def execute(): #pylint: disable=unused-variable
shells = [ int(round(float(x))) for x in image.mrinfo('dwi.mif', 'shell_bvalues').split() ]
# Get lmax information (if provided)
lmax = [ ]
if app.ARGS.lmax:
lmax = [ int(x.strip()) for x in app.ARGS.lmax.split(',') ]
if not len(lmax) == len(shells):
raise MRtrixError('Number of manually-defined lmax\'s (' + str(len(lmax)) + ') does not match number of b-value shells (' + str(len(shells)) + ')')
for shell_l in lmax:
if shell_l % 2:
raise MRtrixError('Values for lmax must be even')
if shell_l < 0:
raise MRtrixError('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'):
run.command('dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif')
# Get response function
bvalues_option = ' -shells ' + ','.join(map(str,shells))
lmax_option = ''
if lmax:
lmax_option = ' -lmax ' + ','.join(map(str,lmax))
run.command('amp2response dwi.mif in_voxels.mif dirs.mif response.txt' + bvalues_option + lmax_option)
run.function(shutil.copyfile, 'response.txt', path.from_user(app.ARGS.output, False))
if app.ARGS.voxels:
run.command('mrconvert in_voxels.mif ' + path.from_user(app.ARGS.voxels), mrconvert_keyval=path.from_user(app.ARGS.input, False), force=app.FORCE_OVERWRITE)
def execute(): #pylint: disable=unused-variable
# Generate the images related to each tissue
run.command('mrconvert input.mif -coord 3 1 CSF.mif')
run.command('mrconvert input.mif -coord 3 2 cGM.mif')
run.command('mrconvert input.mif -coord 3 3 cWM.mif')
run.command('mrconvert input.mif -coord 3 4 sGM.mif')
# Combine WM and subcortical WM into a unique WM image
run.command(
'mrconvert input.mif - -coord 3 3,5 | mrmath - sum WM.mif -axis 3')
# Create an empty lesion image
run.command('mrcalc WM.mif 0 -mul lsn.mif')
# Convert into the 5tt format
run.command('mrcat cGM.mif sGM.mif WM.mif CSF.mif lsn.mif 5tt.mif -axis 3')
if app.ARGS.nocrop:
run.function(os.rename, '5tt.mif', 'result.mif')
else:
run.command(
'mrmath 5tt.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrgrid 5tt.mif crop result.mif -mask -'
)
run.command('mrconvert result.mif ' + path.from_user(app.ARGS.output),
mrconvert_keyval=path.from_user(app.ARGS.input, False),
force=app.FORCE_OVERWRITE)
def execute(): #pylint: disable=unused-variable
import shutil
from mrtrix3 import app, image, path, run
bvalues = [
int(round(float(x)))
for x in image.mrinfo('dwi.mif', 'shell_bvalues').split()
]
if len(bvalues) < 2:
app.error('Need at least 2 unique b-values (including b=0).')
lmax_option = ''
if app.args.lmax:
lmax_option = ' -lmax ' + app.args.lmax
if not app.args.mask:
run.command('maskfilter mask.mif erode mask_eroded.mif -npass ' +
str(app.args.erode))
mask_path = 'mask_eroded.mif'
else:
mask_path = 'mask.mif'
run.command('dwi2tensor dwi.mif -mask ' + mask_path + ' tensor.mif')
run.command(
'tensor2metric tensor.mif -fa fa.mif -vector vector.mif -mask ' +
mask_path)
if app.args.threshold:
run.command('mrthreshold fa.mif voxels.mif -abs ' +
str(app.args.threshold))
else:
run.command('mrthreshold fa.mif voxels.mif -top ' +
str(app.args.number))
run.command(
'dwiextract dwi.mif - -singleshell -no_bzero | amp2response - voxels.mif vector.mif response.txt'
+ lmax_option)
run.function(shutil.copyfile, 'response.txt',
path.fromUser(app.args.output, False))
def execute(): #pylint: disable=unused-variable
import os, shutil
from mrtrix3 import app, image, path, run
shells = [ int(round(float(x))) for x in image.mrinfo('dwi.mif', 'shell_bvalues').split() ]
# Get lmax information (if provided)
lmax = [ ]
if app.args.lmax:
lmax = [ int(x.strip()) for x in app.args.lmax.split(',') ]
if not len(lmax) == len(shells):
app.error('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:
app.error('Values for lmax must be even')
if l<0:
app.error('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'):
run.command('dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif')
# Get response function
bvalues_option = ' -shells ' + ','.join(map(str,shells))
lmax_option = ''
if lmax:
lmax_option = ' -lmax ' + ','.join(map(str,lmax))
run.command('amp2response dwi.mif in_voxels.mif dirs.mif response.txt' + bvalues_option + lmax_option)
run.function(shutil.copyfile, 'response.txt', path.fromUser(app.args.output, False))
run.function(shutil.copyfile, 'in_voxels.mif', 'voxels.mif')
def getInputs():
import os, shutil
from mrtrix3 import app, path, run
run.command('mrconvert ' + path.fromUser(app.args.input, True) + ' ' +
path.toTemp('input.mif', True))
if app.args.lut:
run.function(shutil.copyfile, path.fromUser(app.args.lut, False),
path.toTemp('LUT.txt', False))
def execute():
import os, shutil
from mrtrix3 import app, image, path, run
shells = [
int(round(float(x)))
for x in image.headerField('dwi.mif', 'shells').split()
]
# Get lmax information (if provided)
lmax = []
if app.args.lmax:
lmax = [int(x.strip()) for x in app.args.lmax.split(',')]
if not len(lmax) == len(shells):
app.error('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:
app.error('Values for lmax must be even')
if l < 0:
app.error('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'):
run.command(
'dwi2tensor dwi.mif - -mask in_voxels.mif | tensor2metric - -vector dirs.mif'
)
# Get response function
bvalues_option = ' -shell ' + ','.join(map(str, shells))
lmax_option = ''
if lmax:
lmax_option = ' -lmax ' + ','.join(map(str, lmax))
run.command('amp2response dwi.mif in_voxels.mif dirs.mif response.txt' +
bvalues_option + lmax_option)
run.function(shutil.copyfile, 'response.txt',
path.fromUser(app.args.output, False))
run.function(shutil.copyfile, 'in_voxels.mif', 'voxels.mif')
def execute(): #pylint: disable=unused-variable
bvalues = [ int(round(float(x))) for x in image.mrinfo('dwi.mif', 'shell_bvalues').split() ]
if len(bvalues) < 2:
raise MRtrixError('Need at least 2 unique b-values (including b=0).')
lmax_option = ''
if app.ARGS.lmax:
lmax_option = ' -lmax ' + app.ARGS.lmax
if not app.ARGS.mask:
run.command('maskfilter mask.mif erode mask_eroded.mif -npass ' + str(app.ARGS.erode))
mask_path = 'mask_eroded.mif'
else:
mask_path = 'mask.mif'
run.command('dwi2tensor dwi.mif -mask ' + mask_path + ' tensor.mif')
run.command('tensor2metric tensor.mif -fa fa.mif -vector vector.mif -mask ' + mask_path)
if app.ARGS.threshold:
run.command('mrthreshold fa.mif voxels.mif -abs ' + str(app.ARGS.threshold))
else:
run.command('mrthreshold fa.mif voxels.mif -top ' + str(app.ARGS.number))
run.command('dwiextract dwi.mif - -singleshell -no_bzero | amp2response - voxels.mif vector.mif response.txt' + lmax_option)
run.function(shutil.copyfile, 'response.txt', path.from_user(app.ARGS.output, False))
if app.ARGS.voxels:
run.command('mrconvert voxels.mif ' + path.from_user(app.ARGS.voxels), mrconvert_keyval=path.from_user(app.ARGS.input, False), force=app.FORCE_OVERWRITE)
def execute(): #pylint: disable=unused-variable
import shutil
from mrtrix3 import app, image, path, run
bvalues = [ int(round(float(x))) for x in image.mrinfo('dwi.mif', 'shell_bvalues').split() ]
if len(bvalues) < 2:
app.error('Need at least 2 unique b-values (including b=0).')
lmax_option = ''
if app.args.lmax:
lmax_option = ' -lmax ' + app.args.lmax
if not app.args.mask:
run.command('maskfilter mask.mif erode mask_eroded.mif -npass ' + str(app.args.erode))
mask_path = 'mask_eroded.mif'
else:
mask_path = 'mask.mif'
run.command('dwi2tensor dwi.mif -mask ' + mask_path + ' tensor.mif')
run.command('tensor2metric tensor.mif -fa fa.mif -vector vector.mif -mask ' + mask_path)
if app.args.threshold:
run.command('mrthreshold fa.mif voxels.mif -abs ' + str(app.args.threshold))
else:
run.command('mrthreshold fa.mif voxels.mif -top ' + str(app.args.number))
run.command('dwiextract dwi.mif - -singleshell -no_bzero | amp2response - voxels.mif vector.mif response.txt' + lmax_option)
run.function(shutil.copyfile, 'response.txt', path.fromUser(app.args.output, False))
') does not match input image (' + str(num_volumes) +
' volumes); check your input data')
if app.args.extent:
extent = app.args.extent
else:
extent = '5,5,5'
run.command('mrconvert dwi.mif working.mif')
# denoising
if app.args.denoise:
print("...Beginning denoising")
run.command('dwidenoise -extent ' + extent +
' -noise fullnoisemap.mif working.mif dwidn.mif')
run.function(os.remove, 'working.mif')
run.command('mrconvert dwidn.mif working.mif')
# gibbs artifact correction
if app.args.degibbs:
print("...Beginning degibbsing")
run.command('mrdegibbs -nshifts 20 -minW 1 -maxW 3 working.mif dwigc.mif')
run.function(os.remove, 'working.mif')
run.command('mrconvert dwigc.mif working.mif')
# pre-eddy alignment for multiple input series
if app.args.prealign:
if len(DWInlist) != 1:
miflist = []
for idx, i in enumerate(DWInlist):
run.command('mrconvert -coord 3 ' + idxlist[idx] +
def runGroup(output_dir):
# Check presence of all required input files before proceeding
# Pre-calculate paths of all files since many will be used in more than one location
class subjectPaths(object):
def __init__(self, label):
self.in_dwi = os.path.join(output_dir, label, 'dwi',
label + '_dwi.nii.gz')
self.in_bvec = os.path.join(output_dir, label, 'dwi',
label + '_dwi.bvec')
self.in_bval = os.path.join(output_dir, label, 'dwi',
label + '_dwi.bval')
self.in_json = os.path.join(output_dir, label, 'dwi',
label + '_dwi.json')
self.in_rf = os.path.join(output_dir, label, 'dwi',
label + '_response.txt')
self.in_connectome = os.path.join(output_dir, label, 'connectome',
label + '_connectome.csv')
self.in_mu = os.path.join(output_dir, label, 'connectome',
label + '_mu.txt')
for entry in vars(self).values():
if not os.path.exists(entry):
app.error(
'Unable to find critical subject data (expected location: '
+ entry + ')')
with open(self.in_mu, 'r') as f:
self.mu = float(f.read())
self.RF = []
with open(self.in_rf, 'r') as f:
for line in f:
self.RF.append([float(v) for v in line.split()])
self.temp_mask = os.path.join('masks', label + '.mif')
self.temp_fa = os.path.join('images', label + '.mif')
self.temp_bzero = os.path.join('bzeros', label + '.mif')
self.temp_warp = os.path.join('warps', label + '.mif')
self.temp_voxels = os.path.join('voxels', label + '.mif')
self.median_bzero = 0.0
self.dwiintensitynorm_factor = 1.0
self.RF_multiplier = 1.0
self.global_multiplier = 1.0
self.temp_connectome = os.path.join('connectomes', label + '.csv')
self.out_scale_bzero = os.path.join(
output_dir, label, 'connectome',
label + '_scalefactor_bzero.csv')
self.out_scale_RF = os.path.join(
output_dir, label, 'connectome',
label + '_scalefactor_response.csv')
self.out_connectome = os.path.join(
output_dir, label, 'connectome',
label + '_connectome_scaled.csv')
self.label = label
subject_list = [
'sub-' + sub_dir.split("-")[-1]
for sub_dir in glob.glob(os.path.join(output_dir, 'sub-*'))
]
if not subject_list:
app.error(
'No processed subject data found in output directory for group analysis'
)
subjects = []
for label in subject_list:
subjects.append(subjectPaths(label))
app.makeTempDir()
app.gotoTempDir()
# First pass through subject data in group analysis:
# - Grab DWI data (written back from single-subject analysis back into BIDS format)
# - Generate mask and FA images to be used in populate template generation
# - Generate mean b=0 image for each subject for later use
progress = app.progressBar('Importing and preparing subject data',
len(subjects))
run.function(os.makedirs, 'bzeros')
run.function(os.makedirs, 'images')
run.function(os.makedirs, 'masks')
for s in subjects:
grad_import_option = ' -fslgrad ' + s.in_bvec + ' ' + s.in_bval
run.command('dwi2mask ' + s.in_dwi + ' ' + s.temp_mask +
grad_import_option)
run.command('dwi2tensor ' + s.in_dwi + ' - -mask ' + s.temp_mask +
grad_import_option + ' | tensor2metric - -fa ' + s.temp_fa)
run.command('dwiextract ' + s.in_dwi + grad_import_option +
' - -bzero | mrmath - mean ' + s.temp_bzero + ' -axis 3')
progress.increment()
progress.done()
# First group-level calculation: Generate the population FA template
app.console(
'Generating population template for inter-subject intensity normalisation WM mask derivation'
)
run.command(
'population_template images -mask_dir masks -warp_dir warps template.mif '
'-type rigid_affine_nonlinear -rigid_scale 0.25,0.5,0.8,1.0 -affine_scale 0.7,0.8,1.0,1.0 '
'-nl_scale 0.5,0.75,1.0,1.0,1.0 -nl_niter 5,5,5,5,5 -linear_no_pause')
file.delTemporary('images')
file.delTemporary('masks')
# Second pass through subject data in group analysis:
# - Warp template FA image back to subject space & threshold to define a WM mask in subject space
# - Calculate the median subject b=0 value within this mask
# - Store this in a file, and contribute to calculation of the mean of these values across subjects
# - Contribute to the group average response function
progress = app.progressBar(
'Generating group-average response function and intensity normalisation factors',
len(subjects) + 1)
run.function(os.makedirs, 'voxels')
sum_median_bzero = 0.0
sum_RF = []
for s in subjects:
run.command('mrtransform template.mif -warp_full ' + s.temp_warp +
' - -from 2 -template ' + s.temp_bzero + ' | '
'mrthreshold - ' + s.temp_voxels + ' -abs 0.4')
s.median_bzero = float(
image.statistic(s.temp_bzero, 'median', '-mask ' + s.temp_voxels))
file.delTemporary(s.temp_bzero)
file.delTemporary(s.temp_voxels)
file.delTemporary(s.temp_warp)
sum_median_bzero += s.median_bzero
if sum_RF:
sum_RF = [[a + b for a, b in zip(one, two)]
for one, two in zip(sum_RF, s.RF)]
else:
sum_RF = s.RF
progress.increment()
file.delTemporary('bzeros')
file.delTemporary('voxels')
file.delTemporary('warps')
progress.done()
# Second group-level calculation:
# - Calculate the mean of median b=0 values
# - Calculate the mean response function, and extract the l=0 values from it
mean_median_bzero = sum_median_bzero / len(subjects)
mean_RF = [[v / len(subjects) for v in line] for line in sum_RF]
mean_RF_lzero = [line[0] for line in mean_RF]
# Third pass through subject data in group analysis:
# - Scale the connectome strengths:
# - Multiply by SIFT proportionality coefficient mu
# - Multiply by (mean median b=0) / (subject median b=0)
# - Multiply by (subject RF size) / (mean RF size)
# (needs to account for multi-shell data)
# - Write the result to file
progress = app.progressBar(
'Applying normalisation scaling to subject connectomes', len(subjects))
run.function(os.makedirs, 'connectomes')
for s in subjects:
RF_lzero = [line[0] for line in s.RF]
s.RF_multiplier = 1.0
for (mean, subj) in zip(mean_RF_lzero, RF_lzero):
s.RF_multiplier = s.RF_multiplier * subj / mean
# Don't want to be scaling connectome independently for differences in RF l=0 terms across all shells;
# use the geometric mean of the per-shell scale factors
s.RF_multiplier = math.pow(s.RF_multiplier, 1.0 / len(mean_RF_lzero))
s.bzero_multiplier = mean_median_bzero / s.median_bzero
s.global_multiplier = s.mu * s.bzero_multiplier * s.RF_multiplier
connectome = []
with open(s.in_connectome, 'r') as f:
for line in f:
connectome.append([float(v) for v in line.split()])
with open(s.temp_connectome, 'w') as f:
for line in connectome:
f.write(' '.join([str(v * s.global_multiplier)
for v in line]) + '\n')
progress.increment()
progress.done()
# Third group-level calculation: Generate the group mean connectome
# For any higher-level analysis (e.g. NBSE, computing connectome global measures, etc.),
# trying to incorporate such analysis into this particular pipeline script is likely to
# overly complicate the interface, and not actually provide much in terms of
# convenience / reproducibility guarantees. The primary functionality of this group-level
# analysis is therefore to achieve inter-subject connection density normalisation; users
# then have the flexibility to subsequently analyse the data however they choose (ideally
# based on subject classification data provided with the BIDS-compliant dataset).
progress = app.progressBar('Calculating group mean connectome',
len(subjects) + 1)
mean_connectome = []
for s in subjects:
connectome = []
with open(s.temp_connectome, 'r') as f:
for line in f:
connectome.append([float(v) for v in line.split()])
if mean_connectome:
mean_connectome = [[c1 + c2 for c1, c2 in zip(r1, r2)]
for r1, r2 in zip(mean_connectome, connectome)]
else:
mean_connectome = connectome
progress.increment()
mean_connectome = [[v / len(subjects) for v in row]
for row in mean_connectome]
progress.done()
# Write results of interest back to the output directory;
# both per-subject and group information
progress = app.progressBar('Writing results to output directory',
len(subjects) + 2)
for s in subjects:
run.function(shutil.copyfile, s.temp_connectome, s.out_connectome)
with open(s.out_scale_bzero, 'w') as f:
f.write(str(s.bzero_multiplier))
with open(s.out_scale_RF, 'w') as f:
f.write(str(s.RF_multiplier))
progress.increment()
with open(os.path.join(output_dir, 'mean_response.txt'), 'w') as f:
for row in mean_RF:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.increment()
with open(os.path.join(output_dir, 'mean_connectome.csv'), 'w') as f:
for row in mean_connectome:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.done()
def execute(): #pylint: disable=unused-variable
import os, shutil
from mrtrix3 import app, file, image, path, run #pylint: disable=redefined-builtin
lmax_option = ''
if app.args.lmax:
lmax_option = ' -lmax ' + app.args.lmax
if app.args.max_iters < 2:
app.error('Number of iterations must be at least 2')
for iteration in range(0, app.args.max_iters):
prefix = 'iter' + str(iteration) + '_'
if iteration == 0:
RF_in_path = 'init_RF.txt'
mask_in_path = 'mask.mif'
init_RF = '1 -1 1'
with open(RF_in_path, 'w') as f:
f.write(init_RF)
iter_lmax_option = ' -lmax 4'
else:
RF_in_path = 'iter' + str(iteration-1) + '_RF.txt'
mask_in_path = 'iter' + str(iteration-1) + '_SF_dilated.mif'
iter_lmax_option = lmax_option
# Run CSD
run.command('dwi2fod csd dwi.mif ' + RF_in_path + ' ' + prefix + 'FOD.mif -mask ' + mask_in_path + iter_lmax_option)
# Get amplitudes of two largest peaks, and direction of largest
run.command('fod2fixel ' + prefix + 'FOD.mif ' + prefix + 'fixel -peak peaks.mif -mask ' + mask_in_path + ' -fmls_no_thresholds')
file.delTemporary(prefix + 'FOD.mif')
if iteration:
file.delTemporary(mask_in_path)
run.command('fixel2voxel ' + prefix + 'fixel/peaks.mif split_data ' + prefix + 'amps.mif -number 2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix + 'first_peaks.mif -coord 3 0 -axes 0,1,2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix + 'second_peaks.mif -coord 3 1 -axes 0,1,2')
file.delTemporary(prefix + 'amps.mif')
run.command('fixel2voxel ' + prefix + 'fixel/directions.mif split_dir ' + prefix + 'all_dirs.mif -number 1')
file.delTemporary(prefix + 'fixel')
run.command('mrconvert ' + prefix + 'all_dirs.mif ' + prefix + 'first_dir.mif -coord 3 0:2')
file.delTemporary(prefix + 'all_dirs.mif')
# Calculate the 'cost function' Donald derived for selecting single-fibre voxels
# https://github.com/MRtrix3/mrtrix3/pull/426
# sqrt(|peak1|) * (1 - |peak2| / |peak1|)^2
run.command('mrcalc ' + prefix + 'first_peaks.mif -sqrt 1 ' + prefix + 'second_peaks.mif ' + prefix + 'first_peaks.mif -div -sub 2 -pow -mult '+ prefix + 'CF.mif')
file.delTemporary(prefix + 'first_peaks.mif')
file.delTemporary(prefix + 'second_peaks.mif')
# Select the top-ranked voxels
run.command('mrthreshold ' + prefix + 'CF.mif -top ' + str(app.args.sf_voxels) + ' ' + prefix + 'SF.mif')
# Generate a new response function based on this selection
run.command('amp2response dwi.mif ' + prefix + 'SF.mif ' + prefix + 'first_dir.mif ' + prefix + 'RF.txt' + iter_lmax_option)
file.delTemporary(prefix + 'first_dir.mif')
