')
def execute():
import math, os, shutil
import lib.app
from lib.getHeaderInfo import getHeaderInfo
from lib.getImageStat import getImageStat
from lib.getUserPath import getUserPath
from lib.printMessage import printMessage
from lib.runCommand import runCommand
from lib.warnMessage import warnMessage
from lib.errorMessage import errorMessage
# Ideally want to use the oversampling-based regridding of the 5TT image from the SIFT model, not mrtransform
# May need to commit 5ttregrid...
# Verify input 5tt image
sizes = [int(x) for x in getHeaderInfo('5tt.mif', 'size').split()]
datatype = getHeaderInfo('5tt.mif', 'datatype')
if not len(sizes) == 4 or not sizes[3] == 5 or not datatype.startswith(
'Float'):
errorMessage('Imported anatomical image ' +
os.path.basename(lib.app.args.in_5tt) +
' is not in the 5TT format')
# Get shell information
shells = [
int(round(float(x)))
for x in getHeaderInfo('dwi.mif', 'shells').split()
]
if len(shells) < 3:
warnMessage(
'Less than three b-value shells; response functions will not be applicable in MSMT-CSD algorithm'
)
# Get lmax information (if provided)
wm_lmax = []
if lib.app.args.lmax:
wm_lmax = [int(x.strip()) for x in lib.app.args.lmax.split(',')]
if not len(wm_lmax) == len(shells):
errorMessage('Number of manually-defined lmax\'s (' +
str(len(wm_lmax)) +
') does not match number of b-value shells (' +
str(len(shells)) + ')')
for l in wm_lmax:
if l % 2:
errorMessage('Values for lmax must be even')
if l < 0:
errorMessage('Values for lmax must be non-negative')
runCommand(
'dwi2tensor dwi.mif - -mask mask.mif | tensor2metric - -fa fa.mif -vector vector.mif'
)
if not os.path.exists('dirs.mif'):
shutil.copy('vector.mif', 'dirs.mif')
runCommand(
'mrtransform 5tt.mif 5tt_regrid.mif -template fa.mif -interp linear')
# Basic tissue masks
runCommand(
'mrconvert 5tt_regrid.mif - -coord 3 2 -axes 0,1,2 | mrcalc - ' +
str(lib.app.args.pvf) + ' -gt mask.mif -mult wm_mask.mif')
runCommand(
'mrconvert 5tt_regrid.mif - -coord 3 0 -axes 0,1,2 | mrcalc - ' +
str(lib.app.args.pvf) + ' -gt fa.mif ' + str(lib.app.args.fa) +
' -lt -mult mask.mif -mult gm_mask.mif')
runCommand(
'mrconvert 5tt_regrid.mif - -coord 3 3 -axes 0,1,2 | mrcalc - ' +
str(lib.app.args.pvf) + ' -gt fa.mif ' + str(lib.app.args.fa) +
' -lt -mult mask.mif -mult csf_mask.mif')
# Revise WM mask to only include single-fibre voxels
printMessage(
'Calling dwi2response recursively to select WM single-fibre voxels using \''
+ lib.app.args.wm_algo + '\' algorithm')
recursive_cleanup_option = ''
if not lib.app.cleanup:
recursive_cleanup_option = ' -nocleanup'
runCommand(
'dwi2response -quiet -tempdir ' + lib.app.tempDir +
recursive_cleanup_option + ' ' + lib.app.args.wm_algo +
' dwi.mif wm_ss_response.txt -mask wm_mask.mif -voxels wm_sf_mask.mif')
# Check for empty masks
wm_voxels = int(getImageStat('wm_sf_mask.mif', 'count', 'wm_sf_mask.mif'))
gm_voxels = int(getImageStat('gm_mask.mif', 'count', 'gm_mask.mif'))
csf_voxels = int(getImageStat('csf_mask.mif', 'count', 'csf_mask.mif'))
empty_masks = []
if not wm_voxels:
empty_masks.append('WM')
if not gm_voxels:
empty_masks.append('GM')
if not csf_voxels:
empty_masks.append('CSF')
if empty_masks:
message = ','.join(empty_masks)
message += ' tissue mask'
if len(empty_masks) > 1:
message += 's'
message += ' empty; cannot estimate response function'
if len(empty_masks) > 1:
message += 's'
errorMessage(message)
# For each of the three tissues, generate a multi-shell response
# Since here we're guaranteeing that GM and CSF will be isotropic in all shells, let's use mrstats rather than sh2response (seems a bit weird passing a directions file to sh2response with lmax=0...)
