def process_subject_anatomy(t1):
reconall = ReconAll()
reconall.inputs.subject_id = t1.split('/')[-3]
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = subjects_dir
reconall.inputs.T1_files = t1
reconall.run()
def process_subject_anatomy(subject, t1, subjects_dir='/cluster/transcend/MRI/WMA/recons'):
reconall = ReconAll()
reconall.inputs.subject_id = subject
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = subjects_dir
reconall.inputs.T1_files = t1
reconall.run()
def run_recon_all(subject=None, subjects_dir=subjects_dir, openmp=1):
reconall = ReconAll()
if not os.path.isdir(subjects_dir):
print(
f"Subjects directory {subjects_dir} does not exist, creating it.")
os.mkdir(subjects_dir)
if not os.path.isdir(os.path.join(subjects_dir, subject, "mri")):
anafolder = os.path.join(args.folder, subject)
# catch error, if no .nii exists
try:
nii_file = glob.glob(anafolder + "/*.nii*")[0]
reconall.inputs.subject_id = subject
reconall.inputs.T1_files = nii_file
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = subjects_dir
reconall.inputs.openmp = openmp
reconall.inputs.flags = "-3T"
print(f"Now running recon-all for subject {subject}")
print(f".nii-file used was: {nii_file}")
reconall.run()
except Exception as e:
print(e)
else:
print(
f"A freesurfer segmentation of subject {subject} already exists in {subjects_dir} - aborting"
)
def _nii_to_freesurfer(self):
reconall = ReconAll()
if not self.subject.startswith("sub-"):
self.subject = "sub-" + self.subject
# check if freesurfer segmentation was already performed
freesurfered = os.path.join(self.FS_SUBJECTS_DIR, self.subject)
if not os.path.isdir(freesurfered):
nii_file = glob.glob(opj(self.mri_folder, "*.nii*"))[0]
reconall.inputs.subject_id = self.subject
reconall.inputs.T1_files = nii_file
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = self.FS_SUBJECTS_DIR
reconall.inputs.openmp = self.n_jobs
reconall.inputs.flags = "-3T"
reconall.run()
else:
print(
f"A freesurfer segmentation of subject {self.subject} already exists in {self.FS_SUBJECTS_DIR}"
)
def make_w_masking():
w_mask = Workflow('masking')
n_in = Node(
IdentityInterface(fields=[
'T1w',
'subject', # without sub-
'freesurfer2func',
'func',
]),
name='input')
n_out = Node(IdentityInterface(fields=[
'func',
]), name='output')
n_fl = Node(FLIRT(), name='flirt')
n_fl.inputs.output_type = 'NIFTI_GZ'
n_fl.inputs.apply_xfm = True
n_fl.inputs.interp = 'nearestneighbour'
n_conv = Node(MRIConvert(), name='convert')
n_conv.inputs.out_type = 'niigz'
reconall = Node(ReconAll(), name='reconall')
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = '/Fridge/R01_BAIR/freesurfer'
w_mask.connect(n_in, 'T1w', reconall, 'T1_files')
w_mask.connect(n_in, 'subject', reconall, 'subject_id')
n_mul = Node(interface=BinaryMaths(), name='mul')
n_mul.inputs.operation = 'mul'
w_mask.connect(reconall, ('ribbon', select_ribbon), n_conv, 'in_file')
w_mask.connect(n_conv, 'out_file', n_fl, 'in_file')
w_mask.connect(n_in, 'func', n_fl, 'reference')
w_mask.connect(n_in, 'freesurfer2func', n_fl, 'in_matrix_file')
w_mask.connect(n_in, 'func', n_mul, 'in_file')
w_mask.connect(n_fl, 'out_file', n_mul, 'operand_file')
w_mask.connect(n_mul, 'out_file', n_out, 'func')
return w_mask
def FreeSurfer_Reconall(subject_list, base_directory, out_directory):
#==============================================================
# Loading required packages
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ReconAll
from nipype import SelectFiles
import os
nodes = list()
#====================================
# Defining the nodes for the workflow
# Getting the subject ID
infosource = pe.Node(
interface=util.IdentityInterface(fields=['subject_id']),
name='infosource')
infosource.iterables = ('subject_id', subject_list)
# Getting the relevant diffusion-weighted data
templates = dict(T1='{subject_id}/anat/{subject_id}_T1w.nii.gz')
selectfiles = pe.Node(SelectFiles(templates), name="selectfiles")
selectfiles.inputs.base_directory = os.path.abspath(base_directory)
nodes.append(selectfiles)
reconall = pe.Node(interface=ReconAll(), name='reconall')
reconall.inputs.directive = 'autorecon2'
reconall.inputs.subjects_dir = out_directory
reconall.inputs.flags = '-no-isrunning'
reconall.inputs.ignore_exception = True
# Setting up the workflow
fs_reconall = pe.Workflow(name='fs_reconall')
# Reading in files
fs_reconall.connect(infosource, 'subject_id', selectfiles, 'subject_id')
fs_reconall.connect(selectfiles, 'T1', reconall, 'T1_files')
fs_reconall.connect(infosource, 'subject_id', reconall, 'subject_id')
# Running the workflow
fs_reconall.base_dir = os.path.abspath(out_directory)
fs_reconall.write_graph()
fs_reconall.run('PBSGraph')
class ReconAllRunner(AnatomicalPreprocessing):
"""
Automate ReconAll execution over a provided queryset of
:class:`~django_mri.models.scan.Scan` instances.
"""
#: :class:`~django_analyses.models.analysis.Analysis` instance title.
ANALYSIS_TITLE = "ReconAll"
#: :class:`~django_analyses.models.analysis_version.AnalysisVersion`
#: instance title.
ANALYSIS_VERSION_TITLE = ReconAll().version
#: :class:`~django_analyses.models.pipeline.node.Node` instance
#: configuration.
ANALYSIS_CONFIGURATION = {}
#: Input definition key.
INPUT_KEY = "T1_files"
def get_instance_representation(self, instance: Scan) -> List[str]:
"""
ReconAll expects a list of T1-weighted scans.
Parameters
----------
instance : Scan
Scan to create the input representation for
Returns
-------
List[str]
A list containing the provided scan's path
"""
nii_path = super().get_instance_representation(instance)
return [nii_path]
experiment_dir = '/home/luiscp/Documents/Data/ADRC_90Plus/output'
output_dir = 'seg_analysis'
subject_list = ['233','234','235','236','237','238','239','240','242','243','244','245','246','248','249','250','251','253','254','256','257','259']
#subject_list = ['259']
###############################
#specify nodes
###############################
smooth = Node(SUSAN(fwhm = 8.0,
output_type =u'NIFTI_GZ',
brightness_threshold=20),
name="smooth")
#freesurfer recon-all segmentation
recon_all = Node(ReconAll(subject_id ='subject_id',
directive = u'all'),
name="recon_all")
mr_convertT1=Node(MRIConvert(out_type=u'niigz'),
name="mr_convertT1")
mr_convertaseg=Node(MRIConvert(out_type=u'niigz'),
name="mr_convertaseg")
mr_convertaparc_aseg=MapNode(MRIConvert(out_type=u'niigz'),
name="mr_convertaparc_aseg",iterfield='in_file')
mr_convertbrainmask=Node(MRIConvert(out_type=u'niigz'),
name="mr_convertbrainmask")
mr_convertbrain=Node(MRIConvert(out_type=u'niigz'),
name="mr_convertbrain")
mr_convertwmparc=Node(MRIConvert(out_type=u'niigz'),
name="mr_convertwmparc")
mr_convertwm=Node(MRIConvert(out_type=u'niigz'),
from nipype.interfaces.freesurfer import ReconAll
import os
import shutil
import sys
##################################################
FREESURFER_HOME = os.environ.get('FREESURFER_HOME')
reconall = ReconAll()
# path to the subject T1 MRI image in .nii format
# this file must be in .nii format
nii_file_path = 'nii_file_path'
# Subject ID: this should be a string which defines the folder name
# that will be created automatically for the subject in /usr/local/freesurfer/subjects/
subject_id = 'subject_id'
print('Data preprocessing started for {subject} in {path}'.format(
subject=subject_id, path=nii_file_path))
reconall.inputs.subject_id = subject_id
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = FREESURFER_HOME + '/subjects'
reconall.inputs.T1_files = nii_file_path
reconall.run()
T1_identifier = 'struct.nii.gz' # Name of T1-weighted image
# Create the output folder - FreeSurfer can only run if this folder exists
os.system('mkdir -p %s' % fs_folder)
# Create the pipeline that runs the recon-all command
reconflow = Workflow(name="reconflow")
