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(): #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)