def function(fn, *args, **kwargs): #pylint: disable=unused-variable
import inspect, sys
from mrtrix3 import app
fnstring = fn.__module__ + '.' + fn.__name__ + \
'(' + ', '.join(['\'' + str(a) + '\'' if isinstance(a, str) else str(a) for a in args]) + \
(', ' if (args and kwargs) else '') + \
', '.join([key+'='+str(value) for key, value in kwargs.items()]) + ')'
if _lastFile:
if _triggerContinue(args) or _triggerContinue(kwargs.values()):
app.debug('Detected last file in function \'' + fnstring + '\'; this is the last run.command() / run.function() call that will be skipped')
if app.verbosity:
sys.stderr.write(app.colourExec + 'Skipping function:' + app.colourClear + ' ' + fnstring + '\n')
sys.stderr.flush()
return None
if app.verbosity:
sys.stderr.write(app.colourExec + 'Function:' + app.colourClear + ' ' + fnstring + '\n')
sys.stderr.flush()
# Now we need to actually execute the requested function
try:
if kwargs:
result = fn(*args, **kwargs)
else:
result = fn(*args)
except Exception as e: # pylint: disable=broad-except
app.cleanup = False
caller = inspect.getframeinfo(inspect.stack()[1][0])
error_text = str(type(e).__name__) + ': ' + str(e)
script_name = os.path.basename(sys.argv[0])
app.console('')
try:
filename = caller.filename
lineno = caller.lineno
except AttributeError:
filename = caller[1]
lineno = caller[2]
sys.stderr.write(script_name + ': ' + app.colourError + '[ERROR] Function failed: ' + fnstring + app.colourClear + app.colourDebug + ' (' + os.path.basename(filename) + ':' + str(lineno) + ')' + app.colourClear + '\n')
sys.stderr.write(script_name + ': ' + app.colourConsole + 'Information from failed function:' + app.colourClear + '\n')
for line in error_text.splitlines():
sys.stderr.write(' ' * (len(script_name)+2) + line + '\n')
app.console('')
sys.stderr.flush()
if app.tempDir:
with open(os.path.join(app.tempDir, 'error.txt'), 'w') as outfile:
outfile.write(fnstring + '\n\n' + error_text + '\n')
app.complete()
sys.exit(1)
# Only now do we append to the script log, since the function has completed successfully
if app.tempDir:
with open(os.path.join(app.tempDir, 'log.txt'), 'a') as outfile:
outfile.write(fnstring + '\n')
return result
def waitFor(path):
import os, time
from mrtrix3 import app
def inUse(path):
import subprocess
from distutils.spawn import find_executable
if app.isWindows():
if not os.access(path, os.W_OK):
return None
try:
with open(path, 'rb+') as f:
pass
return False
except:
return True
if not find_executable('fuser'):
return None
# fuser returns zero if there IS at least one process accessing the file
# A fatal error will result in a non-zero code -> inUse() = False, so waitFor() can return
return not subprocess.call(['fuser', '-s', path],
shell=False,
stdin=None,
stdout=None,
stderr=None)
if not os.path.exists(path):
delay = 1.0 / 1024.0
app.console('Waiting for creation of new file \"' + path + '\"')
while not os.path.exists(path):
time.sleep(delay)
delay = max(60.0, delay * 2.0)
app.debug('File \"' + path + '\" appears to have been created')
if not os.path.isfile(path):
app.debug('Path \"' + path +
'\" is not a file; not testing for finalization')
return
init_test = inUse(path)
if init_test is None:
app.debug('Unable to test for finalization of new file \"' + path +
'\"')
return
if not init_test:
app.debug('File \"' + path + '\" immediately ready')
return
app.console('Waiting for finalization of new file \"' + path + '\"')
delay = 1.0 / 1024.0
while True:
if inUse(path):
time.sleep(delay)
delay = max(60.0, delay * 2.0)
else:
app.debug('File \"' + path + '\" appears to have been finalized')
return
def delTempFile(path):
import os
from mrtrix3 import app
if not app._cleanup:
return
if app._verbosity > 2:
app.console('Deleting temporary file: ' + path)
try:
os.remove(path)
except OSError:
app.debug('Unable to delete temporary file ' + path)
def delTempFile(path):
import os
from mrtrix3 import app
if not app._cleanup:
return
if app._verbosity > 2:
app.console('Deleting temporary file: ' + path)
try:
os.remove(path)
except OSError:
app.debug('Unable to delete temporary file ' + path)
def delTempFolder(path):
import shutil
from mrtrix3 import app
if not app._cleanup:
return
if app._verbosity > 2:
app.console('Deleting temporary folder: ' + path)
try:
shutil.rmtree(path)
except OSError:
app.debug('Unable to delete temprary folder ' + path)
def delTempFolder(path):
import shutil
from mrtrix3 import app
if not app._cleanup:
return
if app._verbosity > 2:
app.console('Deleting temporary folder: ' + path)
try:
shutil.rmtree(path)
except OSError:
app.debug('Unable to delete temprary folder ' + path)
def headerKeyValue(image_path, key):
import subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrinfo')), image_path, '-property', key ]
if app._verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app._verbosity > 1:
app.console('Result: ' + result)
return result
def checkFirst(prefix, structures): #pylint: disable=unused-variable
import os
from mrtrix3 import app, file, path # pylint: disable=redefined-builtin
vtk_files = [ prefix + '-' + struct + '_first.vtk' for struct in structures ]
existing_file_count = sum([ os.path.exists(filename) for filename in vtk_files ])
if existing_file_count != len(vtk_files):
if 'SGE_ROOT' in os.environ:
app.console('FSL FIRST job has been submitted to SGE; awaiting completion')
app.console('(note however that FIRST may fail silently, and hence this script may hang indefinitely)')
file.waitFor(vtk_files)
else:
app.error('FSL FIRST has failed; only ' + str(existing_file_count) + ' of ' + str(len(vtk_files)) + ' structures were segmented successfully (check ' + path.toTemp('first.logs', False) + ')')
def check_first(prefix, structures): #pylint: disable=unused-variable
import os
from mrtrix3 import app, MRtrixError, path
vtk_files = [ prefix + '-' + struct + '_first.vtk' for struct in structures ]
existing_file_count = sum([ os.path.exists(filename) for filename in vtk_files ])
if existing_file_count != len(vtk_files):
if 'SGE_ROOT' in os.environ and os.environ['SGE_ROOT']:
app.console('FSL FIRST job may have been run via SGE; awaiting completion')
app.console('(note however that FIRST may fail silently, and hence this script may hang indefinitely)')
path.wait_for(vtk_files)
else:
raise MRtrixError('FSL FIRST has failed; ' + ('only ' if existing_file_count else '') + str(existing_file_count) + ' of ' + str(len(vtk_files)) + ' structures were segmented successfully (check ' + path.to_scratch('first.logs', False) + ')')
def mrinfo(image_path, field): #pylint: disable=unused-variable
from mrtrix3 import app, run #pylint: disable=import-outside-toplevel
command = [ run.exe_name(run.version_match('mrinfo')), image_path, '-' + field ]
if app.VERBOSITY > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None) #pylint: disable=consider-using-with
result = proc.communicate()[0].rstrip().decode('utf-8')