reconflow.base_dir = opj(experiment_dir, 'workingdir_reconflow')
# Some magical stuff happens here (not important for now)
infosource = Node(IdentityInterface(fields=['subject_id']), name="infosource")
infosource.iterables = ('subject_id', subject_list)
# This node represents the actual recon-all command
econall = Node(
ReconAll(
directive='all',
#flags='-nuintensitycor- 3T',
subjects_dir=fs_folder),
name="reconall")
# This function returns for each subject the path to struct.nii.gz
def pathfinder(subject, foldername, filename):
from os.path import join as opj
struct_path = opj(foldername, subject, filename)
return struct_path
# This section connects all the nodes of the pipeline to each other
reconflow.connect([
(infosource, reconall, [('subject_id', 'subject_id')]),
(infosource, reconall, [(('subject_id', pathfinder, data_dir,
def main():
"""
Create the help message
"""
pipeline_description = textwrap.dedent('''
Pipeline to run the complte freesurfer pipeline on structural images
''')
"""
Create the parser
"""
parser = ArgumentParser(formatter_class=RawDescriptionHelpFormatter,
description=pipeline_description)
# Input images
parser.add_argument('--input_t1w',
dest='input_t1w',
metavar='FILE',
help='List of T1w Nifti file(s) to process',
nargs='+',
required=True)
parser.add_argument('--input_t2w',
dest='input_t2w',
metavar='FILE',
help='Optional list of T2w Nifti file(s) to process',
nargs='+')
parser.add_argument('--input_sid',
dest='input_sid',
metavar='FILE',
help='Optional list subject ID',
nargs='+')
# Output directory
parser.add_argument('-o',
'--output_dir',
dest='output_dir',
metavar='DIR',
help='Output directory where to save the results',
default=os.getcwd())
"""
Add default arguments in the parser
"""
default_parser_argument(parser)
"""
Parse the input arguments
"""
args = parser.parse_args()
"""
Create the output folder if it does not exists
"""
result_dir = os.path.abspath(args.output_dir)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
"""
Check the length of the input lists
"""
if args.input_t2w is not None:
if len(args.input_t1w) is not len(args.input_t2w):
raise Exception('The numbers of T1w and T2w files differ')
if args.input_sid is not None:
if len(args.input_t1w) is not len(args.input_sid):
raise Exception('The numbers of T1w files and subject ID differ')
"""
Create the workflow that generates a cross sectional groupwise and extract diffusion features subject-wise
"""
workflow = pe.Workflow(name='freesurfer')
workflow.base_output_dir = 'freesurfer'
input_node = pe.Node(interface=niu.IdentityInterface(
fields=['T1_files', 'T2_files', 'subject_id']),
name='input_node')
input_node.inputs.T1_files = [os.path.abspath(f) for f in args.input_t1w]
if args.input_t2w is not None:
input_node.inputs.T2_files = [
os.path.abspath(f) for f in args.input_t2w
]
if args.input_sid is not None:
input_node.inputs.subject_id = args.input_sid
recon = None
if args.input_t2w is not None and args.input_sid is not None:
recon = pe.MapNode(interface=ReconAll(),
iterfield=['T1_files', 'T2_file', 'subject_id'],
name='recon')
workflow.connect(input_node, 'T2_files', recon, 'T2_file')
workflow.connect(input_node, 'subject_id', recon, 'subject_id')
recon.inputs.use_T2 = True
elif args.input_t2w is not None:
recon = pe.MapNode(interface=ReconAll(),
iterfield=['T1_files', 'T2_file'],
name='recon')
workflow.connect(input_node, 'T2_files', recon, 'T2_file')
recon.inputs.use_T2 = True
elif args.input_sid is not None:
recon = pe.MapNode(interface=ReconAll(),
iterfield=['T1_files', 'subject_id'],
name='recon')
workflow.connect(input_node, 'subject_id', recon, 'subject_id')
workflow.connect(input_node, 'T1_files', recon, 'T1_files')
recon.inputs.subjects_dir = result_dir
recon.inputs.openmp = args.openmp_core
"""
output the graph if required
"""
if args.graph is True:
generate_graph(workflow=workflow)
sys.exit(0)
"""
Edit the qsub arguments based on the input arguments
"""
qsubargs_time = '48:00:00'
qsubargs_mem = '5.9G'
if args.use_qsub is True and args.openmp_core > 1:
qsubargs_mem = str(max(0.95, 5.9 / args.openmp_core)) + 'G'
qsubargs = '-l s_stack=10240 -j y -b y -S /bin/csh -V'
qsubargs = qsubargs + ' -l h_rt=' + qsubargs_time
qsubargs = qsubargs + ' -l tmem=' + qsubargs_mem + ' -l h_vmem=' + qsubargs_mem + ' -l vf=' + qsubargs_mem
"""
Run the workflow
"""
run_workflow(workflow=workflow, qsubargs=qsubargs, parser=args)
def _run_interface(self, runtime):
from additional_interfaces import DipyDenoiseT1
from additional_interfaces import FSRename
from nipype.interfaces.ants import N4BiasFieldCorrection
from nipype.interfaces.ants.segmentation import BrainExtraction
from nipype.interfaces.freesurfer import MRIConvert
from nipype.interfaces.freesurfer import ReconAll
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import os
subject_id = self.inputs.subject_id
T1 = self.inputs.T1
template_directory = self.inputs.template_directory
out_directory = self.inputs.out_directory
subjects_dir = out_directory + '/FreeSurfer/'
if not os.path.isdir(subjects_dir):
os.mkdir(subjects_dir)
os.environ['SUBJECTS_DIR'] = subjects_dir
# Getting a better field of view
robustfov = pe.Node(interface=fsl.RobustFOV(), name='robustfov')
robustfov.inputs.in_file = T1
# Denoising
T1_denoise = pe.Node(interface=DipyDenoiseT1(), name='T1_denoise')
# Brain extraction
brainextraction = pe.Node(interface=fsl.BET(), name='brainextraction')
# Renaming files for FreeSurfer
rename = pe.Node(FSRename(), name='rename')
# Running FreeSurfer
autorecon1 = pe.Node(interface=ReconAll(), name='autorecon1')
autorecon1.inputs.subject_id = subject_id
autorecon1.inputs.directive = 'autorecon1'
autorecon1.inputs.args = '-noskullstrip'
autorecon1.inputs.subjects_dir = subjects_dir
autorecon2 = pe.Node(interface=ReconAll(), name='autorecon2')
autorecon2.inputs.directive = 'autorecon2'
autorecon3 = pe.Node(interface=ReconAll(), name='autorecon3')
autorecon3.inputs.directive = 'autorecon3'
wm_convert = pe.Node(interface=MRIConvert(), name='wm_convert')
wm_convert.inputs.out_file = subjects_dir + '/' + subject_id + '/mri/' + 'wm.nii'
wm_convert.inputs.out_type = 'nii'
T1_convert = pe.Node(interface=MRIConvert(), name='T1_convert')
T1_convert.inputs.out_file = subjects_dir + '/' + subject_id + '/mri/' + 'T1.nii.gz'
T1_convert.inputs.out_type = 'niigz'
mask_convert = pe.Node(interface=MRIConvert(), name='mask_convert')
mask_convert.inputs.out_file = subjects_dir + '/' + subject_id + '/mri/' + 'brainmask.nii.gz'
mask_convert.inputs.out_type = 'niigz'
# Connecting the pipeline
T1_preproc = pe.Workflow(name='t1_preproc')
T1_preproc.connect(robustfov, 'out_roi', T1_denoise, 'in_file')
T1_preproc.connect(T1_denoise, 'out_file', brainextraction, 'in_file')
T1_preproc.connect(
brainextraction, 'out_file', autorecon1, 'T1_files')
T1_preproc.connect(
autorecon1, 'subject_id', autorecon2, 'subject_id')
T1_preproc.connect(
autorecon1, 'subjects_dir', autorecon2, 'subjects_dir')
T1_preproc.connect(
autorecon1, 'subject_id', rename, 'subject_id')
T1_preproc.connect(
autorecon1, 'subjects_dir', rename, 'subjects_dir')
T1_preproc.connect(
autorecon2, 'subject_id', autorecon3, 'subject_id')
T1_preproc.connect(
autorecon2, 'subjects_dir', autorecon3, 'subjects_dir')
T1_preproc.connect(autorecon3, 'wm', wm_convert, 'in_file')
T1_preproc.connect(autorecon3, 'T1', T1_convert, 'in_file')
T1_preproc.connect(
autorecon3, 'brainmask', mask_convert, 'in_file')
# ==============================================================
# Running the workflow
T1_preproc.base_dir = os.path.abspath(self.inputs.out_directory + '/_subject_id_' + self.inputs.subject_id)
T1_preproc.write_graph()
T1_preproc.run()
return runtime
#: within the *ANALYSIS_INTERFACES* setting.