wm_responses = []
gm_responses = []
csf_responses = []
max_length = 0
for index, b in enumerate(shells):
dwi_path = 'dwi_b' + str(b) + '.mif'
# dwiextract will yield a 4D image, even if there's only a single volume in a shell
runCommand('dwiextract dwi.mif -shell ' + str(b) + ' ' + dwi_path)
this_b_lmax_option = ''
if wm_lmax:
this_b_lmax_option = ' -lmax ' + str(wm_lmax[index])
runCommand('amp2sh ' + dwi_path +
' - | sh2response - wm_sf_mask.mif dirs.mif wm_response_b' +
str(b) + '.txt' + this_b_lmax_option)
wm_response = open('wm_response_b' + str(b) + '.txt',
'r').read().split()
wm_responses.append(wm_response)
max_length = max(max_length, len(wm_response))
mean_dwi_path = 'dwi_b' + str(b) + '_mean.mif'
runCommand('mrmath ' + dwi_path + ' mean ' + mean_dwi_path +
' -axis 3')
gm_mean = float(getImageStat(mean_dwi_path, 'mean', 'gm_mask.mif'))
csf_mean = float(getImageStat(mean_dwi_path, 'mean', 'csf_mask.mif'))
gm_responses.append(str(gm_mean * math.sqrt(4.0 * math.pi)))
csf_responses.append(str(csf_mean * math.sqrt(4.0 * math.pi)))
with open('wm.txt', 'w') as f:
for line in wm_responses:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
with open('gm.txt', 'w') as f:
for line in gm_responses:
f.write(line + '\n')
with open('csf.txt', 'w') as f:
for line in csf_responses:
f.write(line + '\n')
shutil.copyfile('wm.txt', getUserPath(lib.app.args.out_wm, False))
shutil.copyfile('gm.txt', getUserPath(lib.app.args.out_gm, False))
shutil.copyfile('csf.txt', getUserPath(lib.app.args.out_csf, False))
# Generate output 4D binary image with voxel selections; RGB as in MSMT-CSD paper
runCommand(
'mrcat csf_mask.mif gm_mask.mif wm_sf_mask.mif voxels.mif -axis 3')
subject_dir = os.path.join(all_subjects_dir, subject_label)
output_mask = os.path.join(subject_dir, 'mask.mif')
lib.app.checkOutputFile(output_mask)
output_fod = os.path.join(subject_dir, 'fod.mif')
lib.app.checkOutputFile(output_fod)
output_gm = os.path.join(subject_dir, 'gm.mif')
lib.app.checkOutputFile(output_gm)
output_csf = os.path.join(subject_dir, 'csf.mif')
lib.app.checkOutputFile(output_csf)
min_voxel_size = 1.25;
if lib.app.args.vox_size:
min_voxel_size = float(lib.app.args.vox_size)
voxel_sizes = getHeaderInfo(os.path.join(subject_dir, 'dwi_preproc.mif'), 'vox').split()
mean_voxel_size = 0.0
for i in range(0,3):
mean_voxel_size = mean_voxel_size + float(voxel_sizes[i]) / 3.0
input_to_csd = os.path.join(subject_dir, 'dwi_preproc.mif')
# Up sample
if mean_voxel_size > min_voxel_size:
runCommand('mrresize -vox ' + str(min_voxel_size) + ' ' + input_to_csd + ' ' + os.path.join(lib.app.tempDir, subject_label + 'dwi_upsampled.mif'))
input_to_csd = os.path.join(lib.app.tempDir, subject_label + 'dwi_upsampled.mif')
# Compute brain mask
runCommand('dwi2mask ' + input_to_csd + ' ' + output_mask + lib.app.mrtrixForce)
# Perform CSD
def execute():