# Should we terminate?
if iteration > 0:
run.command('mrcalc ' + prefix + 'SF.mif iter' + str(iteration-1) + '_SF.mif -sub ' + prefix + 'SF_diff.mif')
file.delTemporary('iter' + str(iteration-1) + '_SF.mif')
max_diff = image.statistic(prefix + 'SF_diff.mif', 'max')
file.delTemporary(prefix + 'SF_diff.mif')
if int(max_diff) == 0:
app.console('Convergence of SF voxel selection detected at iteration ' + str(iteration))
file.delTemporary(prefix + 'CF.mif')
run.function(shutil.copyfile, prefix + 'RF.txt', 'response.txt')
run.function(shutil.move, prefix + 'SF.mif', 'voxels.mif')
break
# Select a greater number of top single-fibre voxels, and dilate (within bounds of initial mask);
# these are the voxels that will be re-tested in the next iteration
run.command('mrthreshold ' + prefix + 'CF.mif -top ' + str(app.args.iter_voxels) + ' - | maskfilter - dilate - -npass ' + str(app.args.dilate) + ' | mrcalc mask.mif - -mult ' + prefix + 'SF_dilated.mif')
file.delTemporary(prefix + 'CF.mif')
# Commence the next iteration
# If terminating due to running out of iterations, still need to put the results in the appropriate location
if not os.path.exists('response.txt'):
app.console('Exiting after maximum ' + str(app.args.max_iters) + ' iterations')
run.function(shutil.copyfile, 'iter' + str(app.args.max_iters-1) + '_RF.txt', 'response.txt')
run.function(shutil.move, 'iter' + str(app.args.max_iters-1) + '_SF.mif', 'voxels.mif')
run.function(shutil.copyfile, 'response.txt', path.fromUser(app.args.output, False))
def getInputs(): #pylint: disable=unused-variable
import shutil
from mrtrix3 import app, path, run
run.command('mrconvert ' + path.fromUser(app.args.input, True) + ' ' + path.toTemp('input.mif', True))
if app.args.lut:
run.function(shutil.copyfile, path.fromUser(app.args.lut, False), path.toTemp('LUT.txt', False))
def execute(): #pylint: disable=unused-variable
import os, shutil
from mrtrix3 import app, file, image, path, run #pylint: disable=redefined-builtin
lmax_option = ''
if app.args.lmax:
lmax_option = ' -lmax ' + app.args.lmax
if app.args.max_iters < 2:
app.error('Number of iterations must be at least 2')
for iteration in range(0, app.args.max_iters):
prefix = 'iter' + str(iteration) + '_'
if iteration == 0:
RF_in_path = 'init_RF.txt'
mask_in_path = 'mask.mif'
init_RF = '1 -1 1'
with open(RF_in_path, 'w') as f:
f.write(init_RF)
iter_lmax_option = ' -lmax 4'
else:
RF_in_path = 'iter' + str(iteration - 1) + '_RF.txt'
mask_in_path = 'iter' + str(iteration - 1) + '_SF_dilated.mif'
iter_lmax_option = lmax_option
# Run CSD
run.command('dwi2fod csd dwi.mif ' + RF_in_path + ' ' + prefix +
'FOD.mif -mask ' + mask_in_path + iter_lmax_option)
# Get amplitudes of two largest peaks, and direction of largest
run.command('fod2fixel ' + prefix + 'FOD.mif ' + prefix +
'fixel -peak peaks.mif -mask ' + mask_in_path +
' -fmls_no_thresholds')
file.delTemporary(prefix + 'FOD.mif')
if iteration:
file.delTemporary(mask_in_path)
run.command('fixel2voxel ' + prefix + 'fixel/peaks.mif split_data ' +
prefix + 'amps.mif -number 2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'first_peaks.mif -coord 3 0 -axes 0,1,2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'second_peaks.mif -coord 3 1 -axes 0,1,2')
file.delTemporary(prefix + 'amps.mif')
run.command('fixel2voxel ' + prefix +
'fixel/directions.mif split_dir ' + prefix +
'all_dirs.mif -number 1')
file.delTemporary(prefix + 'fixel')
run.command('mrconvert ' + prefix + 'all_dirs.mif ' + prefix +
'first_dir.mif -coord 3 0:2')
file.delTemporary(prefix + 'all_dirs.mif')
# Calculate the 'cost function' Donald derived for selecting single-fibre voxels
# https://github.com/MRtrix3/mrtrix3/pull/426
# sqrt(|peak1|) * (1 - |peak2| / |peak1|)^2
run.command('mrcalc ' + prefix + 'first_peaks.mif -sqrt 1 ' + prefix +
'second_peaks.mif ' + prefix +
'first_peaks.mif -div -sub 2 -pow -mult ' + prefix +
'CF.mif')
file.delTemporary(prefix + 'first_peaks.mif')
file.delTemporary(prefix + 'second_peaks.mif')
# Select the top-ranked voxels
run.command('mrthreshold ' + prefix + 'CF.mif -top ' +
str(app.args.sf_voxels) + ' ' + prefix + 'SF.mif')
# Generate a new response function based on this selection
run.command('amp2response dwi.mif ' + prefix + 'SF.mif ' + prefix +
'first_dir.mif ' + prefix + 'RF.txt' + iter_lmax_option)
file.delTemporary(prefix + 'first_dir.mif')
# Should we terminate?
if iteration > 0:
run.command('mrcalc ' + prefix + 'SF.mif iter' +
str(iteration - 1) + '_SF.mif -sub ' + prefix +
'SF_diff.mif')
file.delTemporary('iter' + str(iteration - 1) + '_SF.mif')
max_diff = image.statistic(prefix + 'SF_diff.mif', 'max')
file.delTemporary(prefix + 'SF_diff.mif')
if int(max_diff) == 0:
app.console(
'Convergence of SF voxel selection detected at iteration '
+ str(iteration))
file.delTemporary(prefix + 'CF.mif')
run.function(shutil.copyfile, prefix + 'RF.txt',
'response.txt')
run.function(shutil.move, prefix + 'SF.mif', 'voxels.mif')
break
# Select a greater number of top single-fibre voxels, and dilate (within bounds of initial mask);
# these are the voxels that will be re-tested in the next iteration
run.command('mrthreshold ' + prefix + 'CF.mif -top ' +
str(app.args.iter_voxels) +
' - | maskfilter - dilate - -npass ' +
str(app.args.dilate) + ' | mrcalc mask.mif - -mult ' +
prefix + 'SF_dilated.mif')
file.delTemporary(prefix + 'CF.mif')
# Commence the next iteration
# If terminating due to running out of iterations, still need to put the results in the appropriate location
if not os.path.exists('response.txt'):
app.console('Exiting after maximum ' + str(app.args.max_iters) +
' iterations')
run.function(shutil.copyfile,
'iter' + str(app.args.max_iters - 1) + '_RF.txt',
'response.txt')
run.function(shutil.move,
'iter' + str(app.args.max_iters - 1) + '_SF.mif',
'voxels.mif')
run.function(shutil.copyfile, 'response.txt',
path.fromUser(app.args.output, False))
def function(self, func, *args, **kwargs):
from mrtrix3 import run #pylint: disable=import-outside-toplevel
assert self.valid
run.function(func, *args, **kwargs)
self._increment()
def execute(): #pylint: disable=unused-variable
# 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
verification_text = ''
try:
verification_text = run.command('5ttcheck 5tt.mif').stderr
except run.MRtrixCmdError as except_5ttcheck:
verification_text = except_5ttcheck.stderr
if 'WARNING' in verification_text or 'ERROR' in verification_text:
app.warn('Command 5ttcheck indicates problems with provided input 5TT image \'' + app.ARGS.in_5tt + '\':')
for line in verification_text.splitlines():
app.warn(line)
app.warn('These may or may not interfere with the dwi2response msmt_5tt script')
# Get shell information
shells = [ int(round(float(x))) for x in image.mrinfo('dwi.mif', 'shell_bvalues').split() ]
if len(shells) < 3:
app.warn('Less than three b-values; response functions will not be applicable in resolving three tissues using MSMT-CSD algorithm')
# Get lmax information (if provided)
wm_lmax = [ ]
if app.ARGS.lmax:
wm_lmax = [ int(x.strip()) for x in app.ARGS.lmax.split(',') ]
if not len(wm_lmax) == len(shells):
raise MRtrixError('Number of manually-defined lmax\'s (' + str(len(wm_lmax)) + ') does not match number of b-values (' + str(len(shells)) + ')')
for shell_l in wm_lmax:
if shell_l % 2:
raise MRtrixError('Values for lmax must be even')
if shell_l < 0:
raise MRtrixError('Values for lmax must be non-negative')
run.command('dwi2tensor dwi.mif - -mask mask.mif | tensor2metric - -fa fa.mif -vector vector.mif')
if not os.path.exists('dirs.mif'):
run.function(shutil.copy, 'vector.mif', 'dirs.mif')
run.command('mrtransform 5tt.mif 5tt_regrid.mif -template fa.mif -interp linear')
# Basic tissue masks
run.command('mrconvert 5tt_regrid.mif - -coord 3 2 -axes 0,1,2 | mrcalc - ' + str(app.ARGS.pvf) + ' -gt mask.mif -mult wm_mask.mif')
run.command('mrconvert 5tt_regrid.mif - -coord 3 0 -axes 0,1,2 | mrcalc - ' + str(app.ARGS.pvf) + ' -gt fa.mif ' + str(app.ARGS.fa) + ' -lt -mult mask.mif -mult gm_mask.mif')
run.command('mrconvert 5tt_regrid.mif - -coord 3 3 -axes 0,1,2 | mrcalc - ' + str(app.ARGS.pvf) + ' -gt fa.mif ' + str(app.ARGS.fa) + ' -lt -mult mask.mif -mult csf_mask.mif')
# Revise WM mask to only include single-fibre voxels
recursive_cleanup_option=''
if not app.DO_CLEANUP:
recursive_cleanup_option = ' -nocleanup'
if not app.ARGS.sfwm_fa_threshold:
app.console('Selecting WM single-fibre voxels using \'' + app.ARGS.wm_algo + '\' algorithm')
run.command('dwi2response ' + app.ARGS.wm_algo + ' dwi.mif wm_ss_response.txt -mask wm_mask.mif -voxels wm_sf_mask.mif -scratch ' + path.quote(app.SCRATCH_DIR) + recursive_cleanup_option)
else:
app.console('Selecting WM single-fibre voxels using \'fa\' algorithm with a hard FA threshold of ' + str(app.ARGS.sfwm_fa_threshold))
run.command('dwi2response fa dwi.mif wm_ss_response.txt -mask wm_mask.mif -threshold ' + str(app.ARGS.sfwm_fa_threshold) + ' -voxels wm_sf_mask.mif -scratch ' + path.quote(app.SCRATCH_DIR) + recursive_cleanup_option)
# Check for empty masks
wm_voxels = image.statistics('wm_sf_mask.mif', mask='wm_sf_mask.mif').count
gm_voxels = image.statistics('gm_mask.mif', mask='gm_mask.mif').count
csf_voxels = image.statistics('csf_mask.mif', mask='csf_mask.mif').count
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'
raise MRtrixError(message)
# For each of the three tissues, generate a multi-shell response
bvalues_option = ' -shells ' + ','.join(map(str,shells))
sfwm_lmax_option = ''
if wm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str,wm_lmax))
run.command('amp2response dwi.mif wm_sf_mask.mif dirs.mif wm.txt' + bvalues_option + sfwm_lmax_option)
run.command('amp2response dwi.mif gm_mask.mif dirs.mif gm.txt' + bvalues_option + ' -isotropic')
run.command('amp2response dwi.mif csf_mask.mif dirs.mif csf.txt' + bvalues_option + ' -isotropic')
run.function(shutil.copyfile, 'wm.txt', path.from_user(app.ARGS.out_wm, False))
run.function(shutil.copyfile, 'gm.txt', path.from_user(app.ARGS.out_gm, False))
run.function(shutil.copyfile, 'csf.txt', path.from_user(app.ARGS.out_csf, False))
# Generate output 4D binary image with voxel selections; RGB as in MSMT-CSD paper
run.command('mrcat csf_mask.mif gm_mask.mif wm_sf_mask.mif voxels.mif -axis 3')
if app.ARGS.voxels:
run.command('mrconvert voxels.mif ' + path.from_user(app.ARGS.voxels), mrconvert_keyval=path.from_user(app.ARGS.input, False), 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)
def execute():
import math, os, shutil
from mrtrix3 import app, image, path, run
# Get b-values and number of volumes per b-value.
bvalues = [
int(round(float(x)))
for x in image.headerField('dwi.mif', 'shells').split()
]
bvolumes = [
int(x) for x in image.headerField('dwi.mif', 'shellcounts').split()
]
app.console(
str(len(bvalues)) + ' unique b-value(s) detected: ' +
','.join(map(str, bvalues)) + ' with ' + ','.join(map(str, bvolumes)) +
' volumes.')
if len(bvalues) < 2:
app.error('Need at least 2 unique b-values (including b=0).')
# Get lmax information (if provided).
sfwm_lmax = []
if app.args.lmax:
sfwm_lmax = [int(x.strip()) for x in app.args.lmax.split(',')]
if not len(sfwm_lmax) == len(bvalues):
app.error('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:
app.error('Values supplied to the -lmax option must be even.')
if l < 0:
app.error(
'Values supplied to the -lmax option must be non-negative.'