interfaces = {
"apply_topup": {ApplyTOPUP().version: ApplyTOPUP},
"binary_maths": {BinaryMaths().version: BinaryMaths},
"BET": {BET().version: BET},
"CAT12 Segmentation": {"12.7": Cat12Segmentation},
"fslmerge": {Merge().version: Merge},
"fslreorient2std": {Reorient2Std().version: Reorient2Std},
"fslroi": {ExtractROI().version: ExtractROI},
"FAST": {FastWrapper.version: FastWrapper},
"FLIRT": {FLIRT().version: FLIRT},
"FNIRT": {FNIRT().version: FNIRT},
"FSL Anatomical Processing Script": {FslAnat.__version__: FslAnat},
"mean_image": {MeanImage().version: MeanImage},
"robustfov": {RobustFOV().version: RobustFOV},
"ReconAll": {ReconAll().version: ReconAll},
"SUSAN": {SUSAN().version: SUSAN},
"topup": {TopupWrapper.version: TopupWrapper},
"eddy": {Eddy().version: Eddy},
"denoise": {DWIDenoise().version: DWIDenoise},
"degibbs": {MRDeGibbs().version: MRDeGibbs},
"bias_correct": {DWIBiasCorrect().version: DWIBiasCorrect},
"dwifslpreproc": {DwiFslPreproc.__version__: DwiFslPreproc},
"mrconvert": {MRConvert.__version__: MRConvert},
"dwi2fod": {
ConstrainedSphericalDeconvolution().version: ConstrainedSphericalDeconvolution # noqa: E501
},
"dwi2response": {ResponseSD().version: ResponseSD},
"5ttgen": {Generate5tt().version: Generate5tt},
"dwi2tensor": {Dwi2Tensor.__version__: Dwi2Tensor},
"tensor2metric": {Tensor2metric.__version__: Tensor2metric},
def WorkupT1T2(processingLevel,
mountPrefix,
ScanDir,
subject_data_file,
atlas_fname_wpath,
BCD_model_path,
Version=110,
InterpolationMode="Linear",
Mode=10,
DwiList=[]):
processingLevel = int(processingLevel)
"""
Run autoworkup on a single subjects data.
This is the main function to call when processing a single subject worth of
data. ScanDir is the base of the directory to place results, T1Images & T2Images
are the lists of images to be used in the auto-workup. atlas_fname_wpath is
the path and filename of the atlas to use.
"""
subjData = csv.reader(open(subject_data_file, 'rb'),
delimiter=',',
quotechar='"')
myDB = dict()
multiLevel = AutoVivification()
for row in subjData:
currDict = dict()
validEntry = True
if len(row) == 5:
site = row[0]
subj = row[1]
session = row[2]
T1s = eval(row[3])
T2s = eval(row[4])
fullT1s = [mountPrefix + i for i in T1s]
fullT2s = [mountPrefix + i for i in T2s]
currDict['T1s'] = fullT1s
currDict['T2s'] = fullT2s
currDict['site'] = site
currDict['subj'] = subj
if len(fullT1s) < 1:
print("Invalid Entry! {0}".format(currDict))
validEntry = False
if len(fullT2s) < 1:
print("Invalid Entry! {0}".format(currDict))
validEntry = False
for i in fullT1s:
if not os.path.exists(i):
print("Missing File: {0}".format(i))
validEntry = False
for i in fullT2s:
if not os.path.exists(i):
print("Missing File: {0}".format(i))
validEntry = False
if validEntry == True:
myDB[session] = currDict
UNIQUE_ID = site + "_" + subj + "_" + session
multiLevel[UNIQUE_ID] = currDict
from cPickle import dump
dump(multiLevel, open('db.tmp', 'w'))
########### PIPELINE INITIALIZATION #############
baw200 = pe.Workflow(name="BAW_20120104_workflow")
baw200.config['execution'] = {
'plugin': 'Linear',
#'stop_on_first_crash':'True',
'stop_on_first_crash': 'False',
'stop_on_first_rerun': 'False',
'hash_method': 'timestamp',
'single_thread_matlab': 'True',
'remove_unnecessary_outputs': 'False',
'use_relative_paths': 'False',
'remove_node_directories': 'False',
'local_hash_check': 'True'
}
baw200.config['logging'] = {
'workflow_level': 'DEBUG',
'filemanip_level': 'DEBUG',
'interface_level': 'DEBUG',
'log_directory': ScanDir
}
baw200.base_dir = ScanDir
########################################################
# Run ACPC Detect on first T1 Image - Base Image
########################################################
"""TODO: Determine if we want to pass subjectID and scanID, always require full
paths, get them from the output path, or something else.
"""
siteSource = pe.Node(interface=IdentityInterface(fields=['uid']),
name='99_siteSource')
siteSource.iterables = ('uid', multiLevel.keys())
def getFirstT1(uid, dbfile):
from cPickle import load
with open(dbfile) as fp:
db = load(fp)
print("uid:= {0}, dbfile: {1}".format(uid, dbfile))
print("result:= {0}".format(db[uid]["T1s"]))
return db[uid]["T1s"][0]
def sessionImages(dbfile, uid):
from cPickle import load
with open(dbfile) as fp:
db = load(fp)
return db[uid]["T1s"], db[uid]["T2s"]
T1andT2ImageListNode = pe.Node(interface=Function(
function=sessionImages,
input_names=['dbfile', 'uid'],
output_names=['T1List', 'T2List']),
run_without_submitting=True,
name="99_nestedImageList")
T1andT2ImageListNode.inputs.dbfile = os.path.join(os.getcwd(),
'db.tmp') #multiLevel
baw200.connect(siteSource, 'uid', T1andT2ImageListNode, 'uid')
########################################################
# Run ACPC Detect on First T1 Image
########################################################
BCD = pe.Node(interface=BRAINSConstellationDetector(), name="01_BCD")
## Use program default BCD.inputs.inputTemplateModel = T1ACPCModelFile
##BCD.inputs.outputVolume = "BCD_OUT" + "_ACPC_InPlace.nii.gz" #$# T1AcpcImageList
BCD.inputs.outputResampledVolume = "BCD_OUT" + "_ACPC.nii.gz"
BCD.inputs.outputTransform = "BCD_OUT" + "_ACPC_transform.mat"
BCD.inputs.outputLandmarksInInputSpace = "BCD_OUT" + "_ACPC_Original.fcsv"
BCD.inputs.outputLandmarksInACPCAlignedSpace = "BCD_OUT" + "_ACPC_Landmarks.fcsv"
BCD.inputs.outputMRML = "BCD_OUT" + "_ACPC_Scene.mrml"
BCD.inputs.outputLandmarkWeights = "BCD_OUT" + "_ACPC_Landmarks.wgts"
BCD.inputs.interpolationMode = InterpolationMode
BCD.inputs.houghEyeDetectorMode = 1
BCD.inputs.acLowerBound = 80
BCD.inputs.llsModel = os.path.join(BCD_model_path, 'LLSModel.hdf5')
BCD.inputs.inputTemplateModel = os.path.join(BCD_model_path, 'T1.mdl')
# Entries below are of the form:
baw200.connect([
(siteSource, BCD, [(('uid', getFirstT1,
os.path.join(os.getcwd(),
'db.tmp')), 'inputVolume')]),
])
########################################################
# Run BROIA to make T1 image as small as possible
########################################################
BROIA = pe.Node(interface=BRAINSROIAuto(), name="03_BROIA")
BROIA.inputs.ROIAutoDilateSize = 10
BROIA.inputs.outputVolumePixelType = "short"
BROIA.inputs.maskOutput = True
BROIA.inputs.cropOutput = True
BROIA.inputs.outputVolume = "BROIA_OUT" + "_ACPC_InPlace_cropped.nii.gz"
BROIA.inputs.outputROIMaskVolume = "BROIA_OUT" + "_ACPC_InPlace_foreground_seg.nii.gz"
baw200.connect([(BCD, BROIA, [('outputResampledVolume', 'inputVolume')])])
if processingLevel > 1:
########################################################
# Run BABC on Multi-modal images
########################################################
BLI = pe.Node(interface=BRAINSLandmarkInitializer(), name="05_BLI")
BLI.inputs.outputTransformFilename = "landmarkInitializer_atlas_to_subject_transform.mat"
BAtlas = MakeAtlasNode(
atlas_fname_wpath) ## Call function to create node
baw200.connect([
(BCD, BLI, [('outputLandmarksInACPCAlignedSpace',
'inputFixedLandmarkFilename')]),
])
baw200.connect([(BAtlas, BLI, [('template_landmarks_fcsv',
'inputMovingLandmarkFilename')]),
(BAtlas, BLI, [('template_landmark_weights_csv',
'inputWeightFilename')])])
def MakeOneFileList(T1List, T2List, altT1):
full_list = T1List
full_list.extend(T2List)
full_list[0] = altT1 # The ACPC ROIcropped T1 replacement image.