import math, os, shutil
import lib.app
from lib.getHeaderInfo import getHeaderInfo
from lib.getImageStat import getImageStat
from lib.getUserPath import getUserPath
from lib.printMessage import printMessage
from lib.runCommand import runCommand
from lib.warnMessage import warnMessage
from lib.errorMessage import errorMessage
# Get b-values and number of volumes per b-value.
bvalues = [
int(round(float(x)))
for x in getHeaderInfo('dwi.mif', 'shells').split()
]
bvolumes = [
int(x) for x in getHeaderInfo('dwi.mif', 'shellcounts').split()
]
printMessage(
str(len(bvalues)) + ' unique b-value(s) detected: ' +
','.join(map(str, bvalues)) + ' with ' + ','.join(map(str, bvolumes)) +
' volumes.')
if len(bvalues) < 2:
errorMessage('Need at least 2 unique b-values (including b=0).')
# Get lmax information (if provided).
sfwm_lmax = []
if lib.app.args.lmax:
sfwm_lmax = [int(x.strip()) for x in lib.app.args.lmax.split(',')]
if not len(sfwm_lmax) == len(bvalues):
errorMessage('Number of lmax\'s (' + str(len(sfwm_lmax)) +
', as supplied to the -lmax option: ' +
','.join(map(str, sfwm_lmax)) +
') does not match number of unique b-values.')
for l in sfwm_lmax:
if l % 2:
errorMessage(
'Values supplied to the -lmax option must be even.')
if l < 0:
errorMessage(
'Values supplied to the -lmax option must be non-negative.'
)
# Erode (brain) mask.
if lib.app.args.erode > 0:
runCommand('maskfilter mask.mif erode eroded_mask.mif -npass ' +
str(lib.app.args.erode))
else:
runCommand('mrconvert mask.mif eroded_mask.mif -datatype bit')
# Get volumes, compute mean signal and SDM per b-value; compute overall SDM; get rid of erroneous values.
totvolumes = 0
fullsdmcmd = 'mrcalc'
errcmd = 'mrcalc'
zeropath = 'mean_b' + str(bvalues[0]) + '.mif'
for i, b in enumerate(bvalues):
dwipath = 'dwi_b' + str(b) + '.mif'
runCommand('dwiextract dwi.mif -shell ' + str(b) + ' ' + dwipath)
meanpath = 'mean_b' + str(b) + '.mif'
runCommand('mrmath ' + dwipath + ' mean ' + meanpath + ' -axis 3')
errpath = 'err_b' + str(b) + '.mif'
runCommand('mrcalc ' + meanpath + ' -finite ' + meanpath +
' 0 -if 0 -le ' + errpath + ' -datatype bit')
errcmd += ' ' + errpath
if i > 0:
errcmd += ' -add'
sdmpath = 'sdm_b' + str(b) + '.mif'
runCommand('mrcalc ' + zeropath + ' ' + meanpath +
' -divide -log ' + sdmpath)
totvolumes += bvolumes[i]
fullsdmcmd += ' ' + sdmpath + ' ' + str(bvolumes[i]) + ' -mult'
if i > 1:
fullsdmcmd += ' -add'
fullsdmcmd += ' ' + str(totvolumes) + ' -divide full_sdm.mif'
runCommand(fullsdmcmd)
runCommand(
'mrcalc full_sdm.mif -finite full_sdm.mif 0 -if 0 -le err_sdm.mif -datatype bit'
)
errcmd += ' err_sdm.mif -add 0 eroded_mask.mif -if safe_mask.mif -datatype bit'
runCommand(errcmd)
runCommand('mrcalc safe_mask.mif full_sdm.mif 0 -if 10 -min safe_sdm.mif')