)
# Erode (brain) mask.
if app.args.erode > 0:
run.command('maskfilter mask.mif erode eroded_mask.mif -npass ' +
str(app.args.erode))
else:
run.command('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):
meanpath = 'mean_b' + str(b) + '.mif'
run.command('dwiextract dwi.mif -shell ' + str(b) +
' - | mrmath - mean ' + meanpath + ' -axis 3')
errpath = 'err_b' + str(b) + '.mif'
run.command('mrcalc ' + meanpath + ' -finite ' + meanpath +
' 0 -if 0 -le ' + errpath + ' -datatype bit')
errcmd += ' ' + errpath
if i > 0:
errcmd += ' -add'
sdmpath = 'sdm_b' + str(b) + '.mif'
run.command('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'
run.command(fullsdmcmd)
run.command(
'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'
run.command(errcmd)
run.command('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.
run.command(
'dwi2tensor dwi.mif - -mask safe_mask.mif | tensor2metric - -fa safe_fa.mif -vector safe_vecs.mif -modulate none -mask safe_mask.mif'
)
run.command('mrcalc safe_mask.mif safe_fa.mif 0 -if ' + str(app.args.fa) +
' -gt crude_wm.mif -datatype bit')
run.command(
'mrcalc crude_wm.mif 0 safe_mask.mif -if _crudenonwm.mif -datatype bit'
)
# Crude GM versus CSF separation based on SDM.
crudenonwmmedian = image.statistic('safe_sdm.mif', 'median',
'_crudenonwm.mif')
run.command(
'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'
)
run.command(
'mrcalc crude_csf.mif 0 _crudenonwm.mif -if crude_gm.mif -datatype bit'
)
# Refine WM: remove high SDM outliers.
crudewmmedian = image.statistic('safe_sdm.mif', 'median', 'crude_wm.mif')
run.command('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' +
str(crudewmmedian) + ' -gt _crudewmhigh.mif -datatype bit')
run.command(
'mrcalc _crudewmhigh.mif 0 crude_wm.mif -if _crudewmlow.mif -datatype bit'
)
crudewmQ1 = float(
image.statistic('safe_sdm.mif', 'median', '_crudewmlow.mif'))
crudewmQ3 = float(
image.statistic('safe_sdm.mif', 'median', '_crudewmhigh.mif'))
crudewmoutlthresh = crudewmQ3 + (crudewmQ3 - crudewmQ1)
run.command('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' +
str(crudewmoutlthresh) +
' -gt _crudewmoutliers.mif -datatype bit')
run.command(
'mrcalc _crudewmoutliers.mif 0 crude_wm.mif -if refined_wm.mif -datatype bit'
)
# Refine GM: separate safer GM from partial volumed voxels.
crudegmmedian = image.statistic('safe_sdm.mif', 'median', 'crude_gm.mif')
run.command('mrcalc crude_gm.mif safe_sdm.mif 0 -if ' +
str(crudegmmedian) + ' -gt _crudegmhigh.mif -datatype bit')
run.command(
'mrcalc _crudegmhigh.mif 0 crude_gm.mif -if _crudegmlow.mif -datatype bit'
)
run.command(
'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'
)
run.command(
'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'
)
run.command(
'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 = image.statistic('safe_sdm.mif', 'min', 'crude_csf.mif')
run.command('mrcalc _crudewmoutliers.mif safe_sdm.mif 0 -if ' +
str(crudecsfmin) +
' -gt 1 crude_csf.mif -if _crudecsfextra.mif -datatype bit')
run.command(
'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(
image.statistic('refined_wm.mif', 'count', 'refined_wm.mif'))
voxsfwmcount = int(round(refwmcount * app.args.sfwm / 100.0))
app.console('Running \'tournier\' algorithm to select ' +
str(voxsfwmcount) + ' single-fibre WM voxels.')
cleanopt = ''
if not app._cleanup:
cleanopt = ' -nocleanup'
run.command('dwi2response tournier dwi.mif _respsfwmss.txt -sf_voxels ' +
str(voxsfwmcount) + ' -iter_voxels ' + str(voxsfwmcount * 10) +
' -mask refined_wm.mif -voxels voxels_sfwm.mif -tempdir ' +
app._tempDir + cleanopt)
# Get final voxels for GM response function estimation from GM.
refgmmedian = image.statistic('safe_sdm.mif', 'median', 'refined_gm.mif')
run.command('mrcalc refined_gm.mif safe_sdm.mif 0 -if ' +
str(refgmmedian) + ' -gt _refinedgmhigh.mif -datatype bit')
run.command(
'mrcalc _refinedgmhigh.mif 0 refined_gm.mif -if _refinedgmlow.mif -datatype bit'
)
refgmhighcount = float(
image.statistic('_refinedgmhigh.mif', 'count', '_refinedgmhigh.mif'))
refgmlowcount = float(
image.statistic('_refinedgmlow.mif', 'count', '_refinedgmlow.mif'))
voxgmhighcount = int(round(refgmhighcount * app.args.gm / 100.0))
voxgmlowcount = int(round(refgmlowcount * app.args.gm / 100.0))
run.command(
'mrcalc _refinedgmhigh.mif safe_sdm.mif 0 -if - | mrthreshold - - -bottom '
+ str(voxgmhighcount) +
' -ignorezero | mrcalc _refinedgmhigh.mif - 0 -if _refinedgmhighselect.mif -datatype bit'
)
run.command(
'mrcalc _refinedgmlow.mif safe_sdm.mif 0 -if - | mrthreshold - - -top '
+ str(voxgmlowcount) +
' -ignorezero | mrcalc _refinedgmlow.mif - 0 -if _refinedgmlowselect.mif -datatype bit'
)
run.command(
'mrcalc _refinedgmhighselect.mif 1 _refinedgmlowselect.mif -if voxels_gm.mif -datatype bit'
)
# Get final voxels for CSF response function estimation from CSF.
refcsfcount = float(
image.statistic('refined_csf.mif', 'count', 'refined_csf.mif'))
voxcsfcount = int(round(refcsfcount * app.args.csf / 100.0))
run.command(
'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 = ' --> '
app.console('Summary of voxel counts:')
app.console(
'Mask: ' + str(int(image.statistic('mask.mif', 'count', 'mask.mif'))) +
textarrow +
str(int(image.statistic('eroded_mask.mif', 'count',
'eroded_mask.mif'))) + textarrow +
str(int(image.statistic('safe_mask.mif', 'count', 'safe_mask.mif'))))
app.console(
'WM: ' +
str(int(image.statistic('crude_wm.mif', 'count', 'crude_wm.mif'))) +
textarrow +
str(int(image.statistic('refined_wm.mif', 'count',
'refined_wm.mif'))) + textarrow +
str(int(image.statistic('voxels_sfwm.mif', 'count',
'voxels_sfwm.mif'))) + ' (SF)')
app.console(
'GM: ' +
str(int(image.statistic('crude_gm.mif', 'count', 'crude_gm.mif'))) +
textarrow +
str(int(image.statistic('refined_gm.mif', 'count',
'refined_gm.mif'))) + textarrow +
str(int(image.statistic('voxels_gm.mif', 'count', 'voxels_gm.mif'))))
app.console(
'CSF: ' +
str(int(image.statistic('crude_csf.mif', 'count', 'crude_csf.mif'))) +
textarrow +
str(int(image.statistic('refined_csf.mif', 'count',
'refined_csf.mif'))) + textarrow +
str(int(image.statistic('voxels_csf.mif', 'count', 'voxels_csf.mif'))))
# Generate single-fibre WM, GM and CSF responses
bvalues_option = ' -shell ' + ','.join(map(str, bvalues))
sfwm_lmax_option = ''
if sfwm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str, sfwm_lmax))
run.command(
'amp2response dwi.mif voxels_sfwm.mif safe_vecs.mif response_sfwm.txt'
+ bvalues_option + sfwm_lmax_option)
run.command(
'amp2response dwi.mif voxels_gm.mif safe_vecs.mif response_gm.txt' +
bvalues_option + ' -isotropic')
run.command(
'amp2response dwi.mif voxels_csf.mif safe_vecs.mif response_csf.txt' +
bvalues_option + ' -isotropic')
run.function(shutil.copyfile, 'response_sfwm.txt',
path.fromUser(app.args.out_sfwm, False))
run.function(shutil.copyfile, 'response_gm.txt',
path.fromUser(app.args.out_gm, False))
run.function(shutil.copyfile, 'response_csf.txt',
path.fromUser(app.args.out_csf, False))
# Generate 4D binary images with voxel selections at major stages in algorithm (RGB as in MSMT-CSD paper).
run.command(
'mrcat crude_csf.mif crude_gm.mif crude_wm.mif crude.mif -axis 3')
run.command(
'mrcat refined_csf.mif refined_gm.mif refined_wm.mif refined.mif -axis 3'
)
run.command(
'mrcat voxels_csf.mif voxels_gm.mif voxels_sfwm.mif voxels.mif -axis 3'
)
def getInputs():
import os, shutil
from mrtrix3 import app, path, run
if app.args.lut:
run.function(shutil.copyfile, path.fromUser(app.args.lut, False),
path.toTemp('LUT.txt', False))
def execute():
import math, os, shutil
from mrtrix3 import app, image, path, run
# 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
run.command('5ttcheck 5tt.mif', False)
# Get shell information
shells = [
int(round(float(x)))
for x in image.headerField('dwi.mif', 'shells').split()
]
if len(shells) < 3:
app.warn(
'Less than three b-value shells; response functions will not be applicable in resolving three tissues using MSMT-CSD algorithm'
)
# Get lmax information (if provided)
wm_lmax = []
if app.args.lmax:
wm_lmax = [int(x.strip()) for x in app.args.lmax.split(',')]
if not len(wm_lmax) == len(shells):
app.error('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:
app.error('Values for lmax must be even')
if l < 0:
app.error('Values for lmax must be non-negative')
run.command(
'dwi2tensor dwi.mif - -mask mask.mif | tensor2metric - -fa fa.mif -vector vector.mif'
)
if not os.path.exists('dirs.mif'):
run.function(shutil.copy, 'vector.mif', 'dirs.mif')
run.command(
'mrtransform 5tt.mif 5tt_regrid.mif -template fa.mif -interp linear')
# Basic tissue masks
run.command(
'mrconvert 5tt_regrid.mif - -coord 3 2 -axes 0,1,2 | mrcalc - ' +
str(app.args.pvf) + ' -gt mask.mif -mult wm_mask.mif')
run.command(
'mrconvert 5tt_regrid.mif - -coord 3 0 -axes 0,1,2 | mrcalc - ' +
str(app.args.pvf) + ' -gt fa.mif ' + str(app.args.fa) +
' -lt -mult mask.mif -mult gm_mask.mif')
run.command(
'mrconvert 5tt_regrid.mif - -coord 3 3 -axes 0,1,2 | mrcalc - ' +
str(app.args.pvf) + ' -gt fa.mif ' + str(app.args.fa) +
' -lt -mult mask.mif -mult csf_mask.mif')
# Revise WM mask to only include single-fibre voxels
app.console(
'Calling dwi2response recursively to select WM single-fibre voxels using \''
+ app.args.wm_algo + '\' algorithm')
recursive_cleanup_option = ''
if not app._cleanup:
recursive_cleanup_option = ' -nocleanup'
run.command(
'dwi2response ' + app.args.wm_algo +
' dwi.mif wm_ss_response.txt -mask wm_mask.mif -voxels wm_sf_mask.mif -tempdir '
+ app._tempDir + recursive_cleanup_option)
# Check for empty masks
wm_voxels = int(
image.statistic('wm_sf_mask.mif', 'count', 'wm_sf_mask.mif'))
gm_voxels = int(image.statistic('gm_mask.mif', 'count', 'gm_mask.mif'))
csf_voxels = int(image.statistic('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'
app.error(message)
# For each of the three tissues, generate a multi-shell response
bvalues_option = ' -shell ' + ','.join(map(str, shells))
sfwm_lmax_option = ''
if wm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str, wm_lmax))
run.command('amp2response dwi.mif wm_sf_mask.mif dirs.mif wm.txt' +
bvalues_option + sfwm_lmax_option)
run.command('amp2response dwi.mif gm_mask.mif dirs.mif gm.txt' +
bvalues_option + ' -isotropic')
run.command('amp2response dwi.mif csf_mask.mif dirs.mif csf.txt' +
bvalues_option + ' -isotropic')
run.function(shutil.copyfile, 'wm.txt',
path.fromUser(app.args.out_wm, False))
run.function(shutil.copyfile, 'gm.txt',
path.fromUser(app.args.out_gm, False))
run.function(shutil.copyfile, 'csf.txt',
path.fromUser(app.args.out_csf, False))
# Generate output 4D binary image with voxel selections; RGB as in MSMT-CSD paper
run.command(
'mrcat csf_mask.mif gm_mask.mif wm_sf_mask.mif voxels.mif -axis 3')
if not grad:
app.error('No diffusion gradient table found')
if not len(grad) == num_volumes:
app.error('Number of lines in gradient table (' + str(len(grad)) + ') does not match input image (' + str(num_volumes) + ' volumes); check your input data')
if app.args.extent:
extent = app.args.extent
else: extent = '5,5,5'
run.command('mrconvert dwi.mif working.mif')
# denoising
if app.args.denoise:
print("...Beginning denoising")
run.command('dwidenoise -extent ' + extent + ' -noise fullnoisemap.mif working.mif dwidn.mif')
run.function(os.remove,'working.mif')
run.command('mrconvert dwidn.mif working.mif')
# gibbs artifact correction
if app.args.degibbs:
print("...Beginning degibbsing")
run.command('mrdegibbs -nshifts 20 -minW 1 -maxW 3 working.mif dwigc.mif')
run.function(os.remove,'working.mif')
run.command('mrconvert dwigc.mif working.mif')
# pre-eddy alignment for multiple input series
if app.args.prealign:
if len(DWInlist) != 1:
miflist = []
for idx,i in enumerate(DWInlist):
run.command('mrconvert -coord 3 ' + idxlist[idx] + ' working.mif dwipretf' + str(idx) + '.mif')
def execute():
import math, os, shutil
from mrtrix3 import app, image, path, run
# Get b-values and number of volumes per b-value.
bvalues = [ int(round(float(x))) for x in image.headerField('dwi.mif', 'shells').split() ]
bvolumes = [ int(x) for x in image.headerField('dwi.mif', 'shellcounts').split() ]
app.console(str(len(bvalues)) + ' unique b-value(s) detected: ' + ','.join(map(str,bvalues)) + ' with ' + ','.join(map(str,bvolumes)) + ' volumes.')
if len(bvalues) < 2:
app.error('Need at least 2 unique b-values (including b=0).')