return full_list
makeImagePathList = pe.Node(Function(
function=MakeOneFileList,
input_names=['T1List', 'T2List', 'altT1'],
output_names=['imagePathList']),
run_without_submitting=True,
name="99_makeImagePathList")
baw200.connect(T1andT2ImageListNode, 'T1List', makeImagePathList,
'T1List')
baw200.connect(T1andT2ImageListNode, 'T2List', makeImagePathList,
'T2List')
# -- Alternate mode
baw200.connect(BROIA, 'outputVolume', makeImagePathList, 'altT1')
# -- Standard mode to make 256^3 images
#baw200.connect( BCD, 'outputResampledVolume', makeImagePathList, 'altT1' )
def MakeOneFileTypeList(T1List, T2List):
input_types = ["T1"] * len(T1List)
input_types.extend(["T2"] * len(T2List))
return ",".join(input_types)
makeImageTypeList = pe.Node(Function(function=MakeOneFileTypeList,
input_names=['T1List', 'T2List'],
output_names=['imageTypeList']),
run_without_submitting=True,
name="99_makeImageTypeList")
baw200.connect(T1andT2ImageListNode, 'T1List', makeImageTypeList,
'T1List')
baw200.connect(T1andT2ImageListNode, 'T2List', makeImageTypeList,
'T2List')
def MakeOutFileList(T1List, T2List):
def GetExtBaseName(filename):
'''
Get the filename without the extension. Works for .ext and .ext.gz
'''
import os
currBaseName = os.path.basename(filename)
currExt = os.path.splitext(currBaseName)[1]
currBaseName = os.path.splitext(currBaseName)[0]
if currExt == ".gz":
currBaseName = os.path.splitext(currBaseName)[0]
currExt = os.path.splitext(currBaseName)[1]
return currBaseName
all_files = T1List
all_files.extend(T2List)
out_corrected_names = []
for i in all_files:
out_name = GetExtBaseName(i) + "_corrected.nii.gz"
out_corrected_names.append(out_name)
return out_corrected_names
makeOutImageList = pe.Node(Function(function=MakeOutFileList,
input_names=['T1List', 'T2List'],
output_names=['outImageList']),
run_without_submitting=True,
name="99_makeOutImageList")
baw200.connect(T1andT2ImageListNode, 'T1List', makeOutImageList,
'T1List')
baw200.connect(T1andT2ImageListNode, 'T2List', makeOutImageList,
'T2List')
BABC = pe.Node(interface=BRAINSABC(), name="11_BABC")
baw200.connect(makeImagePathList, 'imagePathList', BABC,
'inputVolumes')
baw200.connect(makeImageTypeList, 'imageTypeList', BABC,
'inputVolumeTypes')
baw200.connect(makeOutImageList, 'outImageList', BABC, 'outputVolumes')
BABC.inputs.debuglevel = 0
BABC.inputs.maxIterations = 3
BABC.inputs.maxBiasDegree = 4
BABC.inputs.filterIteration = 3
BABC.inputs.filterMethod = 'GradientAnisotropicDiffusion'
BABC.inputs.gridSize = [28, 20, 24]
BABC.inputs.outputFormat = "NIFTI"
BABC.inputs.outputLabels = "brain_label_seg.nii.gz"
BABC.inputs.outputDirtyLabels = "volume_label_seg.nii.gz"
BABC.inputs.posteriorTemplate = "POSTERIOR_%s.nii.gz"
BABC.inputs.atlasToSubjectTransform = "atlas_to_subject.mat"
BABC.inputs.implicitOutputs = [
't1_average_BRAINSABC.nii.gz', 't2_average_BRAINSABC.nii.gz'
]
BABC.inputs.resamplerInterpolatorType = InterpolationMode
##
BABC.inputs.outputDir = './'
baw200.connect(BAtlas, 'AtlasPVDefinition_xml', BABC,
'atlasDefinition')
baw200.connect(BLI, 'outputTransformFilename', BABC,
'atlasToSubjectInitialTransform')
"""
Get the first T1 and T2 corrected images from BABC
"""
bfc_files = pe.Node(Function(
input_names=['in_files', 'T1_count'],
output_names=['t1_corrected', 't2_corrected'],
function=get_first_T1_and_T2),
name='99_bfc_files')
bfc_files.inputs.T1_count = len(T1andT2ImageListNode.outputs.T1List)
baw200.connect(BABC, 'outputVolumes', bfc_files, 'in_files')
"""
ResampleNACLabels
"""
ResampleAtlasNACLabels = pe.Node(interface=BRAINSResample(),
name="13_ResampleAtlasNACLabels")
ResampleAtlasNACLabels.inputs.interpolationMode = "NearestNeighbor"
ResampleAtlasNACLabels.inputs.outputVolume = "atlasToSubjectNACLabels.nii.gz"
baw200.connect(BABC, 'atlasToSubjectTransform', ResampleAtlasNACLabels,
'warpTransform')
baw200.connect(bfc_files, 't1_corrected', ResampleAtlasNACLabels,
'referenceVolume')
baw200.connect(BAtlas, 'template_nac_lables', ResampleAtlasNACLabels,
'inputVolume')
"""
BRAINSMush
"""
BMUSH = pe.Node(interface=BRAINSMush(), name="15_BMUSH")
BMUSH.inputs.outputVolume = "MushImage.nii.gz"
BMUSH.inputs.outputMask = "MushMask.nii.gz"
BMUSH.inputs.lowerThresholdFactor = 1.2
BMUSH.inputs.upperThresholdFactor = 0.55
baw200.connect(bfc_files, 't1_corrected', BMUSH, 'inputFirstVolume')
baw200.connect(bfc_files, 't2_corrected', BMUSH, 'inputSecondVolume')
baw200.connect(BABC, 'outputLabels', BMUSH, 'inputMaskVolume')
"""
BRAINSROIAuto
"""
BROI = pe.Node(interface=BRAINSROIAuto(), name="17_BRAINSROIAuto")
BROI.inputs.closingSize = 12
BROI.inputs.otsuPercentileThreshold = 0.01
BROI.inputs.thresholdCorrectionFactor = 1.0
BROI.inputs.outputROIMaskVolume = "temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz"
baw200.connect(bfc_files, 't1_corrected', BROI, 'inputVolume')
"""
Split the implicit outputs of BABC
"""
SplitAvgBABC = pe.Node(Function(
input_names=['in_files', 'T1_count'],
output_names=['avgBABCT1', 'avgBABCT2'],
function=get_first_T1_and_T2),
run_without_submitting=True,
name="99_SplitAvgBABC")
SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.
baw200.connect(BABC, 'implicitOutputs', SplitAvgBABC, 'in_files')
"""
Gradient Anistropic Diffusion images for BRAINSCut
"""
GADT1 = pe.Node(interface=GradientAnisotropicDiffusionImageFilter(),
name="27_GADT1")
GADT1.inputs.timeStep = 0.025
GADT1.inputs.conductance = 1
GADT1.inputs.numberOfIterations = 5
GADT1.inputs.outputVolume = "GADT1.nii.gz"
baw200.connect(SplitAvgBABC, 'avgBABCT1', GADT1, 'inputVolume')
GADT2 = pe.Node(interface=GradientAnisotropicDiffusionImageFilter(),
name="27_GADT2")
GADT2.inputs.timeStep = 0.025
GADT2.inputs.conductance = 1
GADT2.inputs.numberOfIterations = 5
GADT2.inputs.outputVolume = "GADT2.nii.gz"
def printFullPath(outFileFullPath):
print("=" * 80)
print("=" * 80)
print("=" * 80)
print("=" * 80)
print("{0}".format(outFileFullPath))
return outFileFullPath
printOutImage = pe.Node(Function(function=printFullPath,
input_names=['outFileFullPath'],
output_names=['genoutFileFullPath']),
run_without_submitting=True,
name="99_printOutImage")
baw200.connect(GADT2, 'outputVolume', printOutImage, 'outFileFullPath')
baw200.connect(SplitAvgBABC, 'avgBABCT2', GADT2, 'inputVolume')
"""
Sum the gradient images for BRAINSCut
"""
SGI = pe.Node(interface=GenerateSummedGradientImage(), name="27_SGI")
SGI.inputs.outputFileName = "SummedGradImage.nii.gz"
baw200.connect(GADT1, 'outputVolume', SGI, 'inputVolume1')
baw200.connect(GADT2, 'outputVolume', SGI, 'inputVolume2')
if processingLevel > 1:
"""
Load the BRAINSCut models & probabiity maps.