if app.VERBOSITY > 1:
app.console('Result: ' + result)
# Don't exit on error; let the calling function determine whether or not
# the absence of the key is an issue
return result
def mrinfo(image_path, field): #pylint: disable=unused-variable
import subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrinfo')), image_path, '-' + field ]
if app.verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, dummy_err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app.verbosity > 1:
app.console('Result: ' + result)
# Don't exit on error; let the calling function determine whether or not
# the absence of the key is an issue
return result
def mrinfo(image_path, field): #pylint: disable=unused-variable
import subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrinfo')), image_path, '-' + field ]
if app.verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, dummy_err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app.verbosity > 1:
app.console('Result: ' + result)
# Don't exit on error; let the calling function determine whether or not
# the absence of the key is an issue
return result
def statistic(image_path, statistic, mask_path = ''):
import subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrstats')), image_path, '-output', statistic ]
if mask_path:
command.extend([ '-mask', mask_path ])
if app._verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app._verbosity > 1:
app.console('Result: ' + result)
return result
def statistics(image_path, **kwargs): #pylint: disable=unused-variable
from mrtrix3 import app, run #pylint: disable=import-outside-toplevel
mask = kwargs.pop('mask', None)
allvolumes = kwargs.pop('allvolumes', False)
ignorezero = kwargs.pop('ignorezero', False)
if kwargs:
raise TypeError(
'Unsupported keyword arguments passed to image.statistics(): ' +
str(kwargs))
command = [run.exe_name(run.version_match('mrstats')), image_path]
for stat in IMAGE_STATISTICS:
command.extend(['-output', stat])
if mask:
command.extend(['-mask', mask])
if allvolumes:
command.append('-allvolumes')
if ignorezero:
command.append('-ignorezero')
if app.VERBOSITY > 1:
app.console('Command: \'' + ' '.join(command) +
'\' (piping data to local storage)')
try:
from subprocess import DEVNULL #pylint: disable=import-outside-toplevel
except ImportError:
DEVNULL = open(os.devnull, 'wb')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=DEVNULL)
stdout = proc.communicate()[0]
if proc.returncode:
raise MRtrixError(
'Error trying to calculate statistics from image \'' + image_path +
'\'')
stdout_lines = [
line.strip() for line in stdout.decode('cp437').splitlines()
]
result = []
for line in stdout_lines:
line = line.replace('N/A', 'nan').split()
assert len(line) == len(IMAGE_STATISTICS)
result.append(
ImageStatistics(float(line[0]), float(line[1]), float(line[2]),
float(line[3]), float(line[4]), float(line[5]),
int(line[6])))
if len(result) == 1:
result = result[0]
if app.VERBOSITY > 1:
app.console('Result: ' + str(result))
return result
def statistic(image_path, stat, options=''): #pylint: disable=unused-variable
import shlex, subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrstats')), image_path, '-output', stat ]
if options:
command.extend(shlex.split(options))
if app.verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, dummy_err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app.verbosity > 1:
app.console('Result: ' + result)
if proc.returncode:
app.error('Error trying to calculate statistic \'' + stat + '\' from image \'' + image_path + '\'')
return result
def statistic(image_path, stat, options=''): #pylint: disable=unused-variable
import shlex, subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrstats')), image_path, '-output', stat ]
if options:
command.extend(shlex.split(options))
if app.verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, dummy_err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app.verbosity > 1:
app.console('Result: ' + result)
if proc.returncode:
app.error('Error trying to calculate statistic \'' + stat + '\' from image \'' + image_path + '\'')
return result
def __init__(self, image_path):
from mrtrix3 import app, path, run #pylint: disable=import-outside-toplevel
filename = path.name_temporary('json')
command = [
run.exe_name(run.version_match('mrinfo')), image_path, '-json_all',
filename
]
if app.VERBOSITY > 1:
app.console('Loading header for image file \'' + image_path + '\'')
app.debug(str(command))
result = subprocess.call(command, stdout=None, stderr=None)
if result:
raise MRtrixError(
'Could not access header information for image \'' +
image_path + '\'')
try:
with open(filename, 'r') as json_file:
data = json.load(json_file)
except UnicodeDecodeError:
with open(filename, 'r') as json_file:
data = json.loads(json_file.read().decode('utf-8',
errors='replace'))
os.remove(filename)
try:
#self.__dict__.update(data)
# Load the individual header elements manually, for a couple of reasons:
# - So that pylint knows that they'll be there
# - Write to private members, and give read-only access
self._name = data['name']
self._size = data['size']
self._spacing = data['spacing']
self._strides = data['strides']
self._format = data['format']
self._datatype = data['datatype']
self._intensity_offset = data['intensity_offset']
self._intensity_scale = data['intensity_scale']
self._transform = data['transform']
if not 'keyval' in data or not data['keyval']:
self._keyval = {}
else:
self._keyval = data['keyval']
except:
raise MRtrixError(
'Error in reading header information from file \'' +
image_path + '\'')
app.debug(str(vars(self)))
def checkFirst(prefix, structures): #pylint: disable=unused-variable
import os
from mrtrix3 import app, file, path # pylint: disable=redefined-builtin
vtk_files = [prefix + '-' + struct + '_first.vtk' for struct in structures]
existing_file_count = sum(
[os.path.exists(filename) for filename in vtk_files])
if existing_file_count != len(vtk_files):
if 'SGE_ROOT' in os.environ:
app.console(
'FSL FIRST job has been submitted to SGE; awaiting completion')
app.console(
'(note however that FIRST may fail silently, and hence this script may hang indefinitely)'
)
file.waitFor(vtk_files)
else:
app.error('FSL FIRST has failed; only ' +
str(existing_file_count) + ' of ' + str(len(vtk_files)) +
' structures were segmented successfully (check ' +
path.toTemp('first.logs', False) + ')')
def delTemporary(path): #pylint: disable=unused-variable
import shutil, os
from mrtrix3 import app
if not app.cleanup:
return
if os.path.isfile(path):
temporary_type = 'file'
func = os.remove
elif os.path.isdir(path):
temporary_type = 'directory'
func = shutil.rmtree
else:
app.debug('Unknown target \'' + path + '\'')
return
if app.verbosity > 2:
app.console('Deleting temporary ' + temporary_type + ': \'' + path + '\'')
try:
func(path)
except OSError:
app.debug('Unable to delete temporary ' + temporary_type + ': \'' + path + '\'')
def statistic(image_path, stat, options=''): #pylint: disable=unused-variable
import shlex, subprocess
from mrtrix3 import app, MRtrixError, run
command = [ run.exe_name(run.version_match('mrstats')), image_path, '-output', stat ]
if options:
command.extend(shlex.split(options))
if app.VERBOSITY > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result = [ line.strip() for line in proc.communicate()[0].decode('cp437').splitlines() ]
if stat == 'count':
result = [ int(i) for i in result ]
else:
result = [ float(f) for f in result ]
if len(result) == 1:
result = result[0]
if app.VERBOSITY > 1:
app.console('Result: ' + str(result))
if proc.returncode:
raise MRtrixError('Error trying to calculate statistic \'' + stat + '\' from image \'' + image_path + '\'')
return result
def __init__(self, image_path):
import json, os, subprocess
from mrtrix3 import app, path, run
filename = path.newTemporary('json')
command = [
run.exeName(run.versionMatch('mrinfo')), image_path, '-json_all',
filename
]
if app.verbosity > 1:
app.console('Loading header for image file \'' + image_path + '\'')
app.debug(str(command))
result = subprocess.call(command, stdout=None, stderr=None)
if result:
app.error('Could not access header information for image \'' +
image_path + '\'')
with open(filename, 'r') as f:
data = json.load(f)
os.remove(filename)
try:
#self.__dict__.update(data)
# Load the individual header elements manually, for a couple of reasons:
# - So that pylint knows that they'll be there
# - Write to private members, and give read-only access
self._name = data['name']
self._size = data['size']
self._spacing = data['spacing']
self._strides = data['strides']
self._format = data['format']
self._datatype = data['datatype']
self._intensity_offset = data['intensity_offset']
self._intensity_scale = data['intensity_scale']
self._transform = data['transform']
if not 'keyval' in data or not data['keyval']:
self._keyval = {}
else:
self._keyval = data['keyval']
except:
app.error('Error in reading header information from file \'' +
image_path + '\'')
app.debug(str(vars(self)))
def __init__(self, image_path):
import json, os, subprocess
from mrtrix3 import app, path, run
filename = path.newTemporary('json')
command = [ run.exeName(run.versionMatch('mrinfo')), image_path, '-json_all', filename ]
if app.verbosity > 1:
app.console('Loading header for image file \'' + image_path + '\'')
app.debug(str(command))
result = subprocess.call(command, stdout=None, stderr=None)
if result:
app.error('Could not access header information for image \'' + image_path + '\'')
try:
with open(filename, 'r') as f:
data = json.load(f)
except UnicodeDecodeError:
with open(filename, 'r') as f:
data = json.loads(f.read().decode('utf-8', errors='replace'))
os.remove(filename)
try:
#self.__dict__.update(data)
# Load the individual header elements manually, for a couple of reasons:
# - So that pylint knows that they'll be there
# - Write to private members, and give read-only access
self._name = data['name']
self._size = data['size']
self._spacing = data['spacing']
self._strides = data['strides']
self._format = data['format']
self._datatype = data['datatype']
self._intensity_offset = data['intensity_offset']
self._intensity_scale = data['intensity_scale']
self._transform = data['transform']
if not 'keyval' in data or not data['keyval']:
self._keyval = { }
else:
self._keyval = data['keyval']
except:
app.error('Error in reading header information from file \'' + image_path + '\'')
app.debug(str(vars(self)))
def delTemporary(path): #pylint: disable=unused-variable
import shutil, os
from mrtrix3 import app
if not app.cleanup:
return
if os.path.isfile(path):
temporary_type = 'file'
func = os.remove
elif os.path.isdir(path):
temporary_type = 'directory'
func = shutil.rmtree
else:
app.debug('Unknown target \'' + path + '\'')
return
if app.verbosity > 2:
app.console('Deleting temporary ' + temporary_type + ': \'' + path +
'\'')
try:
func(path)
except OSError:
app.debug('Unable to delete temporary ' + temporary_type + ': \'' +
path + '\'')
def headerField(image_path, field):
import subprocess
from mrtrix3 import app, run
command = [ run.exeName(run.versionMatch('mrinfo')), image_path, '-' + field ]
if app._verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, err = proc.communicate()
result = result.rstrip().decode('utf-8')
if app._verbosity > 1:
if '\n' in result:
app.console('Result: (' + str(result.count('\n')+1) + ' lines)')
app.debug(result)
else:
app.console('Result: ' + result)
return result
def getScheme(image_path):
import subprocess
from mrtrix3 import app, run
command = [ run.versionMatch('mrinfo'), image_path, '-petable' ]
if app._verbosity > 1:
app.console('Command: \'' + ' '.join(command) + '\' (piping data to local storage)')
proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=None)
result, err = proc.communicate()
result = result.rstrip().decode('utf-8')
if result:
result = [ [ float(f) for f in line.split() ] for line in result.split('\n') ]
if app._verbosity > 1:
if not result:
app.console('Result: No phase encoding table found')
else:
app.console('Result: ' + str(len(result)) + ' x ' + str(len(result[0])) + ' table')
app.debug(str(result))
return result
def command(cmd, exitOnError=True): #pylint: disable=unused-variable
import inspect, itertools, shlex, signal, string, subprocess, sys, tempfile
from distutils.spawn import find_executable
from mrtrix3 import app
# This is the only global variable that is _modified_ within this function
global _processes
# Vectorise the command string, preserving anything encased within quotation marks
if os.sep == '/': # Cheap POSIX compliance check
cmdsplit = shlex.split(cmd)
else: # Native Windows Python
cmdsplit = [ entry.strip('\"') for entry in shlex.split(cmd, posix=False) ]
if _lastFile:
if _triggerContinue(cmdsplit):
app.debug('Detected last file in command \'' + cmd + '\'; this is the last run.command() / run.function() call that will be skipped')
if app.verbosity:
sys.stderr.write(app.colourExec + 'Skipping command:' + app.colourClear + ' ' + cmd + '\n')
sys.stderr.flush()
return ('', '')
# This splits the command string based on the piping character '|', such that each
# individual executable (along with its arguments) appears as its own list
cmdstack = [ list(g) for k, g in itertools.groupby(cmdsplit, lambda s : s != '|') if k ]
for line in cmdstack:
is_mrtrix_exe = line[0] in _mrtrix_exe_list
if is_mrtrix_exe:
line[0] = versionMatch(line[0])
if app.numThreads is not None:
line.extend( [ '-nthreads', str(app.numThreads) ] )
# Get MRtrix3 binaries to output additional INFO-level information if running in debug mode
if app.verbosity == 3:
line.append('-info')
elif not app.verbosity:
line.append('-quiet')
else:
line[0] = exeName(line[0])
shebang = _shebang(line[0])
if shebang:
if not is_mrtrix_exe:
# If a shebang is found, and this call is therefore invoking an
# interpreter, can't rely on the interpreter finding the script
# from PATH; need to find the full path ourselves.
line[0] = find_executable(line[0])
for item in reversed(shebang):
line.insert(0, item)
app.debug('To execute: ' + str(cmdstack))
if app.verbosity:
sys.stderr.write(app.colourExec + 'Command:' + app.colourClear + ' ' + cmd + '\n')
sys.stderr.flush()
# Disable interrupt signal handler while threads are running
try:
signal.signal(signal.SIGINT, signal.default_int_handler)
except:
pass
# Construct temporary text files for holding stdout / stderr contents when appropriate
# (One entry per process; each is a tuple containing two entries, each of which is either a
# file-like object, or None)
tempfiles = [ ]
# Execute all processes
assert not _processes
for index, to_execute in enumerate(cmdstack):
file_out = None
file_err = None
# If there's at least one command prior to this, need to receive the stdout from the prior command
# at the stdin of this command; otherwise, nothing to receive
if index > 0:
handle_in = _processes[index-1].stdout
else:
handle_in = None
# If this is not the last command, then stdout needs to be piped to the next command;
# otherwise, write stdout to a temporary file so that the contents can be read later
if index < len(cmdstack)-1:
handle_out = subprocess.PIPE
else:
file_out = tempfile.TemporaryFile()
handle_out = file_out.fileno()
# If we're in debug / info mode, the contents of stderr will be read and printed to the terminal
# as the command progresses, hence this needs to go to a pipe; otherwise, write it to a temporary
# file so that the contents can be read later
if app.verbosity > 1:
handle_err = subprocess.PIPE
else:
file_err = tempfile.TemporaryFile()
handle_err = file_err.fileno()
# Set off the processes
try:
try:
process = subprocess.Popen (to_execute, stdin=handle_in, stdout=handle_out, stderr=handle_err, env=_env, preexec_fn=os.setpgrp) # pylint: disable=bad-option-value,subprocess-popen-preexec-fn
except AttributeError:
process = subprocess.Popen (to_execute, stdin=handle_in, stdout=handle_out, stderr=handle_err, env=_env)
_processes.append(process)
tempfiles.append( ( file_out, file_err ) )
# FileNotFoundError not defined in Python 2.7
except OSError as e:
if exitOnError:
app.error('\'' + to_execute[0] + '\' not executed ("' + str(e) + '"); script cannot proceed')
else:
app.warn('\'' + to_execute[0] + '\' not executed ("' + str(e) + '")')
for p in _processes:
p.terminate()
_processes = [ ]
break
return_stdout = ''
return_stderr = ''
error = False
error_text = ''
# Wait for all commands to complete
# Switch how we monitor running processes / wait for them to complete
# depending on whether or not the user has specified -info or -debug option
try:
if app.verbosity > 1:
for process in _processes:
stderrdata = b''
do_indent = True
while True:
# Have to read one character at a time: Waiting for a newline character using e.g. readline() will prevent MRtrix progressbars from appearing
byte = process.stderr.read(1)
stderrdata += byte
char = byte.decode('cp1252', errors='ignore')
if not char and process.poll() is not None:
break
if do_indent and char in string.printable and char != '\r' and char != '\n':
sys.stderr.write(' ')
do_indent = False
elif char in [ '\r', '\n' ]:
do_indent = True
sys.stderr.write(char)
sys.stderr.flush()
stderrdata = stderrdata.decode('utf-8', errors='replace')
return_stderr += stderrdata
if process.returncode:
error = True
error_text += stderrdata
else:
for process in _processes:
process.wait()
except (KeyboardInterrupt, SystemExit):
app.handler(signal.SIGINT, inspect.currentframe())
# Re-enable interrupt signal handler
try:
signal.signal(signal.SIGINT, app.handler)
except:
pass
# For any command stdout / stderr data that wasn't either passed to another command or
# printed to the terminal during execution, read it here.
for index in range(len(cmdstack)):
if tempfiles[index][0] is not None:
tempfiles[index][0].flush()
tempfiles[index][0].seek(0)
stdout_text = tempfiles[index][0].read().decode('utf-8', errors='replace')
return_stdout += stdout_text
if _processes[index].returncode:
error = True
error_text += stdout_text
if tempfiles[index][1] is not None:
tempfiles[index][1].flush()
tempfiles[index][1].seek(0)
stderr_text = tempfiles[index][1].read().decode('utf-8', errors='replace')
return_stderr += stderr_text
if _processes[index].returncode:
error = True
error_text += stderr_text
_processes = [ ]
if error:
if exitOnError:
app.cleanup = False
caller = inspect.getframeinfo(inspect.stack()[1][0])
script_name = os.path.basename(sys.argv[0])
app.console('')
try:
filename = caller.filename
lineno = caller.lineno
except AttributeError:
filename = caller[1]
lineno = caller[2]
sys.stderr.write(script_name + ': ' + app.colourError + '[ERROR] Command failed: ' + cmd + app.colourClear + app.colourDebug + ' (' + os.path.basename(filename) + ':' + str(lineno) + ')' + app.colourClear + '\n')
sys.stderr.write(script_name + ': ' + app.colourConsole + 'Output of failed command:' + app.colourClear + '\n')
for line in error_text.splitlines():
sys.stderr.write(' ' * (len(script_name)+2) + line + '\n')
app.console('')
sys.stderr.flush()
if app.tempDir:
with open(os.path.join(app.tempDir, 'error.txt'), 'w') as outfile:
outfile.write(cmd + '\n\n' + error_text + '\n')
app.complete()
sys.exit(1)
else:
app.warn('Command failed: ' + cmd)
# Only now do we append to the script log, since the command has completed successfully
# Note: Writing the command as it was formed as the input to run.command():
# other flags may potentially change if this file is eventually used to resume the script
if app.tempDir:
with open(os.path.join(app.tempDir, 'log.txt'), 'a') as outfile:
outfile.write(cmd + '\n')
return (return_stdout, return_stderr)
def execute(): #pylint: disable=unused-variable
class Input(object):
def __init__(self, filename, prefix, mask_filename=''):
self.filename = filename
self.prefix = prefix
self.mask_filename = mask_filename
input_dir = path.from_user(app.ARGS.input_dir, False)
if not os.path.exists(input_dir):
raise MRtrixError('input directory not found')
in_files = path.all_in_dir(input_dir, dir_path=False)
if len(in_files) <= 1:
raise MRtrixError(
'not enough images found in input directory: more than one image is needed to perform a group-wise intensity normalisation'
)
app.console('performing global intensity normalisation on ' +
str(len(in_files)) + ' input images')
mask_dir = path.from_user(app.ARGS.mask_dir, False)
if not os.path.exists(mask_dir):
raise MRtrixError('mask directory not found')
mask_files = path.all_in_dir(mask_dir, dir_path=False)
if len(mask_files) != len(in_files):
raise MRtrixError(
'the number of images in the mask directory does not equal the number of images in the input directory'
)
mask_common_postfix = os.path.commonprefix([i[::-1]
for i in mask_files])[::-1]
mask_prefixes = []
for mask_file in mask_files:
mask_prefixes.append(mask_file.split(mask_common_postfix)[0])
common_postfix = os.path.commonprefix([i[::-1] for i in in_files])[::-1]