# Get lmax information (if provided).
sfwm_lmax = [ ]
if app.args.lmax:
sfwm_lmax = [ int(x.strip()) for x in app.args.lmax.split(',') ]
if not len(sfwm_lmax) == len(bvalues):
app.error('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:
app.error('Values supplied to the -lmax option must be even.')
if l<0:
app.error('Values supplied to the -lmax option must be non-negative.')
# Erode (brain) mask.
if app.args.erode > 0:
run.command('maskfilter mask.mif erode eroded_mask.mif -npass ' + str(app.args.erode))
else:
run.command('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):
meanpath = 'mean_b' + str(b) + '.mif'
run.command('dwiextract dwi.mif -shell ' + str(b) + ' - | mrmath - mean ' + meanpath + ' -axis 3')
errpath = 'err_b' + str(b) + '.mif'
run.command('mrcalc ' + meanpath + ' -finite ' + meanpath + ' 0 -if 0 -le ' + errpath + ' -datatype bit')
errcmd += ' ' + errpath
if i>0:
errcmd += ' -add'
sdmpath = 'sdm_b' + str(b) + '.mif'
run.command('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'
run.command(fullsdmcmd)
run.command('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'
run.command(errcmd)
run.command('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.
run.command('dwi2tensor dwi.mif - -mask safe_mask.mif | tensor2metric - -fa safe_fa.mif -vector safe_vecs.mif -modulate none -mask safe_mask.mif')
run.command('mrcalc safe_mask.mif safe_fa.mif 0 -if ' + str(app.args.fa) + ' -gt crude_wm.mif -datatype bit')
run.command('mrcalc crude_wm.mif 0 safe_mask.mif -if _crudenonwm.mif -datatype bit')
# Crude GM versus CSF separation based on SDM.
crudenonwmmedian = image.statistic('safe_sdm.mif', 'median', '_crudenonwm.mif')
run.command('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')
run.command('mrcalc crude_csf.mif 0 _crudenonwm.mif -if crude_gm.mif -datatype bit')
# Refine WM: remove high SDM outliers.
crudewmmedian = image.statistic('safe_sdm.mif', 'median', 'crude_wm.mif')
run.command('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' + str(crudewmmedian) + ' -gt _crudewmhigh.mif -datatype bit')
run.command('mrcalc _crudewmhigh.mif 0 crude_wm.mif -if _crudewmlow.mif -datatype bit')
crudewmQ1 = float(image.statistic('safe_sdm.mif', 'median', '_crudewmlow.mif'))
crudewmQ3 = float(image.statistic('safe_sdm.mif', 'median', '_crudewmhigh.mif'))
crudewmoutlthresh = crudewmQ3 + (crudewmQ3 - crudewmQ1)
run.command('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' + str(crudewmoutlthresh) + ' -gt _crudewmoutliers.mif -datatype bit')
run.command('mrcalc _crudewmoutliers.mif 0 crude_wm.mif -if refined_wm.mif -datatype bit')
# Refine GM: separate safer GM from partial volumed voxels.
crudegmmedian = image.statistic('safe_sdm.mif', 'median', 'crude_gm.mif')
run.command('mrcalc crude_gm.mif safe_sdm.mif 0 -if ' + str(crudegmmedian) + ' -gt _crudegmhigh.mif -datatype bit')
run.command('mrcalc _crudegmhigh.mif 0 crude_gm.mif -if _crudegmlow.mif -datatype bit')
run.command('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')
run.command('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')
run.command('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 = image.statistic('safe_sdm.mif', 'min', 'crude_csf.mif')
run.command('mrcalc _crudewmoutliers.mif safe_sdm.mif 0 -if ' + str(crudecsfmin) + ' -gt 1 crude_csf.mif -if _crudecsfextra.mif -datatype bit')
run.command('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(image.statistic('refined_wm.mif', 'count', 'refined_wm.mif'))
voxsfwmcount = int(round(refwmcount * app.args.sfwm / 100.0))
app.console('Running \'tournier\' algorithm to select ' + str(voxsfwmcount) + ' single-fibre WM voxels.')
cleanopt = ''
if not app._cleanup:
cleanopt = ' -nocleanup'
run.command('dwi2response tournier dwi.mif _respsfwmss.txt -sf_voxels ' + str(voxsfwmcount) + ' -iter_voxels ' + str(voxsfwmcount * 10) + ' -mask refined_wm.mif -voxels voxels_sfwm.mif -tempdir ' + app._tempDir + cleanopt)
# Get final voxels for GM response function estimation from GM.
refgmmedian = image.statistic('safe_sdm.mif', 'median', 'refined_gm.mif')
run.command('mrcalc refined_gm.mif safe_sdm.mif 0 -if ' + str(refgmmedian) + ' -gt _refinedgmhigh.mif -datatype bit')
run.command('mrcalc _refinedgmhigh.mif 0 refined_gm.mif -if _refinedgmlow.mif -datatype bit')
refgmhighcount = float(image.statistic('_refinedgmhigh.mif', 'count', '_refinedgmhigh.mif'))
refgmlowcount = float(image.statistic('_refinedgmlow.mif', 'count', '_refinedgmlow.mif'))
voxgmhighcount = int(round(refgmhighcount * app.args.gm / 100.0))
voxgmlowcount = int(round(refgmlowcount * app.args.gm / 100.0))
run.command('mrcalc _refinedgmhigh.mif safe_sdm.mif 0 -if - | mrthreshold - - -bottom ' + str(voxgmhighcount) + ' -ignorezero | mrcalc _refinedgmhigh.mif - 0 -if _refinedgmhighselect.mif -datatype bit')
run.command('mrcalc _refinedgmlow.mif safe_sdm.mif 0 -if - | mrthreshold - - -top ' + str(voxgmlowcount) + ' -ignorezero | mrcalc _refinedgmlow.mif - 0 -if _refinedgmlowselect.mif -datatype bit')
run.command('mrcalc _refinedgmhighselect.mif 1 _refinedgmlowselect.mif -if voxels_gm.mif -datatype bit')
# Get final voxels for CSF response function estimation from CSF.
refcsfcount = float(image.statistic('refined_csf.mif', 'count', 'refined_csf.mif'))
voxcsfcount = int(round(refcsfcount * app.args.csf / 100.0))
run.command('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 = ' --> '
app.console('Summary of voxel counts:')
app.console('Mask: ' + str(int(image.statistic('mask.mif', 'count', 'mask.mif'))) + textarrow + str(int(image.statistic('eroded_mask.mif', 'count', 'eroded_mask.mif'))) + textarrow + str(int(image.statistic('safe_mask.mif', 'count', 'safe_mask.mif'))))
app.console('WM: ' + str(int(image.statistic('crude_wm.mif', 'count', 'crude_wm.mif'))) + textarrow + str(int(image.statistic('refined_wm.mif', 'count', 'refined_wm.mif'))) + textarrow + str(int(image.statistic('voxels_sfwm.mif', 'count', 'voxels_sfwm.mif'))) + ' (SF)')
app.console('GM: ' + str(int(image.statistic('crude_gm.mif', 'count', 'crude_gm.mif'))) + textarrow + str(int(image.statistic('refined_gm.mif', 'count', 'refined_gm.mif'))) + textarrow + str(int(image.statistic('voxels_gm.mif', 'count', 'voxels_gm.mif'))))
app.console('CSF: ' + str(int(image.statistic('crude_csf.mif', 'count', 'crude_csf.mif'))) + textarrow + str(int(image.statistic('refined_csf.mif', 'count', 'refined_csf.mif'))) + textarrow + str(int(image.statistic('voxels_csf.mif', 'count', 'voxels_csf.mif'))))
# Generate single-fibre WM, GM and CSF responses
bvalues_option = ' -shell ' + ','.join(map(str,bvalues))
sfwm_lmax_option = ''
if sfwm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str,sfwm_lmax))
run.command('amp2response dwi.mif voxels_sfwm.mif safe_vecs.mif response_sfwm.txt' + bvalues_option + sfwm_lmax_option)
run.command('amp2response dwi.mif voxels_gm.mif safe_vecs.mif response_gm.txt' + bvalues_option + ' -isotropic')
run.command('amp2response dwi.mif voxels_csf.mif safe_vecs.mif response_csf.txt' + bvalues_option + ' -isotropic')
run.function(shutil.copyfile, 'response_sfwm.txt', path.fromUser(app.args.out_sfwm, False))
run.function(shutil.copyfile, 'response_gm.txt', path.fromUser(app.args.out_gm, False))
run.function(shutil.copyfile, 'response_csf.txt', path.fromUser(app.args.out_csf, False))
# Generate 4D binary images with voxel selections at major stages in algorithm (RGB as in MSMT-CSD paper).
run.command('mrcat crude_csf.mif crude_gm.mif crude_wm.mif crude.mif -axis 3')
run.command('mrcat refined_csf.mif refined_gm.mif refined_wm.mif refined.mif -axis 3')
run.command('mrcat voxels_csf.mif voxels_gm.mif voxels_sfwm.mif voxels.mif -axis 3')
def execute(): #pylint: disable=unused-variable
bzero_threshold = float(
CONFIG['BZeroThreshold']) if 'BZeroThreshold' in CONFIG else 10.0
# CHECK INPUTS AND OPTIONS
app.console('-------')
# Get b-values and number of volumes per b-value.
bvalues = [
int(round(float(x)))
for x in image.mrinfo('dwi.mif', 'shell_bvalues').split()
]
bvolumes = [int(x) for x in image.mrinfo('dwi.mif', 'shell_sizes').split()]
app.console(
str(len(bvalues)) + ' unique b-value(s) detected: ' +
','.join(map(str, bvalues)) + ' with ' + ','.join(map(str, bvolumes)) +
' volumes')
if len(bvalues) < 2:
raise MRtrixError('Need at least 2 unique b-values (including b=0).')
bvalues_option = ' -shells ' + ','.join(map(str, bvalues))
# Get lmax information (if provided).
sfwm_lmax = []
if app.ARGS.lmax:
sfwm_lmax = [int(x.strip()) for x in app.ARGS.lmax.split(',')]
if not len(sfwm_lmax) == len(bvalues):
raise MRtrixError('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 sfl in sfwm_lmax:
if sfl % 2:
raise MRtrixError(
'Values supplied to the -lmax option must be even.')
if sfl < 0:
raise MRtrixError(
'Values supplied to the -lmax option must be non-negative.'