"""
BCM_outputs = [
'phi', 'rho', 'theta', 'r_probabilityMaps',
'l_probabilityMaps', 'models'
]
BCM_Models = pe.Node(interface=nio.DataGrabber(
input_names=['structures'], outfields=BCM_outputs),
name='10_BCM_Models')
BCM_Models.inputs.base_directory = atlas_fname_wpath
BCM_Models.inputs.template_args['phi'] = [[
'spatialImages', 'phi', 'nii.gz'
]]
BCM_Models.inputs.template_args['rho'] = [[
'spatialImages', 'rho', 'nii.gz'
]]
BCM_Models.inputs.template_args['theta'] = [[
'spatialImages', 'theta', 'nii.gz'
]]
BCM_Models.inputs.template_args['r_probabilityMaps'] = [[
'structures'
]]
BCM_Models.inputs.template_args['l_probabilityMaps'] = [[
'structures'
]]
BCM_Models.inputs.template_args['models'] = [['structures']]
BRAINSCut_structures = [
'caudate', 'thalamus', 'putamen', 'hippocampus'
]
#BRAINSCut_structures = ['caudate','thalamus']
BCM_Models.iterables = ('structures', BRAINSCut_structures)
BCM_Models.inputs.template = '%s/%s.%s'
BCM_Models.inputs.field_template = dict(
r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',
l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',
models='modelFiles/%sModel*',
)
"""
The xml creation and BRAINSCut need to be their own mini-pipeline that gets
executed once for each of the structures in BRAINSCut_structures. This can be
accomplished with a map node and a new pipeline.
"""
"""
Create xml file for BRAINSCut
"""
BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),
name="30_BFitAtlasToSubject")
BFitAtlasToSubject.inputs.costMetric = "MMI"
BFitAtlasToSubject.inputs.maskProcessingMode = "ROI"
BFitAtlasToSubject.inputs.numberOfSamples = 100000
BFitAtlasToSubject.inputs.numberOfIterations = [1500, 1500]
BFitAtlasToSubject.inputs.numberOfHistogramBins = 50
BFitAtlasToSubject.inputs.maximumStepLength = 0.2
BFitAtlasToSubject.inputs.minimumStepLength = [0.005, 0.005]
BFitAtlasToSubject.inputs.transformType = ["Affine", "BSpline"]
BFitAtlasToSubject.inputs.maxBSplineDisplacement = 7
BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter = 65
BFitAtlasToSubject.inputs.splineGridSize = [28, 20, 24]
BFitAtlasToSubject.inputs.outputVolume = "Trial_Initializer_Output.nii.gz"
BFitAtlasToSubject.inputs.outputTransform = "Trial_Initializer_Output.mat"
baw200.connect(SplitAvgBABC, 'avgBABCT1', BFitAtlasToSubject,
'fixedVolume')
baw200.connect(BABC, 'outputLabels', BFitAtlasToSubject,
'fixedBinaryVolume')
baw200.connect(BAtlas, 'template_t1', BFitAtlasToSubject,
'movingVolume')
baw200.connect(BAtlas, 'template_brain', BFitAtlasToSubject,
'movingBinaryVolume')
baw200.connect(BLI, 'outputTransformFilename', BFitAtlasToSubject,
'initialTransform')
CreateBRAINSCutXML = pe.Node(Function(
input_names=[
'rho', 'phi', 'theta', 'model', 'r_probabilityMap',
'l_probabilityMap', 'atlasT1', 'atlasBrain', 'subjT1',
'subjT2', 'subjT1GAD', 'subjT2GAD', 'subjSGGAD',
'subjBrain', 'atlasToSubj', 'output_dir'
],
output_names=['xml_filename'],
function=create_BRAINSCut_XML),
overwrite=True,
name="CreateBRAINSCutXML")
## HACK Makde better directory
CreateBRAINSCutXML.inputs.output_dir = "./" #os.path.join(baw200.base_dir, "BRAINSCut_output")
baw200.connect(BCM_Models, 'models', CreateBRAINSCutXML, 'model')
baw200.connect(BCM_Models, 'rho', CreateBRAINSCutXML, 'rho')
baw200.connect(BCM_Models, 'phi', CreateBRAINSCutXML, 'phi')
baw200.connect(BCM_Models, 'theta', CreateBRAINSCutXML, 'theta')
baw200.connect(BCM_Models, 'r_probabilityMaps', CreateBRAINSCutXML,
'r_probabilityMap')
baw200.connect(BCM_Models, 'l_probabilityMaps', CreateBRAINSCutXML,
'l_probabilityMap')
baw200.connect(BAtlas, 'template_t1', CreateBRAINSCutXML,
'atlasT1')
baw200.connect(BAtlas, 'template_brain', CreateBRAINSCutXML,
'atlasBrain')
baw200.connect(SplitAvgBABC, 'avgBABCT1', CreateBRAINSCutXML,
'subjT1')
baw200.connect(SplitAvgBABC, 'avgBABCT2', CreateBRAINSCutXML,
'subjT2')
baw200.connect(GADT1, 'outputVolume', CreateBRAINSCutXML,
'subjT1GAD')
baw200.connect(GADT2, 'outputVolume', CreateBRAINSCutXML,
'subjT2GAD')
baw200.connect(SGI, 'outputFileName', CreateBRAINSCutXML,
'subjSGGAD')
baw200.connect(BABC, 'outputLabels', CreateBRAINSCutXML,
'subjBrain')
baw200.connect(BFitAtlasToSubject, 'outputTransform',
CreateBRAINSCutXML, 'atlasToSubj')
#CreateBRAINSCutXML.inputs.atlasToSubj = "INTERNAL_REGISTER.mat"
#baw200.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')
if 1 == 1:
"""
BRAINSCut
"""
BRAINSCUT = pe.Node(interface=BRAINSCut(),
name="BRAINSCUT",
input_names=['netConfiguration'])
BRAINSCUT.inputs.applyModel = True
baw200.connect(CreateBRAINSCutXML, 'xml_filename', BRAINSCUT,
'netConfiguration')
"""
BRAINSTalairach
Not implemented yet.
"""
## Make deformed Atlas image space
if processingLevel > 2:
print(
"""
Run ANTS Registration at processingLevel={0}
""".format(processingLevel))
ComputeAtlasToSubjectTransform = pe.Node(
interface=ANTSWrapper(),
name="19_ComputeAtlasToSubjectTransform")
ComputeAtlasToSubjectTransform.inputs.output_prefix = "ANTS_"
baw200.connect(SplitAvgBABC, 'avgBABCT1',
ComputeAtlasToSubjectTransform, "fixed_T1_image")
baw200.connect(SplitAvgBABC, 'avgBABCT2',
ComputeAtlasToSubjectTransform, "fixed_T2_image")
baw200.connect(BAtlas, 'template_t1',
ComputeAtlasToSubjectTransform, "moving_T1_image")
baw200.connect(BAtlas, 'template_t1',
ComputeAtlasToSubjectTransform, "moving_T2_image")
WarpAtlas = pe.Node(interface=WarpAllAtlas(), name="19_WarpAtlas")
WarpAtlas.inputs.moving_atlas = atlas_fname_wpath
WarpAtlas.inputs.deformed_atlas = "./"
baw200.connect(ComputeAtlasToSubjectTransform, 'output_affine',
WarpAtlas, "affine_transform")
baw200.connect(ComputeAtlasToSubjectTransform, 'output_warp',
WarpAtlas, "deformation_field")
baw200.connect(SplitAvgBABC, 'avgBABCT1', WarpAtlas,
'reference_image')
if processingLevel > 3:
print(
"""
Run Freesurfer ReconAll at processingLevel={0}
""".format(processingLevel))
subj_id = os.path.basename(
os.path.dirname(os.path.dirname(baw200.base_dir)))
scan_id = os.path.basename(os.path.dirname(baw200.base_dir))
reconall = pe.Node(interface=ReconAll(), name="41_FS510")
reconall.inputs.subject_id = subj_id + '_' + scan_id
reconall.inputs.directive = 'all'
reconall.inputs.subjects_dir = '.'