# Compute FA and principal eigenvectors; crude WM versus GM-CSF separation based on FA.
runCommand(
'dwi2tensor dwi.mif - -mask safe_mask.mif | tensor2metric - -fa safe_fa.mif -vector safe_vecs.mif -modulate none -mask safe_mask.mif'
)
runCommand('mrcalc safe_mask.mif safe_fa.mif 0 -if ' +
str(lib.app.args.fa) + ' -gt crude_wm.mif -datatype bit')
runCommand(
'mrcalc crude_wm.mif 0 safe_mask.mif -if _crudenonwm.mif -datatype bit'
)
# Crude GM versus CSF separation based on SDM.
crudenonwmmedian = getImageStat('safe_sdm.mif', 'median',
'_crudenonwm.mif')
runCommand(
'mrcalc _crudenonwm.mif safe_sdm.mif ' + str(crudenonwmmedian) +
' -subtract 0 -if - | mrthreshold - - -mask _crudenonwm.mif | mrcalc _crudenonwm.mif - 0 -if crude_csf.mif -datatype bit'
)
runCommand(
'mrcalc crude_csf.mif 0 _crudenonwm.mif -if crude_gm.mif -datatype bit'
)
# Refine WM: remove high SDM outliers.
crudewmmedian = getImageStat('safe_sdm.mif', 'median', 'crude_wm.mif')
runCommand('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' + str(crudewmmedian) +
' -gt _crudewmhigh.mif -datatype bit')
runCommand(
'mrcalc _crudewmhigh.mif 0 crude_wm.mif -if _crudewmlow.mif -datatype bit'
)
crudewmQ1 = float(getImageStat('safe_sdm.mif', 'median',
'_crudewmlow.mif'))
crudewmQ3 = float(
getImageStat('safe_sdm.mif', 'median', '_crudewmhigh.mif'))
crudewmoutlthresh = crudewmQ3 + (crudewmQ3 - crudewmQ1)
runCommand('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' +
str(crudewmoutlthresh) +
' -gt _crudewmoutliers.mif -datatype bit')
runCommand(
'mrcalc _crudewmoutliers.mif 0 crude_wm.mif -if refined_wm.mif -datatype bit'
)
# Refine GM: separate safer GM from partial volumed voxels.
crudegmmedian = getImageStat('safe_sdm.mif', 'median', 'crude_gm.mif')
runCommand('mrcalc crude_gm.mif safe_sdm.mif 0 -if ' + str(crudegmmedian) +
' -gt _crudegmhigh.mif -datatype bit')
runCommand(
'mrcalc _crudegmhigh.mif 0 crude_gm.mif -if _crudegmlow.mif -datatype bit'
)
runCommand(
'mrcalc _crudegmhigh.mif safe_sdm.mif ' + str(crudegmmedian) +
' -subtract 0 -if - | mrthreshold - - -mask _crudegmhigh.mif -invert | mrcalc _crudegmhigh.mif - 0 -if _crudegmhighselect.mif -datatype bit'
)
runCommand(
'mrcalc _crudegmlow.mif safe_sdm.mif ' + str(crudegmmedian) +
' -subtract -neg 0 -if - | mrthreshold - - -mask _crudegmlow.mif -invert | mrcalc _crudegmlow.mif - 0 -if _crudegmlowselect.mif -datatype bit'
)
runCommand(
'mrcalc _crudegmhighselect.mif 1 _crudegmlowselect.mif -if refined_gm.mif -datatype bit'
)
# Refine CSF: recover lost CSF from crude WM SDM outliers, separate safer CSF from partial volumed voxels.
crudecsfmin = getImageStat('safe_sdm.mif', 'min', 'crude_csf.mif')
runCommand('mrcalc _crudewmoutliers.mif safe_sdm.mif 0 -if ' +
str(crudecsfmin) +
' -gt 1 crude_csf.mif -if _crudecsfextra.mif -datatype bit')
runCommand(
'mrcalc _crudecsfextra.mif safe_sdm.mif ' + str(crudecsfmin) +
' -subtract 0 -if - | mrthreshold - - -mask _crudecsfextra.mif | mrcalc _crudecsfextra.mif - 0 -if refined_csf.mif -datatype bit'
)
# Get final voxels for single-fibre WM response function estimation from WM using 'tournier' algorithm.
refwmcount = float(
getImageStat('refined_wm.mif', 'count', 'refined_wm.mif'))
voxsfwmcount = int(round(refwmcount * lib.app.args.sfwm / 100.0))
printMessage('Running \'tournier\' algorithm to select ' +
str(voxsfwmcount) + ' single-fibre WM voxels.')