)
sfwm_lmax_option = ''
if sfwm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str, sfwm_lmax))
# PREPARATION
app.console('-------')
app.console('Preparation:')
# Erode (brain) mask.
if app.ARGS.erode > 0:
app.console('* Eroding brain mask by ' + str(app.ARGS.erode) +
' pass(es)...')
run.command('maskfilter mask.mif erode eroded_mask.mif -npass ' +
str(app.ARGS.erode),
show=False)
else:
app.console('Not eroding brain mask.')
run.command('mrconvert mask.mif eroded_mask.mif -datatype bit',
show=False)
statmaskcount = image.statistics('mask.mif', mask='mask.mif').count
statemaskcount = image.statistics('eroded_mask.mif',
mask='eroded_mask.mif').count
app.console(' [ mask: ' + str(statmaskcount) + ' -> ' +
str(statemaskcount) + ' ]')
# Get volumes, compute mean signal and SDM per b-value; compute overall SDM; get rid of erroneous values.
app.console('* Computing signal decay metric (SDM):')
totvolumes = 0
fullsdmcmd = 'mrcalc'
errcmd = 'mrcalc'
zeropath = 'mean_b' + str(bvalues[0]) + '.mif'
for ibv, bval in enumerate(bvalues):
app.console(' * b=' + str(bval) + '...')
meanpath = 'mean_b' + str(bval) + '.mif'
run.command('dwiextract dwi.mif -shells ' + str(bval) +
' - | mrcalc - 0 -max - | mrmath - mean ' + meanpath +
' -axis 3',
show=False)
errpath = 'err_b' + str(bval) + '.mif'
run.command('mrcalc ' + meanpath + ' -finite ' + meanpath +
' 0 -if 0 -le ' + errpath + ' -datatype bit',
show=False)
errcmd += ' ' + errpath
if ibv > 0:
errcmd += ' -add'
sdmpath = 'sdm_b' + str(bval) + '.mif'
run.command('mrcalc ' + zeropath + ' ' + meanpath +
' -divide -log ' + sdmpath,
show=False)
totvolumes += bvolumes[ibv]
fullsdmcmd += ' ' + sdmpath + ' ' + str(bvolumes[ibv]) + ' -mult'
if ibv > 1:
fullsdmcmd += ' -add'
fullsdmcmd += ' ' + str(totvolumes) + ' -divide full_sdm.mif'
run.command(fullsdmcmd, show=False)
app.console('* Removing erroneous voxels from mask and correcting SDM...')
run.command(
'mrcalc full_sdm.mif -finite full_sdm.mif 0 -if 0 -le err_sdm.mif -datatype bit',
show=False)
errcmd += ' err_sdm.mif -add 0 eroded_mask.mif -if safe_mask.mif -datatype bit'
run.command(errcmd, show=False)
run.command('mrcalc safe_mask.mif full_sdm.mif 0 -if 10 -min safe_sdm.mif',
show=False)
statsmaskcount = image.statistics('safe_mask.mif',
mask='safe_mask.mif').count
app.console(' [ mask: ' + str(statemaskcount) + ' -> ' +
str(statsmaskcount) + ' ]')
# CRUDE SEGMENTATION
app.console('-------')
app.console('Crude segmentation:')
# Compute FA and principal eigenvectors; crude WM versus GM-CSF separation based on FA.
app.console('* Crude WM versus GM-CSF separation (at FA=' +
str(app.ARGS.fa) + ')...')
run.command(
'dwi2tensor dwi.mif - -mask safe_mask.mif | tensor2metric - -fa safe_fa.mif -vector safe_vecs.mif -modulate none -mask safe_mask.mif',
show=False)
run.command('mrcalc safe_mask.mif safe_fa.mif 0 -if ' + str(app.ARGS.fa) +
' -gt crude_wm.mif -datatype bit',
show=False)
run.command(
'mrcalc crude_wm.mif 0 safe_mask.mif -if _crudenonwm.mif -datatype bit',
show=False)
statcrudewmcount = image.statistics('crude_wm.mif',
mask='crude_wm.mif').count
statcrudenonwmcount = image.statistics('_crudenonwm.mif',
mask='_crudenonwm.mif').count
app.console(' [ ' + str(statsmaskcount) + ' -> ' + str(statcrudewmcount) +
' (WM) & ' + str(statcrudenonwmcount) + ' (GM-CSF) ]')
# Crude GM versus CSF separation based on SDM.
app.console('* Crude GM versus CSF separation...')
crudenonwmmedian = image.statistics('safe_sdm.mif',
mask='_crudenonwm.mif').median
run.command(
'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',
show=False)
run.command(
'mrcalc crude_csf.mif 0 _crudenonwm.mif -if crude_gm.mif -datatype bit',
show=False)
statcrudegmcount = image.statistics('crude_gm.mif',
mask='crude_gm.mif').count
statcrudecsfcount = image.statistics('crude_csf.mif',
mask='crude_csf.mif').count
app.console(' [ ' + str(statcrudenonwmcount) + ' -> ' +
str(statcrudegmcount) + ' (GM) & ' + str(statcrudecsfcount) +
' (CSF) ]')
# REFINED SEGMENTATION
app.console('-------')
app.console('Refined segmentation:')
# Refine WM: remove high SDM outliers.
app.console('* Refining WM...')
crudewmmedian = image.statistics('safe_sdm.mif',
mask='crude_wm.mif').median
run.command('mrcalc crude_wm.mif safe_sdm.mif ' + str(crudewmmedian) +
' -subtract -abs 0 -if _crudewm_sdmad.mif',
show=False)
crudewmmad = image.statistics('_crudewm_sdmad.mif',
mask='crude_wm.mif').median
crudewmoutlthresh = crudewmmedian + (1.4826 * crudewmmad * 2.0)
run.command('mrcalc crude_wm.mif safe_sdm.mif 0 -if ' +
str(crudewmoutlthresh) +
' -gt _crudewmoutliers.mif -datatype bit',
show=False)
run.command(
'mrcalc _crudewmoutliers.mif 0 crude_wm.mif -if refined_wm.mif -datatype bit',
show=False)
statrefwmcount = image.statistics('refined_wm.mif',
mask='refined_wm.mif').count
app.console(' [ WM: ' + str(statcrudewmcount) + ' -> ' +
str(statrefwmcount) + ' ]')
# Refine GM: separate safer GM from partial volumed voxels.
app.console('* Refining GM...')
crudegmmedian = image.statistics('safe_sdm.mif',
mask='crude_gm.mif').median
run.command('mrcalc crude_gm.mif safe_sdm.mif 0 -if ' +
str(crudegmmedian) + ' -gt _crudegmhigh.mif -datatype bit',
show=False)
run.command(
'mrcalc _crudegmhigh.mif 0 crude_gm.mif -if _crudegmlow.mif -datatype bit',
show=False)
run.command(
'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',
show=False)
run.command(
'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',
show=False)
run.command(
'mrcalc _crudegmhighselect.mif 1 _crudegmlowselect.mif -if refined_gm.mif -datatype bit',
show=False)
statrefgmcount = image.statistics('refined_gm.mif',
mask='refined_gm.mif').count
app.console(' [ GM: ' + str(statcrudegmcount) + ' -> ' +
str(statrefgmcount) + ' ]')
# Refine CSF: recover lost CSF from crude WM SDM outliers, separate safer CSF from partial volumed voxels.
app.console('* Refining CSF...')
crudecsfmin = image.statistics('safe_sdm.mif', mask='crude_csf.mif').min
run.command('mrcalc _crudewmoutliers.mif safe_sdm.mif 0 -if ' +
str(crudecsfmin) +
' -gt 1 crude_csf.mif -if _crudecsfextra.mif -datatype bit',
show=False)
run.command(
'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',
show=False)
statrefcsfcount = image.statistics('refined_csf.mif',
mask='refined_csf.mif').count
app.console(' [ CSF: ' + str(statcrudecsfcount) + ' -> ' +
str(statrefcsfcount) + ' ]')
# FINAL VOXEL SELECTION AND RESPONSE FUNCTION ESTIMATION
app.console('-------')
app.console('Final voxel selection and response function estimation:')
# Get final voxels for CSF response function estimation from refined CSF.
app.console('* CSF:')
app.console(' * Selecting final voxels (' + str(app.ARGS.csf) +
'% of refined CSF)...')
voxcsfcount = int(round(statrefcsfcount * app.ARGS.csf / 100.0))
run.command(
'mrcalc refined_csf.mif safe_sdm.mif 0 -if - | mrthreshold - - -top ' +
str(voxcsfcount) +
' -ignorezero | mrcalc refined_csf.mif - 0 -if - -datatype bit | mrconvert - voxels_csf.mif -axes 0,1,2',
show=False)
statvoxcsfcount = image.statistics('voxels_csf.mif',
mask='voxels_csf.mif').count
app.console(' [ CSF: ' + str(statrefcsfcount) + ' -> ' +
str(statvoxcsfcount) + ' ]')
# Estimate CSF response function
app.console(' * Estimating response function...')
run.command(
'amp2response dwi.mif voxels_csf.mif safe_vecs.mif response_csf.txt' +
bvalues_option + ' -isotropic',
show=False)
# Get final voxels for GM response function estimation from refined GM.
app.console('* GM:')
app.console(' * Selecting final voxels (' + str(app.ARGS.gm) +
'% of refined GM)...')
voxgmcount = int(round(statrefgmcount * app.ARGS.gm / 100.0))
refgmmedian = image.statistics('safe_sdm.mif',
mask='refined_gm.mif').median
run.command(
'mrcalc refined_gm.mif safe_sdm.mif ' + str(refgmmedian) +
' -subtract -abs 1 -add 0 -if - | mrthreshold - - -bottom ' +
str(voxgmcount) +
' -ignorezero | mrcalc refined_gm.mif - 0 -if - -datatype bit | mrconvert - voxels_gm.mif -axes 0,1,2',
show=False)
statvoxgmcount = image.statistics('voxels_gm.mif',
mask='voxels_gm.mif').count
app.console(' [ GM: ' + str(statrefgmcount) + ' -> ' +
str(statvoxgmcount) + ' ]')
# Estimate GM response function
app.console(' * Estimating response function...')
run.command(
'amp2response dwi.mif voxels_gm.mif safe_vecs.mif response_gm.txt' +
bvalues_option + ' -isotropic',
show=False)
# Get final voxels for single-fibre WM response function estimation from refined WM.
app.console('* Single-fibre WM:')
app.console(' * Selecting final voxels' +
('' if app.ARGS.wm_algo == 'tax' else
(' (' + str(app.ARGS.sfwm) + '% of refined WM)')) + '...')
voxsfwmcount = int(round(statrefwmcount * app.ARGS.sfwm / 100.0))
if app.ARGS.wm_algo:
recursive_cleanup_option = ''
if not app.DO_CLEANUP:
recursive_cleanup_option = ' -nocleanup'
app.console(' Selecting WM single-fibre voxels using \'' +
app.ARGS.wm_algo + '\' algorithm')
if app.ARGS.wm_algo == 'tax' and app.ARGS.sfwm != 0.5:
app.warn(
'Single-fibre WM response function selection algorithm "tax" will not honour requested WM voxel percentage'
)
run.command(
'dwi2response ' + app.ARGS.wm_algo +
' dwi.mif _respsfwmss.txt -mask refined_wm.mif -voxels voxels_sfwm.mif'
+ ('' if app.ARGS.wm_algo == 'tax' else
(' -number ' + str(voxsfwmcount))) + ' -scratch ' +
path.quote(app.SCRATCH_DIR) + recursive_cleanup_option,
show=False)
else:
app.console(
' Selecting WM single-fibre voxels using built-in (Dhollander et al., 2019) algorithm'
)
run.command('mrmath dwi.mif mean mean_sig.mif -axis 3', show=False)
refwmcoef = image.statistics('mean_sig.mif',
mask='refined_wm.mif').median * math.sqrt(
4.0 * math.pi)
if sfwm_lmax:
isiso = [lm == 0 for lm in sfwm_lmax]
else:
isiso = [bv < bzero_threshold for bv in bvalues]
with open('ewmrf.txt', 'w') as ewr:
for iis in isiso:
if iis:
ewr.write("%s 0 0 0\n" % refwmcoef)
else:
ewr.write("%s -%s %s -%s\n" %
(refwmcoef, refwmcoef, refwmcoef, refwmcoef))
run.command(
'dwi2fod msmt_csd dwi.mif ewmrf.txt abs_ewm2.mif response_csf.txt abs_csf2.mif -mask refined_wm.mif -lmax 2,0'
+ bvalues_option,
show=False)
run.command(
'mrconvert abs_ewm2.mif - -coord 3 0 | mrcalc - abs_csf2.mif -add abs_sum2.mif',
show=False)
run.command(
'sh2peaks abs_ewm2.mif - -num 1 -mask refined_wm.mif | peaks2amp - - | mrcalc - abs_sum2.mif -divide - | mrconvert - metric_sfwm2.mif -coord 3 0 -axes 0,1,2',
show=False)
run.command(
'mrcalc refined_wm.mif metric_sfwm2.mif 0 -if - | mrthreshold - - -top '
+ str(voxsfwmcount * 2) +
' -ignorezero | mrcalc refined_wm.mif - 0 -if - -datatype bit | mrconvert - refined_sfwm.mif -axes 0,1,2',
show=False)
run.command(
'dwi2fod msmt_csd dwi.mif ewmrf.txt abs_ewm6.mif response_csf.txt abs_csf6.mif -mask refined_sfwm.mif -lmax 6,0'
+ bvalues_option,
show=False)
run.command(
'mrconvert abs_ewm6.mif - -coord 3 0 | mrcalc - abs_csf6.mif -add abs_sum6.mif',
show=False)
run.command(
'sh2peaks abs_ewm6.mif - -num 1 -mask refined_sfwm.mif | peaks2amp - - | mrcalc - abs_sum6.mif -divide - | mrconvert - metric_sfwm6.mif -coord 3 0 -axes 0,1,2',
show=False)
run.command(
'mrcalc refined_sfwm.mif metric_sfwm6.mif 0 -if - | mrthreshold - - -top '
+ str(voxsfwmcount) +
' -ignorezero | mrcalc refined_sfwm.mif - 0 -if - -datatype bit | mrconvert - voxels_sfwm.mif -axes 0,1,2',
show=False)
statvoxsfwmcount = image.statistics('voxels_sfwm.mif',
mask='voxels_sfwm.mif').count
app.console(' [ WM: ' + str(statrefwmcount) + ' -> ' +
str(statvoxsfwmcount) + ' (single-fibre) ]')
# Estimate SF WM response function
app.console(' * Estimating response function...')