baw200.connect(SplitAvgBABC, 'avgBABCT1', reconall, 'T1_files')
else:
print "Skipping freesurfer"
print "Start Processing"
#baw200.run(plugin='MultiProc', plugin_args={'n_procs' : 4})
baw200.run(
plugin='SGE',
plugin_args=dict(
template=GLOBAL_SGE_SCRIPT,
qsub_args=
"-S /bin/bash -q all.q -pe smp1 2-4 -o /dev/null -e /dev/null "))
def main():
arguments = docopt(__doc__)
study = arguments['<study>']
use_server = arguments['--log-to-server']
debug = arguments['--debug']
config = load_config(study)
if use_server:
add_server_handler(config)
if debug:
logger.setLevel(logging.DEBUG)
## setup some paths
study_base_dir = config.get_study_base()
fs_dir = config.get_path('freesurfer')
data_dir = config.get_path('nii')
# not sure where to put this. Potentially it could be very large
# keeping it means existing subjects don't get re-run.
# it could be deleted but then would need extra code to Determine
# if subjects have been run.
working_dir = os.path.join(study_base_dir,
'pipelines/workingdir_reconflow')
## These are overrides, for testing
base_dir = '/external/rprshnas01/tigrlab/'
fs_dir = os.path.join(base_dir, 'scratch/twright/pipelines/freesurfer',
study)
working_dir = os.path.join(
base_dir, 'scratch/twright/pipelines/workingdir_reconflow')
# freesurfer fails if the subjects dir doesn't exist
check_folder_exists(fs_dir)
# get the list of subjects that are not phantoms and have been qc'd
subject_list = config.get_subject_metadata()
subject_list = [
subject for subject in subject_list
if not dm_scanid.is_phantom(subject)
]
# Need to determine if the study has T2 (or FLAIR) scans,
# do this by looking in the study_config.yml for expected scantypes.
# Current pipelines add T2 files if they exist on a per-subject basis
# Nipype expects the each run of the pipeline to be the same across all subjects
# it is possible to set some parameters on a per-subject basis (see nu-iter setting)
# but is this desirable?
scan_types = get_common_scan_types(config)
if not 'T1' in scan_types:
msg = 'Study {} does not have T1 scans, aborting.'.format(study)
sys.exit(msg)
templates = {'T1': '{dm_subject_id}/{dm_subject_id}_??_T1_??*.nii.gz'}
if 'T2' in scan_types:
templates['T2'] = '{dm_subject_id}/{dm_subject_id}_??_T2_??*.nii.gz'
if 'FLAIR' in scan_types:
logger.debug('FLAIR processing not yet implemented')
#templates = {'T2': '{dm_subject_id}/{dm_subject_id}_??_FLAIR _??*.nii.gz'}
# setup the nipype nodes
# infosource justs iterates through the list of subjects
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
# For testing
subject_list = ['DTI_CMH_H001_02']
infosource.iterables = ('subject_id', subject_list)
# sf finds the files for each subject. The dmSelectFiles class
# overrides the nipype.SelectFiles adding checks that the numbers
# of files matches those defined in study_config.yml
sf = Node(dmSelectFiles(templates), name="selectFiles")
sf.inputs.base_directory = data_dir
# set_nuiter implements a simple function to set the iteration count
# on a subject by subject basis
set_nuiter = Node(Function(input_names=['subject_id'],
output_names=['nu_iter'],
function=get_nuiter_settings),
name='get_nuiter')
# reconall is the interface for the recon-all freesurfer function
# currently seem unable to specify multiple directives
# (e.g. -qcache and -notal-check)
reconall = Node(ReconAll(directive='all',
parallel=True,
subjects_dir=fs_dir),
name='recon-all')
# if this is running on a cluster, we can specify node specific requirements
# i.e. reconall runs well with lots of cores.
reconall.plugin_args = {
'qsub_args': '-l nodes=1:ppn=24',
'overwrite': True
}
# get_summary extracts the summary information from the output of reconall
get_summary = Node(EnigmaSummaryTask(), name='Enigma_Summaries')
## Create the workflow
reconflow = Workflow(name='reconflow')
reconflow.base_dir = working_dir
# need a different connection pattern and param for the reconall node
# if T2 files exist
sf_ra_conx = [('T1', 'T1_files')]
if 'T2' in scan_types:
reconall.inputs.use_T2 = True
sf_ra_conx.append('T2', 'T2_file')
## Connect the outputs from each node to the corresponding inputs
# Basically we link the defined outputs from each node, to the inputs of the next node
# Each item in the list is [node1, node2, [(output_node1, input_node2)]]
# Problem here due to incompatibilities between freesurfer 5 & 6
# this pattern works for freesurfer 5.3.0 (without the parallel flag for reconall)
# but failes for 6.0.0, which doesn't support the nuierations flag.
# reconflow.connect([(infosource, sf, [('subject_id', 'dm_subject_id')]),
# (infosource, set_nuiter, [('subject_id', 'subject_id')]),
# (sf, reconall, sf_ra_conx),
# (set_nuiter, reconall, [('nu_iter', 'flags')])])
# this is the freesurfer 6 compatible version
reconflow.connect([(infosource, sf, [('subject_id', 'dm_subject_id')]),
(infosource, reconall, [('subject_id', 'subject_id')]),
(sf, reconall, sf_ra_conx),
(reconall, get_summary,
[('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id'),
('subjects_dir', 'output_path')])])
# need to use a job template to ensure the environment is set correctly
# on the running nodes.
# Not sure why the current env isn't being passed
job_template = os.path.join(os.path.dirname(__file__),
'job_template_scc.sh')
## run the actual workflow.
# the pbsGraph plugin creates jobs for each node on a PBS torque using
# torque scheduling to keep them in order.
# Use plugin='SGEGraph' to run on lab cluster (not sure what will happen
# to the reconflow node if we don't have any 24 core machines).
# Don't specify a plugin to run on a single machine
reconflow.run(plugin='PBSGraph', plugin_args=dict(template=job_template))
def create_grvx_workflow(parameters):
parameters['paths']['output'].mkdir(exist_ok=True, parents=True)
parameters['timestamp'] = datetime.now().isoformat()
parameters_json = parameters['paths']['output'] / 'parameters.json'
with parameters_json.open('w') as f:
json_dump(parameters, f, indent=2, cls=JSONEncoder_path)
bids = bids_node(parameters)
node_reconall = Node(ReconAll(), name='freesurfer')
node_reconall.inputs.subjects_dir = str(parameters['paths']['freesurfer_subjects_dir'])
node_reconall.inputs.flags = ['-cw256', ]
node_corr = Node(function_corr, name='corr_fmri_ecog')
node_corr.inputs.pvalue = parameters['corr']['pvalue']
node_corr_allfreq = Node(function_corr_allfreq, name='corr_fmri_ecog_allfreq')
node_corr_allfreq.inputs.pvalue = parameters['corr']['pvalue']
node_corr_allfreq.inputs.min_n_sign_elec = parameters['corr']['min_n_sign_elec']
node_corr_summary = JoinNode(
function_corr_summary,
name='corr_fmri_ecog_summary',
joinsource='bids',
joinfield=('in_files', 'ecog_files', 'fmri_files'),
)
w_fmri = workflow_fmri(parameters)
w_ieeg = workflow_ieeg(parameters)
w = Workflow('workflow')
w.base_dir = str(parameters['paths']['output'])
if parameters['fmri']['graymatter']:
w.connect(bids, 'subject', node_reconall, 'subject_id') # we might use freesurfer for other stuff too
w.connect(bids, 'anat', node_reconall, 'T1_files')
w.connect(node_reconall, 'ribbon', w_fmri, 'graymatter.ribbon')
w.connect(bids, 'ieeg', w_ieeg, 'read.ieeg')
w.connect(bids, 'elec', w_ieeg, 'read.electrodes')
w.connect(bids, 'anat', w_fmri, 'bet.in_file')
w.connect(bids, 'func', w_fmri, 'feat_design.func')
w.connect(bids, 'elec', w_fmri, 'at_elec.electrodes')
w.connect(w_ieeg, 'ecog_compare.tsv_compare', node_corr, 'ecog_file')
w.connect(w_fmri, 'at_elec.fmri_vals', node_corr, 'fmri_file')
w.connect(w_ieeg, 'ecog_compare_allfreq.compare', node_corr_allfreq, 'ecog_file')
w.connect(w_fmri, 'at_elec.fmri_vals', node_corr_allfreq, 'fmri_file')
w.connect(node_corr, 'out_file', node_corr_summary, 'in_files')
w.connect(w_ieeg, 'ecog_compare.tsv_compare', node_corr_summary, 'ecog_files')
w.connect(w_fmri, 'at_elec.fmri_vals', node_corr_summary, 'fmri_files')
w.write_graph(graph2use='flat')
log_dir = parameters['paths']['output'] / 'log'
config.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': True,
},
})
rmtree(log_dir, ignore_errors=True)
log_dir.mkdir()
logging.update_logging(config)
return w
sessionIterator = ['01'] # what session numbers are checked
# create identity interface node
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list,)]
# get the anat file for every session for the subject
for session in sessionIterator:
anat_file = opj('sub-{subject_id}', 'ses-' + session, 'anat', 'sub-{subject_id}_ses-' + session + '_T1w.nii')
templates = {'anat': anat_file}
selectfiles = Node(SelectFiles(templates, base_directory='/mnt/Filbey/Evan/examples/BIDS'), name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory="/mnt/Filbey/Evan/tmp/sinker",
container="datasink"),
name="datasink")
# reconAll node
recon_all = Node(ReconAll(), name="reconAll")
wf_sub = Workflow(name="choosing_subjects")
wf_sub.connect(infosource, "subject_id", selectfiles, "subject_id")
wf_sub.connect(selectfiles, "anat", datasink, "anat_files")
wf_sub.connect(selectfiles, "anat", recon_all, 'input.inputspec.T1_files')
wf_sub.connect(infosource, "subject_id", recon_all, 'input.inputspec.subject_id')
wf_sub.run()
def create_main_workflow_FS_segmentation():
# check envoiroment variables
if not os.environ.get('FREESURFER_HOME'):
raise RuntimeError('FREESURFER_HOME environment variable not set')
if not os.environ.get('MNE_ROOT'):
raise RuntimeError('MNE_ROOT environment variable not set')
if not os.environ.get('SUBJECTS_DIR'):
os.environ["SUBJECTS_DIR"] = sbj_dir
if not op.exists(sbj_dir):
os.mkdir(sbj_dir)
print 'SUBJECTS_DIR %s ' % os.environ["SUBJECTS_DIR"]
# (1) iterate over subjects to define paths with templates -> Infosource
# and DataGrabber
# Node: SubjectData - we use IdentityInterface to create our own node,
# to specify the list of subjects the pipeline should be executed on
infosource = pe.Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', subjects_list)