input_list = []
for i in in_files:
subj_prefix = i.split(common_postfix)[0]
if subj_prefix not in mask_prefixes:
raise MRtrixError(
'no matching mask image was found for input image ' + i)
image.check_3d_nonunity(os.path.join(input_dir, i))
index = mask_prefixes.index(subj_prefix)
input_list.append(Input(i, subj_prefix, mask_files[index]))
app.make_scratch_dir()
app.goto_scratch_dir()
path.make_dir('fa')
progress = app.ProgressBar('Computing FA images', len(input_list))
for i in input_list:
run.command('dwi2tensor ' +
path.quote(os.path.join(input_dir, i.filename)) +
' -mask ' +
path.quote(os.path.join(mask_dir, i.mask_filename)) +
' - | tensor2metric - -fa ' +
os.path.join('fa', i.prefix + '.mif'))
progress.increment()
progress.done()
app.console('Generating FA population template')
run.command('population_template fa fa_template.mif' + ' -mask_dir ' +
mask_dir + ' -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' +
' -warp_dir warps' + ' -linear_no_pause' +
' -scratch population_template' +
('' if app.DO_CLEANUP else ' -nocleanup'))
app.console('Generating WM mask in template space')
run.command('mrthreshold fa_template.mif -abs ' + app.ARGS.fa_threshold +
' template_wm_mask.mif')
progress = app.ProgressBar('Intensity normalising subject images',
len(input_list))
path.make_dir(path.from_user(app.ARGS.output_dir, False))
path.make_dir('wm_mask_warped')
for i in input_list:
run.command(
'mrtransform template_wm_mask.mif -interp nearest -warp_full ' +
os.path.join('warps', i.prefix + '.mif') + ' ' +
os.path.join('wm_mask_warped', i.prefix + '.mif') +
' -from 2 -template ' + os.path.join('fa', i.prefix + '.mif'))
run.command('dwinormalise individual ' +
path.quote(os.path.join(input_dir, i.filename)) + ' ' +
os.path.join('wm_mask_warped', i.prefix + '.mif') +
' temp.mif')
run.command(
'mrconvert temp.mif ' +
path.from_user(os.path.join(app.ARGS.output_dir, i.filename)),
mrconvert_keyval=path.from_user(
os.path.join(input_dir, i.filename), False),
force=app.FORCE_OVERWRITE)
os.remove('temp.mif')
progress.increment()
progress.done()
app.console('Exporting template images to user locations')
run.command('mrconvert template_wm_mask.mif ' +
path.from_user(app.ARGS.wm_mask),
mrconvert_keyval='NULL',
force=app.FORCE_OVERWRITE)
run.command('mrconvert fa_template.mif ' +
path.from_user(app.ARGS.fa_template),
mrconvert_keyval='NULL',
force=app.FORCE_OVERWRITE)
def execute():
import os
from distutils.spawn import find_executable
from mrtrix3 import app, file, fsl, image, run
if app.isWindows():
app.error(
'\'fsl\' algorithm of 5ttgen script cannot be run on Windows: FSL not available on Windows'
)
fsl_path = os.environ.get('FSLDIR', '')
if not fsl_path:
app.error(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
ssroi_cmd = 'standard_space_roi'
if not find_executable(ssroi_cmd):
ssroi_cmd = 'fsl5.0-standard_space_roi'
if not find_executable(ssroi_cmd):
app.error(
'Could not find FSL program standard_space_roi; please verify FSL install'
)
bet_cmd = 'bet'
if not find_executable(bet_cmd):
bet_cmd = 'fsl5.0-bet'
if not find_executable(bet_cmd):
app.error(
'Could not find FSL program bet; please verify FSL install')
fast_cmd = 'fast'
if not find_executable(fast_cmd):
fast_cmd = 'fsl5.0-fast'
if not find_executable(fast_cmd):
app.error(
'Could not find FSL program fast; please verify FSL install')
first_cmd = 'run_first_all'
if not find_executable(first_cmd):
first_cmd = "fsl5.0-run_first_all"
if not find_executable(first_cmd):
app.error(
'Could not find FSL program run_first_all; please verify FSL install'
)
first_atlas_path = os.path.join(fsl_path, 'data', 'first',
'models_336_bin')
if not os.path.isdir(first_atlas_path):
app.error(
'Atlases required for FSL\'s FIRST program not installed; please install fsl-first-data using your relevant package manager'
)
fsl_suffix = fsl.suffix()
sgm_structures = [
'L_Accu', 'R_Accu', 'L_Caud', 'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta',
'R_Puta', 'L_Thal', 'R_Thal'
]
if app.args.sgm_amyg_hipp:
sgm_structures.extend(['L_Amyg', 'R_Amyg', 'L_Hipp', 'R_Hipp'])
run.command('mrconvert input.mif T1.nii -stride -1,+2,+3')
fast_t1_input = 'T1.nii'
fast_t2_input = ''
# Decide whether or not we're going to do any brain masking
if os.path.exists('mask.mif'):
fast_t1_input = 'T1_masked' + fsl_suffix
# Check to see if the mask matches the T1 image
if image.match('T1.nii', 'mask.mif'):
run.command('mrcalc T1.nii mask.mif -mult ' + fast_t1_input)
mask_path = 'mask.mif'
else:
app.warn('Mask image does not match input image - re-gridding')
run.command(
'mrtransform mask.mif mask_regrid.mif -template T1.nii')
run.command('mrcalc T1.nii mask_regrid.mif ' + fast_t1_input)
mask_path = 'mask_regrid.mif'
if os.path.exists('T2.nii'):
fast_t2_input = 'T2_masked' + fsl_suffix
run.command('mrcalc T2.nii ' + mask_path + ' -mult ' +
fast_t2_input)
elif app.args.premasked:
fast_t1_input = 'T1.nii'
if os.path.exists('T2.nii'):
fast_t2_input = 'T2.nii'
else:
# Use FSL command standard_space_roi to do an initial masking of the image before BET
# Also reduce the FoV of the image
# Using MNI 1mm dilated brain mask rather than the -b option in standard_space_roi (which uses the 2mm mask); the latter looks 'buggy' to me... Unfortunately even with the 1mm 'dilated' mask, it can still cut into some brain areas, hence the explicit dilation
mni_mask_path = os.path.join(fsl_path, 'data', 'standard',
'MNI152_T1_1mm_brain_mask_dil.nii.gz')
mni_mask_dilation = 0
if os.path.exists(mni_mask_path):
mni_mask_dilation = 4
else:
mni_mask_path = os.path.join(
fsl_path, 'data', 'standard',
'MNI152_T1_2mm_brain_mask_dil.nii.gz')
if os.path.exists(mni_mask_path):
mni_mask_dilation = 2
if mni_mask_dilation:
run.command('maskfilter ' + mni_mask_path +
' dilate mni_mask.nii -npass ' +
str(mni_mask_dilation))
if app.args.nocrop:
ssroi_roi_option = ' -roiNONE'
else:
ssroi_roi_option = ' -roiFOV'
run.command(
ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix +
' -maskMASK mni_mask.nii' + ssroi_roi_option, False)
else:
run.command(ssroi_cmd + ' T1.nii T1_preBET' + fsl_suffix + ' -b',
False)
# For whatever reason, the output file from standard_space_roi may not be
# completed before BET is run
file.waitFor('T1_preBET' + fsl_suffix)
# BET
fast_t1_input = 'T1_BET' + fsl_suffix
run.command(bet_cmd + ' T1_preBET' + fsl_suffix + ' ' + fast_t1_input +
' -f 0.15 -R')
if os.path.exists('T2.nii'):
if app.args.nocrop:
fast_t2_input = 'T2.nii'
else:
# Just a reduction of FoV, no sub-voxel interpolation going on
run.command('mrtransform T2.nii T2_cropped.nii -template ' +
fast_t1_input + ' -interp nearest')
fast_t2_input = 'T2_cropped.nii'
# Finish branching based on brain masking
# FAST
if fast_t2_input:
run.command(fast_cmd + ' -S 2 ' + fast_t2_input + ' ' + fast_t1_input)
else:
run.command(fast_cmd + ' ' + fast_t1_input)
fast_output_prefix = fast_t1_input.split('.')[0]
# FIRST
first_input_is_brain_extracted = ''
if app.args.premasked:
first_input_is_brain_extracted = ' -b'
run.command(first_cmd + ' -s ' + ','.join(sgm_structures) +
' -i T1.nii -o first' + first_input_is_brain_extracted)