cleanopt = ''
if not lib.app.cleanup:
cleanopt = ' -nocleanup'
runCommand(
'dwi2response tournier dwi.mif _respsfwmss.txt -sf_voxels ' +
str(voxsfwmcount) + ' -iter_voxels ' + str(voxsfwmcount * 10) +
' -mask refined_wm.mif -voxels voxels_sfwm.mif -quiet -tempdir ' +
lib.app.tempDir + cleanopt)
# Get final voxels for GM response function estimation from GM.
refgmmedian = getImageStat('safe_sdm.mif', 'median', 'refined_gm.mif')
runCommand('mrcalc refined_gm.mif safe_sdm.mif 0 -if ' + str(refgmmedian) +
' -gt _refinedgmhigh.mif -datatype bit')
runCommand(
'mrcalc _refinedgmhigh.mif 0 refined_gm.mif -if _refinedgmlow.mif -datatype bit'
)
refgmhighcount = float(
getImageStat('_refinedgmhigh.mif', 'count', '_refinedgmhigh.mif'))
refgmlowcount = float(
getImageStat('_refinedgmlow.mif', 'count', '_refinedgmlow.mif'))
voxgmhighcount = int(round(refgmhighcount * lib.app.args.gm / 100.0))
voxgmlowcount = int(round(refgmlowcount * lib.app.args.gm / 100.0))
runCommand(
'mrcalc _refinedgmhigh.mif safe_sdm.mif 0 -if - | mrthreshold - - -bottom '
+ str(voxgmhighcount) +
' -ignorezero | mrcalc _refinedgmhigh.mif - 0 -if _refinedgmhighselect.mif -datatype bit'
)
runCommand(
'mrcalc _refinedgmlow.mif safe_sdm.mif 0 -if - | mrthreshold - - -top '
+ str(voxgmlowcount) +
' -ignorezero | mrcalc _refinedgmlow.mif - 0 -if _refinedgmlowselect.mif -datatype bit'
)
runCommand(
'mrcalc _refinedgmhighselect.mif 1 _refinedgmlowselect.mif -if voxels_gm.mif -datatype bit'
)
# Get final voxels for CSF response function estimation from CSF.
refcsfcount = float(
getImageStat('refined_csf.mif', 'count', 'refined_csf.mif'))
voxcsfcount = int(round(refcsfcount * lib.app.args.csf / 100.0))
runCommand(
'mrcalc refined_csf.mif safe_sdm.mif 0 -if - | mrthreshold - - -top ' +
str(voxcsfcount) +
' -ignorezero | mrcalc refined_csf.mif - 0 -if voxels_csf.mif -datatype bit'
)