# Grab data
# the template can be filled by other inputs
# Here we define an input field for datagrabber called subject_id.
# This is then used to set the template (see %s in the template).
# we can look for DICOM files or .nii ones
if is_nii:
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['struct']),
name='datasource')
datasource.inputs.template = '%s/*/anat/%s*.nii.gz'
datasource.inputs.template_args = dict(struct=[['subject_id',
'subject_id']])
else:
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['dcm_file']),
name='datasource')
datasource.inputs.template = '%s*/*.dcm'
datasource.inputs.template_args = dict(dcm_file=[['subject_id']])
datasource.inputs.base_directory = MRI_path # dir where the MRI files are
datasource.inputs.sort_filelist = True
# get the path of the first dicom file
def get_first_file(dcm_files):
return dcm_files[0]
# return the path of the struct filename in the MRI sbj dir that will be
# the input of MRI convert routine
def get_MRI_sbj_dir(dcm_file):
from nipype.utils.filemanip import split_filename as split_f
import os.path as op
MRI_sbj_dir, basename, ext = split_f(dcm_file)
struct_filename = op.join(MRI_sbj_dir, 'struct.nii.gz')
return struct_filename
get_firstfile = pe.Node(interface=Function(input_names=['dcm_files'],
output_names=['dcm_file'],
function=get_first_file), name='get_firstfile')
get_MRI_sbjdir = pe.Node(interface=Function(input_names=['dcm_file'],
output_names=['struct_filename'],
function=get_MRI_sbj_dir), name='get_MRI_sbjdir')
# MRI_convert Node
# We use it if we don't have a .nii.gz file
# The output of mriconvert is the input of recon-all
mri_convert = pe.Node(interface=MRIConvert(), infields=['in_file'],
outfields=['out_file'],
name='mri_convert')
# (2) ReconAll Node to generate surfaces and parcellations of structural
# data from anatomical images of a subject.
recon_all = pe.Node(interface=ReconAll(), infields=['T1_files'],
name='recon_all')
recon_all.inputs.subjects_dir = sbj_dir
recon_all.inputs.directive = 'all'
# reconall_workflow will be a node of the main workflow
reconall_workflow = pe.Workflow(name=FS_WF_name)
reconall_workflow.base_dir = MRI_path
reconall_workflow.connect(infosource, 'subject_id',
recon_all, 'subject_id')
reconall_workflow.connect(infosource, 'subject_id',
datasource, 'subject_id')
if is_nii:
reconall_workflow.connect(datasource, 'struct', recon_all, 'T1_files')
else:
reconall_workflow.connect(datasource, 'dcm_file',
get_firstfile, 'dcm_files')
reconall_workflow.connect(get_firstfile, 'dcm_file',
get_MRI_sbjdir, 'dcm_file')
reconall_workflow.connect(get_firstfile, 'dcm_file',
mri_convert, 'in_file')
reconall_workflow.connect(get_MRI_sbjdir, 'struct_filename',
mri_convert, 'out_file')
reconall_workflow.connect(mri_convert, 'out_file',
recon_all, 'T1_files')
# (3) BEM generation by the watershed algo of MNE C
main_workflow = pe.Workflow(name=MAIN_WF_name)
main_workflow.base_dir = sbj_dir
# I mode: WatershedBEM Interface of nipype
bem_generation = pe.Node(interface=WatershedBEM(),
infields=['subject_id', 'subjects_dir', 'atlas_mode'],
outfields=['mesh_files'],
name='bem_generation')
bem_generation.inputs.subjects_dir = sbj_dir
bem_generation.inputs.atlas_mode = True
main_workflow.connect(reconall_workflow, 'recon_all.subject_id',
bem_generation, 'subject_id')
# II mode: make_watershed_bem of MNE Python package
def mne_watershed_bem(sbj_dir, sbj_id):
from mne.bem import make_watershed_bem
print 'call make_watershed_bem'
make_watershed_bem(sbj_id, sbj_dir, overwrite=True)
call_mne_watershed_bem = pe.Node(interface=Function(input_names=['sbj_dir', 'sbj_id'],
output_names=['sbj_id'],
function = mne_watershed_bem),
name = 'call_mne_watershed_bem')
# copy the generated meshes from bem/watershed to bem/ and change the names
# according to MNE
def copy_surfaces(sbj_id, mesh_files):
import os
import os.path as op
from smri_params import sbj_dir
from mne.report import Report
report = Report()
surf_names = ['brain_surface', 'inner_skull_surface',
'outer_skull_surface', 'outer_skin_surface']
new_surf_names = ['brain.surf', 'inner_skull.surf',
'outer_skull.surf', 'outer_skin.surf']
bem_dir = op.join(sbj_dir, sbj_id, 'bem')
surface_dir = op.join(sbj_dir, sbj_id, 'bem/watershed')
for i in range(len(surf_names)):
os.system('cp %s %s' %(op.join(surface_dir,sbj_id + '_' + surf_names[i]),
op.join(bem_dir, new_surf_names[i])))
#op.join(bem_dir,sbj_id + '-' + new_surf_names[i])))
report.add_bem_to_section(subject=sbj_id, subjects_dir=sbj_dir)
report_filename = op.join(bem_dir, "BEM_report.html")
print '*** REPORT file %s written ***' % report_filename
print report_filename
report.save(report_filename, open_browser=False, overwrite=True)
return sbj_id
copy_bem_surf = pe.Node(interface=Function(input_names=['sbj_id', 'mesh_files'],
output_names=['sbj_id'],
function = copy_surfaces),
name='copy_bem_surf')
main_workflow.connect(infosource, 'subject_id', copy_bem_surf, 'sbj_id')
main_workflow.connect(bem_generation, 'mesh_files',
copy_bem_surf, 'mesh_files')
return main_workflow
#convert dicom to nifti
test_subject.get_seqs()
test_subject.to_bids(out_path)
"""
# create array of subject objects with path to raw data
subjectList = [Subject] # Subject
# includes pointers to bids format data and a to_bids function
# recon all freesurfer - build commamd
for subject in subjectList:
# initialize reconAll interface
reconall = ReconAll()
# add various attributes to the interface
reconall.inputs.subject_id = subject.subjectID
reconall.inputs.directive = 'foo'
reconall.inputs.subjects_dir = subject.bidsPath
reconall.inputs.T1_files = subject.seqs.anat
# execute the command in shell with above attributes
#todo
#check if output exists prior to execution or catch error from command line
reconall.cmdline
# add recon-all & ants to subject object
# need to add new attributes to subject class. subclass? add to existing? what is "pythonic"? how do I enforce order in
# processing and handle process failure or error? could try hashing the output prior to running the command. this
# checks if the object exists. shell script may have the check already but the goal is to not reprocess anything.
def create_main_workflow_FS_segmentation():
# Check envoiroment variables
if not os.environ.get('FREESURFER_HOME'):
raise RuntimeError('FREESURFER_HOME environment variable not set')
if not os.environ.get('SUBJECTS_DIR'):
os.environ["SUBJECTS_DIR"] = subjects_dir
if not op.exists(subjects_dir):
os.mkdir(subjects_dir)
print('SUBJECTS_DIR %s ' % os.environ["SUBJECTS_DIR"])
main_workflow = pe.Workflow(name=MAIN_WF_name)
main_workflow.base_dir = subjects_dir
# (1) we create a node to pass input filenames to DataGrabber from nipype
# iterate over subjects
infosource = create_iterator(['subject_id'], [subject_ids])