run.command(
'amp2response dwi.mif voxels_sfwm.mif safe_vecs.mif response_sfwm.txt'
+ bvalues_option + sfwm_lmax_option,
show=False)
# OUTPUT AND SUMMARY
app.console('-------')
app.console('Generating outputs...')
# Generate 4D binary images with voxel selections at major stages in algorithm (RGB: WM=blue, GM=green, CSF=red).
run.command(
'mrcat crude_csf.mif crude_gm.mif crude_wm.mif check_crude.mif -axis 3',
show=False)
run.command(
'mrcat refined_csf.mif refined_gm.mif refined_wm.mif check_refined.mif -axis 3',
show=False)
run.command(
'mrcat voxels_csf.mif voxels_gm.mif voxels_sfwm.mif check_voxels.mif -axis 3',
show=False)
# Copy results to output files
run.function(shutil.copyfile,
'response_sfwm.txt',
path.from_user(app.ARGS.out_sfwm, False),
show=False)
run.function(shutil.copyfile,
'response_gm.txt',
path.from_user(app.ARGS.out_gm, False),
show=False)
run.function(shutil.copyfile,
'response_csf.txt',
path.from_user(app.ARGS.out_csf, False),
show=False)
if app.ARGS.voxels:
run.command('mrconvert check_voxels.mif ' +
path.from_user(app.ARGS.voxels),
mrconvert_keyval=path.from_user(app.ARGS.input, False),
force=app.FORCE_OVERWRITE,
show=False)
app.console('-------')
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 get_inputs(): #pylint: disable=unused-variable
run.command('mrconvert ' + path.from_user(app.ARGS.input) + ' ' +
path.to_scratch('input.mif'))
if app.ARGS.lut:
run.function(shutil.copyfile, path.from_user(app.ARGS.lut, False),
path.to_scratch('LUT.txt', False))
def execute(): #pylint: disable=unused-variable
lmax_option = ''
if app.ARGS.lmax:
lmax_option = ' -lmax ' + app.ARGS.lmax
if app.ARGS.max_iters < 2:
raise MRtrixError('Number of iterations must be at least 2')
progress = app.ProgressBar('Optimising')
iter_voxels = app.ARGS.iter_voxels
if iter_voxels == 0:
iter_voxels = 10 * app.ARGS.number
elif iter_voxels < app.ARGS.number:
raise MRtrixError(
'Number of selected voxels (-iter_voxels) must be greater than number of voxels desired (-number)'
)
iteration = 0
while iteration < app.ARGS.max_iters:
prefix = 'iter' + str(iteration) + '_'
if iteration == 0:
rf_in_path = 'init_RF.txt'
mask_in_path = 'mask.mif'
init_rf = '1 -1 1'
with open(rf_in_path, 'w') as init_rf_file:
init_rf_file.write(init_rf)
iter_lmax_option = ' -lmax 4'
else:
rf_in_path = 'iter' + str(iteration - 1) + '_RF.txt'
mask_in_path = 'iter' + str(iteration - 1) + '_SF_dilated.mif'
iter_lmax_option = lmax_option
# Run CSD
run.command('dwi2fod csd dwi.mif ' + rf_in_path + ' ' + prefix +
'FOD.mif -mask ' + mask_in_path)
# Get amplitudes of two largest peaks, and direction of largest
run.command('fod2fixel ' + prefix + 'FOD.mif ' + prefix +
'fixel -peak peaks.mif -mask ' + mask_in_path +
' -fmls_no_thresholds')
app.cleanup(prefix + 'FOD.mif')
if iteration:
app.cleanup(mask_in_path)
run.command('fixel2voxel ' + prefix + 'fixel/peaks.mif none ' +
prefix + 'amps.mif -number 2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'first_peaks.mif -coord 3 0 -axes 0,1,2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'second_peaks.mif -coord 3 1 -axes 0,1,2')
app.cleanup(prefix + 'amps.mif')
run.command('fixel2peaks ' + prefix + 'fixel/directions.mif ' +
prefix + 'first_dir.mif -number 1')
app.cleanup(prefix + 'fixel')
# Calculate the 'cost function' Donald derived for selecting single-fibre voxels
# https://github.com/MRtrix3/mrtrix3/pull/426
# sqrt(|peak1|) * (1 - |peak2| / |peak1|)^2
run.command('mrcalc ' + prefix + 'first_peaks.mif -sqrt 1 ' + prefix +
'second_peaks.mif ' + prefix +
'first_peaks.mif -div -sub 2 -pow -mult ' + prefix +
'CF.mif')
app.cleanup(prefix + 'first_peaks.mif')
app.cleanup(prefix + 'second_peaks.mif')
voxel_count = image.statistics(prefix + 'CF.mif').count
# Select the top-ranked voxels
run.command('mrthreshold ' + prefix + 'CF.mif -top ' +
str(min([app.ARGS.number, voxel_count])) + ' ' + prefix +
'SF.mif')
# Generate a new response function based on this selection
run.command('amp2response dwi.mif ' + prefix + 'SF.mif ' + prefix +
'first_dir.mif ' + prefix + 'RF.txt' + iter_lmax_option)
app.cleanup(prefix + 'first_dir.mif')
new_rf = matrix.load_vector(prefix + 'RF.txt')
progress.increment('Optimising (' + str(iteration + 1) +
' iterations, RF: [ ' + ', '.join('{:.3f}'.format(n)
for n in new_rf) +
'] )')
# Should we terminate?
if iteration > 0:
run.command('mrcalc ' + prefix + 'SF.mif iter' +
str(iteration - 1) + '_SF.mif -sub ' + prefix +
'SF_diff.mif')
app.cleanup('iter' + str(iteration - 1) + '_SF.mif')
max_diff = image.statistics(prefix + 'SF_diff.mif').max
app.cleanup(prefix + 'SF_diff.mif')
if not max_diff:
app.cleanup(prefix + 'CF.mif')
run.function(shutil.copyfile, prefix + 'RF.txt',
'response.txt')
run.function(shutil.move, prefix + 'SF.mif', 'voxels.mif')
break
# Select a greater number of top single-fibre voxels, and dilate (within bounds of initial mask);
# these are the voxels that will be re-tested in the next iteration
run.command('mrthreshold ' + prefix + 'CF.mif -top ' +
str(min([iter_voxels, voxel_count])) +
' - | maskfilter - dilate - -npass ' +
str(app.ARGS.dilate) + ' | mrcalc mask.mif - -mult ' +
prefix + 'SF_dilated.mif')
app.cleanup(prefix + 'CF.mif')
iteration += 1
progress.done()
# If terminating due to running out of iterations, still need to put the results in the appropriate location
if os.path.exists('response.txt'):
app.console(
'Convergence of SF voxel selection detected at iteration ' +
str(iteration + 1))
else:
app.console('Exiting after maximum ' + str(app.ARGS.max_iters) +
' iterations')
run.function(shutil.copyfile,
'iter' + str(app.ARGS.max_iters - 1) + '_RF.txt',
'response.txt')
run.function(shutil.move,
'iter' + str(app.ARGS.max_iters - 1) + '_SF.mif',
'voxels.mif')
run.function(shutil.copyfile, 'response.txt',
path.from_user(app.ARGS.output, False))
if app.ARGS.voxels:
run.command('mrconvert voxels.mif ' + path.from_user(app.ARGS.voxels),
mrconvert_keyval=path.from_user(app.ARGS.input, False),
force=app.FORCE_OVERWRITE)
def execute():
import math, os, shutil
from mrtrix3 import app, file, image, path, run
lmax_option = ''
if app.args.lmax:
lmax_option = ' -lmax ' + app.args.lmax
convergence_change = 0.01 * app.args.convergence
for iteration in range(0, app.args.max_iters):
prefix = 'iter' + str(iteration) + '_'
# How to initialise response function?
# old dwi2response command used mean & standard deviation of DWI data; however
# this may force the output FODs to lmax=2 at the first iteration
# Chantal used a tensor with low FA, but it'd be preferable to get the scaling right
# Other option is to do as before, but get the ratio between l=0 and l=2, and
# generate l=4,6,... using that amplitude ratio
if iteration == 0:
RF_in_path = 'init_RF.txt'
mask_in_path = 'mask.mif'
# TODO This can be changed once #71 is implemented (mrstats statistics across volumes)
volume_means = [float(x) for x in image.statistic('dwi.mif', 'mean', 'mask.mif').split()]
mean = sum(volume_means) / float(len(volume_means))
volume_stds = [float(x) for x in image.statistic('dwi.mif', 'std', 'mask.mif').split()]
std = sum(volume_stds) / float(len(volume_stds))
# Scale these to reflect the fact that we're moving to the SH basis
mean *= math.sqrt(4.0 * math.pi)
std *= math.sqrt(4.0 * math.pi)
# Now produce the initial response function
# Let's only do it to lmax 4
init_RF = [ str(mean), str(-0.5*std), str(0.25*std*std/mean) ]
with open('init_RF.txt', 'w') as f:
f.write(' '.join(init_RF))
else:
RF_in_path = 'iter' + str(iteration-1) + '_RF.txt'
mask_in_path = 'iter' + str(iteration-1) + '_SF.mif'
# Run CSD
run.command('dwi2fod csd dwi.mif ' + RF_in_path + ' ' + prefix + 'FOD.mif -mask ' + mask_in_path)
# Get amplitudes of two largest peaks, and directions of largest
run.command('fod2fixel ' + prefix + 'FOD.mif ' + prefix + 'fixel -peak peaks.mif -mask ' + mask_in_path + ' -fmls_no_thresholds')
file.delTempFile(prefix + 'FOD.mif')
run.command('fixel2voxel ' + prefix + 'fixel/peaks.mif split_data ' + prefix + 'amps.mif')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix + 'first_peaks.mif -coord 3 0 -axes 0,1,2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix + 'second_peaks.mif -coord 3 1 -axes 0,1,2')
file.delTempFile(prefix + 'amps.mif')
run.command('fixel2voxel ' + prefix + 'fixel/directions.mif split_dir ' + prefix + 'all_dirs.mif')
file.delTempFolder(prefix + 'fixel')
run.command('mrconvert ' + prefix + 'all_dirs.mif ' + prefix + 'first_dir.mif -coord 3 0:2')
file.delTempFile(prefix + 'all_dirs.mif')
# Revise single-fibre voxel selection based on ratio of tallest to second-tallest peak
run.command('mrcalc ' + prefix + 'second_peaks.mif ' + prefix + 'first_peaks.mif -div ' + prefix + 'peak_ratio.mif')
file.delTempFile(prefix + 'first_peaks.mif')
file.delTempFile(prefix + 'second_peaks.mif')
run.command('mrcalc ' + prefix + 'peak_ratio.mif ' + str(app.args.peak_ratio) + ' -lt ' + mask_in_path + ' -mult ' + prefix + 'SF.mif -datatype bit')
file.delTempFile(prefix + 'peak_ratio.mif')
# Make sure image isn't empty
SF_voxel_count = int(image.statistic(prefix + 'SF.mif', 'count', prefix + 'SF.mif'))
if not SF_voxel_count:
app.error('Aborting: All voxels have been excluded from single-fibre selection')
# Generate a new response function
run.command('amp2response dwi.mif ' + prefix + 'SF.mif ' + prefix + 'first_dir.mif ' + prefix + 'RF.txt' + lmax_option)
file.delTempFile(prefix + 'first_dir.mif')
# Detect convergence
# Look for a change > some percentage - don't bother looking at the masks
if iteration > 0:
with open(RF_in_path, 'r') as old_RF_file:
old_RF = [ float(x) for x in old_RF_file.read().split() ]
with open(prefix + 'RF.txt', 'r') as new_RF_file:
new_RF = [ float(x) for x in new_RF_file.read().split() ]
reiterate = False
for index in range(0, len(old_RF)):
mean = 0.5 * (old_RF[index] + new_RF[index])
diff = math.fabs(0.5 * (old_RF[index] - new_RF[index]))
ratio = diff / mean
if ratio > convergence_change:
reiterate = True
if not reiterate:
app.console('Exiting at iteration ' + str(iteration) + ' with ' + str(SF_voxel_count) + ' SF voxels due to unchanged response function coefficients')
run.function(shutil.copyfile, prefix + 'RF.txt', 'response.txt')
run.function(shutil.copyfile, prefix + 'SF.mif', 'voxels.mif')
break
file.delTempFile(RF_in_path)
file.delTempFile(mask_in_path)
# Go to the next iteration
# If we've terminated due to hitting the iteration limiter, we still need to copy the output file(s) to the correct location
if not os.path.exists('response.txt'):
app.console('Exiting after maximum ' + str(app.args.max_iters-1) + ' iterations with ' + str(SF_voxel_count) + ' SF voxels')
run.function(shutil.copyfile, 'iter' + str(app.args.max_iters-1) + '_RF.txt', 'response.txt')
run.function(shutil.copyfile, 'iter' + str(app.args.max_iters-1) + '_SF.mif', 'voxels.mif')
run.function(shutil.copyfile, 'response.txt', path.fromUser(app.args.output, False))
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, shutil
from mrtrix3 import app, image, matrix, MRtrixError, path, run
lmax_option = ''
if app.ARGS.lmax:
lmax_option = ' -lmax ' + app.ARGS.lmax
convergence_change = 0.01 * app.ARGS.convergence
progress = app.ProgressBar('Optimising')
iteration = 0
while iteration < app.ARGS.max_iters:
prefix = 'iter' + str(iteration) + '_'
