def execute(): #pylint: disable=unused-variable
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
image_stats = image.statistics('dwi.mif',
mask='mask.mif',
allvolumes=True)
mean = image_stats.mean * math.sqrt(4.0 * math.pi)
std = image_stats.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 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 none ' +
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('fixel2peaks ' + prefix + 'fixel/directions.mif ' +
prefix + 'first_dir.mif -number 1')
app.cleanup(prefix + 'fixel')
# 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.statistics(prefix + 'SF.mif',
mask=prefix + 'SF.mif').count
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, False),
force=app.FORCE_OVERWRITE)
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(): #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
# 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)