# Show summary of voxels counts.
textarrow = ' --> '
printMessage('Summary of voxel counts:')
printMessage(
'Mask: ' + str(int(getImageStat('mask.mif', 'count', 'mask.mif'))) +
textarrow +
str(int(getImageStat('eroded_mask.mif', 'count', 'eroded_mask.mif'))) +
textarrow +
str(int(getImageStat('safe_mask.mif', 'count', 'safe_mask.mif'))))
printMessage(
'WM: ' +
str(int(getImageStat('crude_wm.mif', 'count', 'crude_wm.mif'))) +
textarrow +
str(int(getImageStat('refined_wm.mif', 'count', 'refined_wm.mif'))) +
textarrow +
str(int(getImageStat('voxels_sfwm.mif', 'count', 'voxels_sfwm.mif'))) +
' (SF)')
printMessage(
'GM: ' +
str(int(getImageStat('crude_gm.mif', 'count', 'crude_gm.mif'))) +
textarrow +
str(int(getImageStat('refined_gm.mif', 'count', 'refined_gm.mif'))) +
textarrow +
str(int(getImageStat('voxels_gm.mif', 'count', 'voxels_gm.mif'))))
printMessage(
'CSF: ' +
str(int(getImageStat('crude_csf.mif', 'count', 'crude_csf.mif'))) +
textarrow +
str(int(getImageStat('refined_csf.mif', 'count', 'refined_csf.mif'))) +
textarrow +
str(int(getImageStat('voxels_csf.mif', 'count', 'voxels_csf.mif'))))
# Generate single-fibre WM, GM and CSF responses
sfwm_responses = []
gm_responses = []
csf_responses = []
max_length = 0
for index, b in enumerate(bvalues):
dwipath = 'dwi_b' + str(b) + '.mif'
this_b_lmax_option = ''
if sfwm_lmax:
this_b_lmax_option = ' -lmax ' + str(sfwm_lmax[index])
runCommand(
'amp2sh ' + dwipath +
' - | sh2response - voxels_sfwm.mif safe_vecs.mif _respsfwmb' +
str(b) + '.txt' + this_b_lmax_option)
sfwm_response = open('_respsfwmb' + str(b) + '.txt',
'r').read().split()
sfwm_responses.append(sfwm_response)
max_length = max(max_length, len(sfwm_response))
meanpath = 'mean_b' + str(b) + '.mif'
gm_mean = float(getImageStat(meanpath, 'mean', 'voxels_gm.mif'))
csf_mean = float(getImageStat(meanpath, 'mean', 'voxels_csf.mif'))
gm_responses.append(str(gm_mean * math.sqrt(4.0 * math.pi)))
csf_responses.append(str(csf_mean * math.sqrt(4.0 * math.pi)))
with open('response_sfwm.txt', 'w') as f:
for line in sfwm_responses:
line += ['0'] * (max_length - len(line))
f.write(' '.join(line) + '\n')
with open('response_gm.txt', 'w') as f:
for line in gm_responses:
f.write(line + '\n')
with open('response_csf.txt', 'w') as f:
for line in csf_responses:
f.write(line + '\n')
shutil.copyfile('response_sfwm.txt',
getUserPath(lib.app.args.out_sfwm, False))
shutil.copyfile('response_gm.txt', getUserPath(lib.app.args.out_gm, False))
shutil.copyfile('response_csf.txt', getUserPath(lib.app.args.out_csf,
False))
# Generate 4D binary images with voxel selection at major stages in algorithm (RGB as in MSMT-CSD paper).
runCommand(
'mrcat crude_csf.mif crude_gm.mif crude_wm.mif crude.mif -axis 3')
runCommand(
'mrcat refined_csf.mif refined_gm.mif refined_wm.mif refined.mif -axis 3'
)
runCommand(
'mrcat voxels_csf.mif voxels_gm.mif voxels_sfwm.mif voxels.mif -axis 3'
)
subject_dir = os.path.join(all_subjects_dir, subject_label)
output_mask = os.path.join(subject_dir, 'mask.mif')
lib.app.checkOutputFile(output_mask)
output_fod = os.path.join(subject_dir, 'fod.mif')
lib.app.checkOutputFile(output_fod)
output_gm = os.path.join(subject_dir, 'gm.mif')
lib.app.checkOutputFile(output_gm)
output_csf = os.path.join(subject_dir, 'csf.mif')
lib.app.checkOutputFile(output_csf)
min_voxel_size = 1.25
if lib.app.args.vox_size:
min_voxel_size = float(lib.app.args.vox_size)
voxel_sizes = getHeaderInfo(
os.path.join(subject_dir, 'dwi_preproc.mif'), 'vox').split()
mean_voxel_size = 0.0
for i in range(0, 3):
mean_voxel_size = mean_voxel_size + float(voxel_sizes[i]) / 3.0
input_to_csd = os.path.join(subject_dir, 'dwi_preproc.mif')
# Up sample
if mean_voxel_size > min_voxel_size:
runCommand('mrresize -vox ' + str(min_voxel_size) + ' ' +
input_to_csd + ' ' +
os.path.join(lib.app.tempDir, subject_label +
'dwi_upsampled.mif'))
input_to_csd = os.path.join(lib.app.tempDir,
subject_label + 'dwi_upsampled.mif')