# Test to see whether or not FIRST has succeeded
# However if the expected image is absent, it may be due to FIRST being run
# on SGE; in this case it is necessary to wait and see if the file appears.
# But even in this case, FIRST may still fail, and the file will never appear...
combined_image_path = 'first_all_none_firstseg' + fsl_suffix
if not os.path.isfile(combined_image_path):
if 'SGE_ROOT' in os.environ:
app.console(
'FSL FIRST job has been submitted to SGE; awaiting completion')
app.console(
'(note however that FIRST may fail, and hence this script may hang indefinitely)'
)
file.waitFor(combined_image_path)
else:
app.error(
'FSL FIRST has failed; not all structures were segmented successfully (check '
+ path.toTemp('first.logs', False) + ')')
# Convert FIRST meshes to partial volume images
pve_image_list = []
for struct in sgm_structures:
pve_image_path = 'mesh2pve_' + struct + '.mif'
vtk_in_path = 'first-' + struct + '_first.vtk'
vtk_temp_path = struct + '.vtk'
run.command('meshconvert ' + vtk_in_path + ' ' + vtk_temp_path +
' -transform first2real T1.nii')
run.command('mesh2pve ' + vtk_temp_path + ' ' + fast_t1_input + ' ' +
pve_image_path)
pve_image_list.append(pve_image_path)
pve_cat = ' '.join(pve_image_list)
run.command('mrmath ' + pve_cat +
' sum - | mrcalc - 1.0 -min all_sgms.mif')
# Looks like FAST in 5.0 ignores FSLOUTPUTTYPE when writing the PVE images
# Will have to wait and see whether this changes, and update the script accordingly
if fast_cmd == 'fast':
fast_suffix = fsl_suffix
else:
fast_suffix = '.nii.gz'
# Combine the tissue images into the 5TT format within the script itself
# Step 1: Run LCC on the WM image
run.command(
'mrthreshold ' + fast_output_prefix + '_pve_2' + fast_suffix +
' - -abs 0.001 | maskfilter - connect - -connectivity | mrcalc 1 - 1 -gt -sub remove_unconnected_wm_mask.mif -datatype bit'
)
# Step 2: Generate the images in the same fashion as the 5ttgen command
run.command('mrcalc ' + fast_output_prefix + '_pve_0' + fast_suffix +
' remove_unconnected_wm_mask.mif -mult csf.mif')
run.command('mrcalc 1.0 csf.mif -sub all_sgms.mif -min sgm.mif')
run.command('mrcalc 1.0 csf.mif sgm.mif -add -sub ' + fast_output_prefix +
'_pve_1' + fast_suffix + ' ' + fast_output_prefix + '_pve_2' +
fast_suffix + ' -add -div multiplier.mif')
run.command(
'mrcalc multiplier.mif -finite multiplier.mif 0.0 -if multiplier_noNAN.mif'
)
run.command(
'mrcalc ' + fast_output_prefix + '_pve_1' + fast_suffix +
' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult cgm.mif'
)
run.command(
'mrcalc ' + fast_output_prefix + '_pve_2' + fast_suffix +
' multiplier_noNAN.mif -mult remove_unconnected_wm_mask.mif -mult wm.mif'
)
run.command('mrcalc 0 wm.mif -min path.mif')
run.command(
'mrcat cgm.mif sgm.mif wm.mif csf.mif path.mif - -axis 3 | mrconvert - combined_precrop.mif -stride +2,+3,+4,+1'
)
# Use mrcrop to reduce file size (improves caching of image data during tracking)
if app.args.nocrop:
run.command('mrconvert combined_precrop.mif result.mif')
else:
run.command(
'mrmath combined_precrop.mif sum - -axis 3 | mrthreshold - - -abs 0.5 | mrcrop combined_precrop.mif result.mif -mask -'
)
def execute(): #pylint: disable=unused-variable
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 command(cmd, exitOnError=True):
import inspect, itertools, os, shlex, subprocess, sys, tempfile
from distutils.spawn import find_executable
from mrtrix3 import app
# This is the only global variable that is _modified_ within this function
global _processes
# Vectorise the command string, preserving anything encased within quotation marks
cmdsplit = shlex.split(cmd)
if app._lastFile:
# Check to see if the last file produced in the previous script execution is
# intended to be produced by this command; if it is, this will be the last
# command that gets skipped by the -continue option
# It's possible that the file might be defined in a '--option=XXX' style argument
# It's also possible that the filename in the command string has the file extension omitted
for entry in cmdsplit:
if entry.startswith('--') and '=' in entry:
cmdtotest = entry.split('=')[1]
else:
cmdtotest = entry
filetotest = [ app._lastFile, os.path.splitext(app._lastFile)[0] ]
if cmdtotest in filetotest:
app.debug('Detected last file \'' + app._lastFile + '\' in command \'' + cmd + '\'; this is the last run.command() / run.function() call that will be skipped')
app._lastFile = ''
break
if app._verbosity:
sys.stderr.write(app.colourExec + 'Skipping command:' + app.colourClear + ' ' + cmd + '\n')
sys.stderr.flush()
return
# This splits the command string based on the piping character '|', such that each
# individual executable (along with its arguments) appears as its own list
# Note that for Python2 support, it is necessary to convert groupby() output from
# a generator to a list before it is passed to filter()
cmdstack = [ list(g) for k, g in filter(lambda t : t[0], ((k, list(g)) for k, g in itertools.groupby(cmdsplit, lambda s : s is not '|') ) ) ]
for line in cmdstack:
is_mrtrix_exe = line[0] in _mrtrix_exe_list
if is_mrtrix_exe:
line[0] = versionMatch(line[0])
if app._nthreads is not None:
line.extend( [ '-nthreads', str(app._nthreads) ] )
# Get MRtrix3 binaries to output additional INFO-level information if running in debug mode
if app._verbosity == 3:
line.append('-info')
elif not app._verbosity:
line.append('-quiet')
else:
line[0] = exeName(line[0])
shebang = _shebang(line[0])
if len(shebang):
if not is_mrtrix_exe:
# If a shebang is found, and this call is therefore invoking an
# interpreter, can't rely on the interpreter finding the script
# from PATH; need to find the full path ourselves.
line[0] = find_executable(line[0])
for item in reversed(shebang):
line.insert(0, item)
if app._verbosity:
sys.stderr.write(app.colourExec + 'Command:' + app.colourClear + ' ' + cmd + '\n')
sys.stderr.flush()
app.debug('To execute: ' + str(cmdstack))
# Construct temporary text files for holding stdout / stderr contents when appropriate
# (One entry per process; each is a tuple containing two entries, each of which is either a
# file-like object, or None)
tempfiles = [ ]
# Execute all processes
_processes = [ ]
for index, command in enumerate(cmdstack):
file_out = None
file_err = None
# If there's at least one command prior to this, need to receive the stdout from the prior command
# at the stdin of this command; otherwise, nothing to receive
if index > 0:
handle_in = _processes[index-1].stdout
else:
handle_in = None
# If this is not the last command, then stdout needs to be piped to the next command;
# otherwise, write stdout to a temporary file so that the contents can be read later
if index < len(cmdstack)-1:
handle_out = subprocess.PIPE
else:
file_out = tempfile.TemporaryFile()
handle_out = file_out.fileno()
# If we're in debug / info mode, the contents of stderr will be read and printed to the terminal
# as the command progresses, hence this needs to go to a pipe; otherwise, write it to a temporary
# file so that the contents can be read later
if app._verbosity > 1:
handle_err = subprocess.PIPE
else:
file_err = tempfile.TemporaryFile()
handle_err = file_err.fileno()
# Set off the processes
try:
process = subprocess.Popen (command, stdin=handle_in, stdout=handle_out, stderr=handle_err, env=_env)
_processes.append(process)
tempfiles.append( ( file_out, file_err ) )
# FileNotFoundError not defined in Python 2.7
except OSError as e:
if exitOnError:
app.error('\'' + command[0] + '\' not executed ("' + str(e) + '"); script cannot proceed')
else:
app.warn('\'' + command[0] + '\' not executed ("' + str(e) + '")')
for p in _processes:
p.terminate()
_processes = [ ]
break
except (KeyboardInterrupt, SystemExit):
import inspect, signal
app._handler(signal.SIGINT, inspect.currentframe())
return_stdout = ''
return_stderr = ''
error = False
error_text = ''
# Wait for all commands to complete
try:
# Switch how we monitor running processes / wait for them to complete
# depending on whether or not the user has specified -verbose or -debug option
if app._verbosity > 1:
for process in _processes:
stderrdata = ''
while True:
# Have to read one character at a time: Waiting for a newline character using e.g. readline() will prevent MRtrix progressbars from appearing
line = process.stderr.read(1).decode('utf-8')
sys.stderr.write(line)
sys.stderr.flush()
stderrdata += line
if not line and process.poll() is not None:
break
return_stderr += stderrdata
if process.returncode:
error = True
error_text += stderrdata
else:
for process in _processes:
process.wait()
except (KeyboardInterrupt, SystemExit):
import inspect, signal
app._handler(signal.SIGINT, inspect.currentframe())
# For any command stdout / stderr data that wasn't either passed to another command or
# printed to the terminal during execution, read it here.
for index in range(len(cmdstack)):
if tempfiles[index][0] is not None:
tempfiles[index][0].flush()
tempfiles[index][0].seek(0)
stdout_text = tempfiles[index][0].read().decode('utf-8')
return_stdout += stdout_text
if _processes[index].returncode:
error = True
error_text += stdout_text
if tempfiles[index][1] is not None:
tempfiles[index][1].flush()
tempfiles[index][1].seek(0)
stderr_text = tempfiles[index][1].read().decode('utf-8')
return_stderr += stderr_text
if _processes[index].returncode:
error = True
error_text += stderr_text
_processes = [ ]
if (error):
app._cleanup = False
if exitOnError:
caller = inspect.getframeinfo(inspect.stack()[1][0])
app.console('')
sys.stderr.write(os.path.basename(sys.argv[0]) + ': ' + app.colourError + '[ERROR] Command failed: ' + cmd + app.colourClear + app.colourDebug + ' (' + os.path.basename(caller.filename) + ':' + str(caller.lineno) + ')' + app.colourClear + '\n')
sys.stderr.write(os.path.basename(sys.argv[0]) + ': ' + app.colourConsole + 'Output of failed command:' + app.colourClear + '\n')
sys.stderr.write(error_text)
sys.stderr.flush()
if app._tempDir:
with open(os.path.join(app._tempDir, 'error.txt'), 'w') as outfile:
outfile.write(cmd + '\n\n' + error_text + '\n')
app.complete()
sys.exit(1)
else:
app.warn('Command failed: ' + cmd)
# Only now do we append to the script log, since the command has completed successfully
# Note: Writing the command as it was formed as the input to run.command():
# other flags may potentially change if this file is eventually used to resume the script
if app._tempDir:
with open(os.path.join(app._tempDir, 'log.txt'), 'a') as outfile:
outfile.write(cmd + '\n')
return (return_stdout, return_stderr)
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, 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
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 command(cmd, exitOnError=True): #pylint: disable=unused-variable
import inspect, itertools, shlex, signal, string, subprocess, sys, tempfile
from distutils.spawn import find_executable
from mrtrix3 import app
# This is the only global variable that is _modified_ within this function
global _processes
# Vectorise the command string, preserving anything encased within quotation marks
if os.sep == '/': # Cheap POSIX compliance check
cmdsplit = shlex.split(cmd)
else: # Native Windows Python
cmdsplit = [
entry.strip('\"') for entry in shlex.split(cmd, posix=False)
]
if _lastFile:
if _triggerContinue(cmdsplit):
app.debug(
'Detected last file in command \'' + cmd +
'\'; this is the last run.command() / run.function() call that will be skipped'
)
if app.verbosity:
sys.stderr.write(app.colourExec + 'Skipping command:' +
app.colourClear + ' ' + cmd + '\n')
sys.stderr.flush()
return ('', '')
# This splits the command string based on the piping character '|', such that each
# individual executable (along with its arguments) appears as its own list
cmdstack = [
list(g) for k, g in itertools.groupby(cmdsplit, lambda s: s != '|')
if k
]
for line in cmdstack:
is_mrtrix_exe = line[0] in _mrtrix_exe_list
if is_mrtrix_exe:
line[0] = versionMatch(line[0])
if app.numThreads is not None:
line.extend(['-nthreads', str(app.numThreads)])
# Get MRtrix3 binaries to output additional INFO-level information if running in debug mode
if app.verbosity == 3:
line.append('-info')
elif not app.verbosity:
line.append('-quiet')
else:
line[0] = exeName(line[0])
shebang = _shebang(line[0])
if shebang:
if not is_mrtrix_exe:
# If a shebang is found, and this call is therefore invoking an
# interpreter, can't rely on the interpreter finding the script
# from PATH; need to find the full path ourselves.
line[0] = find_executable(line[0])
for item in reversed(shebang):
line.insert(0, item)
app.debug('To execute: ' + str(cmdstack))
if app.verbosity:
sys.stderr.write(app.colourExec + 'Command:' + app.colourClear + ' ' +
cmd + '\n')
sys.stderr.flush()
# Disable interrupt signal handler while threads are running
try:
signal.signal(signal.SIGINT, signal.default_int_handler)
except:
pass
# Construct temporary text files for holding stdout / stderr contents when appropriate
# (One entry per process; each is a tuple containing two entries, each of which is either a
# file-like object, or None)
tempfiles = []
# Execute all processes
assert not _processes
for index, to_execute in enumerate(cmdstack):
file_out = None
file_err = None
# If there's at least one command prior to this, need to receive the stdout from the prior command
# at the stdin of this command; otherwise, nothing to receive
if index > 0:
handle_in = _processes[index - 1].stdout
else:
handle_in = None
# If this is not the last command, then stdout needs to be piped to the next command;
# otherwise, write stdout to a temporary file so that the contents can be read later
if index < len(cmdstack) - 1:
handle_out = subprocess.PIPE
else:
file_out = tempfile.TemporaryFile()
handle_out = file_out.fileno()
# If we're in debug / info mode, the contents of stderr will be read and printed to the terminal
# as the command progresses, hence this needs to go to a pipe; otherwise, write it to a temporary
# file so that the contents can be read later
if app.verbosity > 1:
handle_err = subprocess.PIPE
else:
file_err = tempfile.TemporaryFile()
handle_err = file_err.fileno()
# Set off the processes
try:
try:
process = subprocess.Popen(to_execute,
stdin=handle_in,
stdout=handle_out,
stderr=handle_err,
env=_env,
preexec_fn=os.setpgrp) # pylint: disable=bad-option-value,subprocess-popen-preexec-fn
except AttributeError:
process = subprocess.Popen(to_execute,
stdin=handle_in,
stdout=handle_out,
stderr=handle_err,
env=_env)
_processes.append(process)
tempfiles.append((file_out, file_err))
# FileNotFoundError not defined in Python 2.7
except OSError as e:
if exitOnError:
app.error('\'' + to_execute[0] + '\' not executed ("' +
str(e) + '"); script cannot proceed')
else:
app.warn('\'' + to_execute[0] + '\' not executed ("' + str(e) +
'")')
for p in _processes:
p.terminate()
_processes = []
break
return_stdout = ''
return_stderr = ''
error = False
error_text = ''
# Wait for all commands to complete
# Switch how we monitor running processes / wait for them to complete
# depending on whether or not the user has specified -info or -debug option
try:
if app.verbosity > 1:
for process in _processes:
stderrdata = b''
do_indent = True
while True:
# Have to read one character at a time: Waiting for a newline character using e.g. readline() will prevent MRtrix progressbars from appearing
byte = process.stderr.read(1)
stderrdata += byte
char = byte.decode('cp1252', errors='ignore')
if not char and process.poll() is not None:
break
if do_indent and char in string.printable and char != '\r' and char != '\n':
sys.stderr.write(' ')
do_indent = False
elif char in ['\r', '\n']:
do_indent = True
sys.stderr.write(char)
sys.stderr.flush()
stderrdata = stderrdata.decode('utf-8', errors='replace')
return_stderr += stderrdata
if process.returncode:
error = True
error_text += stderrdata
else:
for process in _processes:
process.wait()
except (KeyboardInterrupt, SystemExit):
app.handler(signal.SIGINT, inspect.currentframe())
# Re-enable interrupt signal handler
try:
signal.signal(signal.SIGINT, app.handler)
except:
pass
# For any command stdout / stderr data that wasn't either passed to another command or
# printed to the terminal during execution, read it here.
for index in range(len(cmdstack)):
if tempfiles[index][0] is not None:
tempfiles[index][0].flush()
tempfiles[index][0].seek(0)
stdout_text = tempfiles[index][0].read().decode('utf-8',
errors='replace')
return_stdout += stdout_text
if _processes[index].returncode:
error = True
error_text += stdout_text
if tempfiles[index][1] is not None:
tempfiles[index][1].flush()
tempfiles[index][1].seek(0)
stderr_text = tempfiles[index][1].read().decode('utf-8',
errors='replace')
return_stderr += stderr_text
if _processes[index].returncode:
error = True
error_text += stderr_text
_processes = []
if error:
if exitOnError:
app.cleanup = False
caller = inspect.getframeinfo(inspect.stack()[1][0])
script_name = os.path.basename(sys.argv[0])
app.console('')
try:
filename = caller.filename
lineno = caller.lineno
except AttributeError:
filename = caller[1]
lineno = caller[2]
sys.stderr.write(script_name + ': ' + app.colourError +
'[ERROR] Command failed: ' + cmd +
app.colourClear + app.colourDebug + ' (' +
os.path.basename(filename) + ':' + str(lineno) +
')' + app.colourClear + '\n')
sys.stderr.write(script_name + ': ' + app.colourConsole +
'Output of failed command:' + app.colourClear +
'\n')
for line in error_text.splitlines():
sys.stderr.write(' ' * (len(script_name) + 2) + line + '\n')
app.console('')
sys.stderr.flush()
if app.tempDir:
with open(os.path.join(app.tempDir, 'error.txt'),
'w') as outfile:
outfile.write(cmd + '\n\n' + error_text + '\n')
app.complete()
sys.exit(1)
else:
app.warn('Command failed: ' + cmd)
# Only now do we append to the script log, since the command has completed successfully
# Note: Writing the command as it was formed as the input to run.command():
# other flags may potentially change if this file is eventually used to resume the script
if app.tempDir:
with open(os.path.join(app.tempDir, 'log.txt'), 'a') as outfile:
outfile.write(cmd + '\n')
return (return_stdout, return_stderr)
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))
'sub-' + sub_index for sub_index in app.args.participant_label
]
for subject_dir in subjects_to_analyze:
if not os.path.isdir(os.path.join(app.args.bids_dir, subject_dir)):
app.error('Unable to find directory for subject: ' +
subject_dir)
# Run all subjects sequentially
else:
subject_dirs = glob.glob(os.path.join(app.args.bids_dir, 'sub-*'))
subjects_to_analyze = [
'sub-' + directory.split("-")[-1] for directory in subject_dirs
]
if not subjects_to_analyze:
app.error('Could not find any subjects in BIDS directory')
for subject_label in subjects_to_analyze:
app.console('Commencing execution for subject ' + subject_label)
runSubject(app.args.bids_dir, subject_label,
os.path.abspath(app.args.output_dir))
# Running group level
elif app.args.analysis_level == 'group':
if app.args.participant_label:
app.error(
'Cannot use --participant_label option when performing group analysis'
)
runGroup(os.path.abspath(app.args.output_dir))
app.complete()
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 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 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():
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')
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)