# How to initialise response function?
# old dwi2response command used mean & standard deviation of DWI data; however
# this may force the output FODs to lmax=2 at the first iteration
# Chantal used a tensor with low FA, but it'd be preferable to get the scaling right
# Other option is to do as before, but get the ratio between l=0 and l=2, and
# generate l=4,6,... using that amplitude ratio
if iteration == 0:
rf_in_path = 'init_RF.txt'
mask_in_path = 'mask.mif'
# Grab the mean and standard deviation across all volumes in a single mrstats call
# Also scale them to reflect the fact that we're moving to the SH basis
mean = image.statistic('dwi.mif', 'mean',
'-mask mask.mif -allvolumes') * math.sqrt(
4.0 * math.pi)
std = image.statistic('dwi.mif', 'std',
'-mask mask.mif -allvolumes') * math.sqrt(
4.0 * math.pi)
# Now produce the initial response function
# Let's only do it to lmax 4
init_rf = [
str(mean),
str(-0.5 * std),
str(0.25 * std * std / mean)
]
with open('init_RF.txt', 'w') as init_rf_file:
init_rf_file.write(' '.join(init_rf))
else:
rf_in_path = 'iter' + str(iteration - 1) + '_RF.txt'
mask_in_path = 'iter' + str(iteration - 1) + '_SF.mif'
# Run CSD
run.command('dwi2fod csd dwi.mif ' + rf_in_path + ' ' + prefix +
'FOD.mif -mask ' + mask_in_path)
# Get amplitudes of two largest peaks, and directions of largest
run.command('fod2fixel ' + prefix + 'FOD.mif ' + prefix +
'fixel -peak peaks.mif -mask ' + mask_in_path +
' -fmls_no_thresholds')
app.cleanup(prefix + 'FOD.mif')
run.command('fixel2voxel ' + prefix + 'fixel/peaks.mif split_data ' +
prefix + 'amps.mif')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'first_peaks.mif -coord 3 0 -axes 0,1,2')
run.command('mrconvert ' + prefix + 'amps.mif ' + prefix +
'second_peaks.mif -coord 3 1 -axes 0,1,2')
app.cleanup(prefix + 'amps.mif')
run.command('fixel2voxel ' + prefix +
'fixel/directions.mif split_dir ' + prefix +
'all_dirs.mif')
app.cleanup(prefix + 'fixel')
run.command('mrconvert ' + prefix + 'all_dirs.mif ' + prefix +
'first_dir.mif -coord 3 0:2')
app.cleanup(prefix + 'all_dirs.mif')
# Revise single-fibre voxel selection based on ratio of tallest to second-tallest peak
run.command('mrcalc ' + prefix + 'second_peaks.mif ' + prefix +
'first_peaks.mif -div ' + prefix + 'peak_ratio.mif')
app.cleanup(prefix + 'first_peaks.mif')
app.cleanup(prefix + 'second_peaks.mif')
run.command('mrcalc ' + prefix + 'peak_ratio.mif ' +
str(app.ARGS.peak_ratio) + ' -lt ' + mask_in_path +
' -mult ' + prefix + 'SF.mif -datatype bit')
app.cleanup(prefix + 'peak_ratio.mif')
# Make sure image isn't empty
sf_voxel_count = image.statistic(prefix + 'SF.mif', 'count',
'-mask ' + prefix + 'SF.mif')
if not sf_voxel_count:
raise MRtrixError(
'Aborting: All voxels have been excluded from single-fibre selection'
)
# Generate a new response function
run.command('amp2response dwi.mif ' + prefix + 'SF.mif ' + prefix +
'first_dir.mif ' + prefix + 'RF.txt' + lmax_option)
app.cleanup(prefix + 'first_dir.mif')
new_rf = matrix.load_vector(prefix + 'RF.txt')
progress.increment('Optimising (' + str(iteration + 1) +
' iterations, ' + str(sf_voxel_count) +
' voxels, RF: [ ' + ', '.join('{:.3f}'.format(n)
for n in new_rf) +
'] )')
# Detect convergence
# Look for a change > some percentage - don't bother looking at the masks
if iteration > 0:
old_rf = matrix.load_vector(rf_in_path)
reiterate = False
for old_value, new_value in zip(old_rf, new_rf):
mean = 0.5 * (old_value + new_value)
diff = math.fabs(0.5 * (old_value - new_value))
ratio = diff / mean
if ratio > convergence_change:
reiterate = True
if not reiterate:
run.function(shutil.copyfile, prefix + 'RF.txt',
'response.txt')
run.function(shutil.copyfile, prefix + 'SF.mif', 'voxels.mif')
break
app.cleanup(rf_in_path)
app.cleanup(mask_in_path)
iteration += 1
progress.done()
# If we've terminated due to hitting the iteration limiter, we still need to copy the output file(s) to the correct location
if os.path.exists('response.txt'):
app.console('Exited at iteration ' + str(iteration + 1) + ' with ' +
str(sf_voxel_count) +
' SF voxels due to unchanged RF coefficients')
else:
app.console('Exited after maximum ' + str(app.ARGS.max_iters) +
' iterations with ' + str(sf_voxel_count) + ' SF voxels')
run.function(shutil.copyfile,
'iter' + str(app.ARGS.max_iters - 1) + '_RF.txt',
'response.txt')
run.function(shutil.copyfile,
'iter' + str(app.ARGS.max_iters - 1) + '_SF.mif',
'voxels.mif')
run.function(shutil.copyfile, 'response.txt',
path.from_user(app.ARGS.output, False))
if app.ARGS.voxels:
run.command('mrconvert voxels.mif ' + path.from_user(app.ARGS.voxels),
mrconvert_keyval=path.from_user(app.ARGS.input),
force=app.FORCE_OVERWRITE)
def execute():
import math, os, shutil
from mrtrix3 import app, image, path, run
# 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
run.command('5ttcheck 5tt.mif', False)
# Get shell information
shells = [ int(round(float(x))) for x in image.headerField('dwi.mif', 'shells').split() ]
if len(shells) < 3:
app.warn('Less than three b-value shells; response functions will not be applicable in resolving three tissues using MSMT-CSD algorithm')
# Get lmax information (if provided)
wm_lmax = [ ]
if app.args.lmax:
wm_lmax = [ int(x.strip()) for x in app.args.lmax.split(',') ]
if not len(wm_lmax) == len(shells):
app.error('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:
app.error('Values for lmax must be even')
if l<0:
app.error('Values for lmax must be non-negative')
run.command('dwi2tensor dwi.mif - -mask mask.mif | tensor2metric - -fa fa.mif -vector vector.mif')
if not os.path.exists('dirs.mif'):
run.function(shutil.copy, 'vector.mif', 'dirs.mif')
run.command('mrtransform 5tt.mif 5tt_regrid.mif -template fa.mif -interp linear')
# Basic tissue masks
run.command('mrconvert 5tt_regrid.mif - -coord 3 2 -axes 0,1,2 | mrcalc - ' + str(app.args.pvf) + ' -gt mask.mif -mult wm_mask.mif')
run.command('mrconvert 5tt_regrid.mif - -coord 3 0 -axes 0,1,2 | mrcalc - ' + str(app.args.pvf) + ' -gt fa.mif ' + str(app.args.fa) + ' -lt -mult mask.mif -mult gm_mask.mif')
run.command('mrconvert 5tt_regrid.mif - -coord 3 3 -axes 0,1,2 | mrcalc - ' + str(app.args.pvf) + ' -gt fa.mif ' + str(app.args.fa) + ' -lt -mult mask.mif -mult csf_mask.mif')
# Revise WM mask to only include single-fibre voxels
app.console('Calling dwi2response recursively to select WM single-fibre voxels using \'' + app.args.wm_algo + '\' algorithm')
recursive_cleanup_option=''
if not app._cleanup:
recursive_cleanup_option = ' -nocleanup'
run.command('dwi2response ' + app.args.wm_algo + ' dwi.mif wm_ss_response.txt -mask wm_mask.mif -voxels wm_sf_mask.mif -tempdir ' + app._tempDir + recursive_cleanup_option)
# Check for empty masks
wm_voxels = int(image.statistic('wm_sf_mask.mif', 'count', 'wm_sf_mask.mif'))
gm_voxels = int(image.statistic('gm_mask.mif', 'count', 'gm_mask.mif'))
csf_voxels = int(image.statistic('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'
app.error(message)
# For each of the three tissues, generate a multi-shell response
bvalues_option = ' -shell ' + ','.join(map(str,shells))
sfwm_lmax_option = ''
if wm_lmax:
sfwm_lmax_option = ' -lmax ' + ','.join(map(str,wm_lmax))
run.command('amp2response dwi.mif wm_sf_mask.mif dirs.mif wm.txt' + bvalues_option + sfwm_lmax_option)
run.command('amp2response dwi.mif gm_mask.mif dirs.mif gm.txt' + bvalues_option + ' -isotropic')
run.command('amp2response dwi.mif csf_mask.mif dirs.mif csf.txt' + bvalues_option + ' -isotropic')
run.function(shutil.copyfile, 'wm.txt', path.fromUser(app.args.out_wm, False))
run.function(shutil.copyfile, 'gm.txt', path.fromUser(app.args.out_gm, False))
run.function(shutil.copyfile, 'csf.txt', path.fromUser(app.args.out_csf, False))
# Generate output 4D binary image with voxel selections; RGB as in MSMT-CSD paper
run.command('mrcat csf_mask.mif gm_mask.mif wm_sf_mask.mif voxels.mif -axis 3')