# # and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
# Here we define an input field for datagrabber called subject_id.
# This is then used to set the template (see %s in the template).
# we look for .nii files
template_path = '%s/anatomy/highres001.nii.gz'
template_args = [['subject_id']]
infields = ['subject_id']
datasource = create_datagrabber(data_path,
template_path,
template_args,
infields=infields)
# (2) ReconAll Node to generate surfaces and parcellations of structural
# data from anatomical images of a subject.
recon_all = pe.Node(interface=ReconAll(),
infields=['T1_files'],
name='recon_all')
recon_all.inputs.subjects_dir = subjects_dir
recon_all.inputs.directive = 'all'
# reconall_workflow will be a node of the main workflow
reconall_workflow = pe.Workflow(name=FS_WF_name)
reconall_workflow.base_dir = wf_path
reconall_workflow.connect(infosource, 'subject_id', recon_all,
'subject_id')
reconall_workflow.connect(infosource, 'subject_id', datasource,
'subject_id')
reconall_workflow.connect(datasource, 'raw_file', recon_all, 'T1_files')
# (3) BEM generation by make_watershed_bem of MNE Python package
bem_generation = pe.Node(interface=Function(
input_names=['subjects_dir', 'sbj_id'],
output_names=['sbj_id'],
function=_create_bem_sol),
name='call_mne_watershed_bem')
bem_generation.inputs.subjects_dir = subjects_dir
main_workflow.connect(reconall_workflow, 'recon_all.subject_id',
bem_generation, 'sbj_id')
return main_workflow
BET().version: BET
},
"CAT12 Segmentation": {
"12.6": Cat12Segmentation
},
"fslreorient2std": {
Reorient2Std().version: Reorient2Std
},
"FAST": {
FAST().version: FastWrapper
},
"FLIRT": {
FLIRT().version: FLIRT
},
"FNIRT": {
FNIRT().version: FNIRT
},
"FSL Anatomical Processing Script": {
FslAnat.__version__: FslAnat
},
"SUSAN": {
SUSAN().version: SUSAN
},
"ReconAll": {
ReconAll().version: ReconAll
},
"robustfov": {
RobustFOV().version: RobustFOV
},
}
cursor = connection.cursor()
num_threads = 12
base_dir = "/Shared/sinapse/CACHE/20161010_AtrophySimulation_Baseline_CACHE"
for row in cursor.execute(
"SELECT t1_image_file, t2_image_file, session_id FROM input"):
session_id = str(row[2])
t1_file = str(row[0])
t2_file = str(row[1])
wf = Workflow(name="FreeSurfer_{0}".format(session_id))
subject_directory = os.path.dirname(os.path.dirname(t1_file))
recon_all = Node(ReconAll(), "ReconAll")
recon_all.inputs.T1_files = [t1_file]
recon_all.inputs.T2_file = t2_file
recon_all.inputs.openmp = num_threads
recon_all.inputs.subject_id = "FreeSurfer"
recon_all.inputs.flags = "-no-isrunning"
recon_all.inputs.subjects_dir = os.path.join(
"/Shared/sinapse/CACHE/20161010_AtrophySimulation_Baseline",
session_id)
recon_all.plugin_args = plugin_args = {
"qsub_args": "-q HJ,UI,all.q,COE -pe smp {0}".format(num_threads),
"overwrite": True
}
hncma_atlas = os.path.join(subject_directory, "WarpedAtlas2Subject",
"hncma_atlas.nii.gz")
def workflow_fmri(PARAMETERS, FREESURFER_PATH):
"""TODO: input and output"""
input = Node(
IdentityInterface(fields=['subject', 'T1w', 'bold', 'electrodes']),
name='input')
node_bet = Node(BET(), name='bet')
node_bet.inputs.frac = 0.5
node_bet.inputs.vertical_gradient = 0
node_bet.inputs.robust = True
node_featdesign = Node(function_prepare_design, name='feat_design')
node_feat = Node(FEAT(), name='feat')
node_compare = Node(function_fmri_compare, name='fmri_compare')
node_compare.inputs.measure = PARAMETERS['fmri_compare']['measure']
node_compare.inputs.normalize_to_mean = PARAMETERS['fmri_compare'][
'normalize_to_mean']
node_upsample = Node(
FLIRT(), name='upsample') # not perfect, there is a small offset
node_upsample.inputs.apply_isoxfm = UPSAMPLE_RESOLUTION
node_upsample.inputs.interp = 'nearestneighbour'
node_downsample = Node(
FLIRT(), name='downsample') # not perfect, there is a small offset
node_downsample.inputs.apply_xfm = True
node_downsample.inputs.uses_qform = True
# node_downsample.inputs.apply_isoxfm = DOWNSAMPLE_RESOLUTION
node_downsample.inputs.interp = 'nearestneighbour'
node_threshold = Node(Threshold(), name='threshold')
node_threshold.inputs.thresh = GRAYMATTER_THRESHOLD
node_threshold.inputs.args = '-bin'
node_graymatter = Node(function_fmri_graymatter, name='graymatter')
node_realign_gm = Node(FLIRT(), name='realign_gm')
node_realign_gm.inputs.apply_xfm = True
node_realign_gm.inputs.uses_qform = True
kernel_sizes = arange(
PARAMETERS['at_elec']['kernel_start'],
PARAMETERS['at_elec']['kernel_end'],
PARAMETERS['at_elec']['kernel_step'],
)
node_atelec = Node(function_fmri_atelec, name='at_elec')
node_atelec.inputs.distance = PARAMETERS['at_elec']['distance']
node_atelec.inputs.kernel_sizes = list(kernel_sizes)
node_atelec.inputs.graymatter = PARAMETERS['graymatter']
w = Workflow('fmri')
w.connect(input, 'T1w', node_bet, 'in_file')
w.connect(input, 'bold', node_featdesign, 'func')
w.connect(input, 'electrodes', node_atelec, 'electrodes')
w.connect(node_bet, 'out_file', node_featdesign, 'anat')
w.connect(node_featdesign, 'fsf_file', node_feat, 'fsf_file')
w.connect(node_feat, 'feat_dir', node_compare, 'feat_path')
if PARAMETERS['upsample']:
w.connect(node_compare, 'out_file', node_upsample, 'in_file')
w.connect(node_compare, 'out_file', node_upsample, 'reference')
w.connect(node_upsample, 'out_file', node_atelec, 'in_file')
else:
w.connect(node_compare, 'out_file', node_atelec, 'in_file')
if PARAMETERS['graymatter']:
node_reconall = Node(ReconAll(), name='freesurfer')
node_reconall.inputs.subjects_dir = str(FREESURFER_PATH)
node_reconall.inputs.flags = [
'-cw256',
]
w.connect(input, 'T1w', node_reconall, 'T1_files')
w.connect(input, 'subject', node_reconall, 'subject_id')
if PARAMETERS['upsample']:
w.connect(node_graymatter, 'out_file', node_realign_gm, 'in_file')
w.connect(node_upsample, 'out_file', node_realign_gm, 'reference')
w.connect(node_realign_gm, 'out_file', node_threshold, 'in_file')
else:
w.connect(node_graymatter, 'out_file', node_downsample, 'in_file')
w.connect(node_compare, 'out_file', node_downsample, 'reference')
w.connect(node_downsample, 'out_file', node_threshold, 'in_file')
w.connect(node_threshold, 'out_file', node_atelec, 'graymatter')
w.connect(node_reconall, 'ribbon', node_graymatter, 'ribbon')
return w
info = dict(T1=[['subject_id']])
infosource = Node(IdentityInterface(fields=['subject_id']), name='infosource')
infosource.iterables = ('subject_id', sids)
# Create a datasource node to get the T1 file
datasource = Node(DataGrabber(infields=['subject_id'], outfields=info.keys()),
name='datasource')
datasource.inputs.template = '%s/%s'
datasource.inputs.base_directory = os.path.abspath(data_dir)
datasource.inputs.field_template = dict(T1='%s/s1/anatomy/T1_002.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
reconall_node = Node(ReconAll(), name='reconall_node')
reconall_node.inputs.openmp = 2
reconall_node.inputs.args = '-hippocampal-subfields-T1'
reconall_node.inputs.subjects_dir = '/home/data/madlab/surfaces/emuR01'
reconall_node.plugin_args = {
'sbatch_args': ('-p investor --qos pq_madlab -n 2'),
'overwrite': True
}
wf = Workflow(name='fsrecon')
wf.connect(infosource, 'subject_id', datasource, 'subject_id')
wf.connect(infosource, 'subject_id', reconall_node, 'subject_id')
wf.connect(datasource, 'T1', reconall_node, 'T1_files')
wf.base_dir = os.path.abspath('/scratch/madlab/emu/')
"title": "12.7",
"description": "",
"fixed_run_method_kwargs": {
"verbose_output_dict": True
},
"input": CAT12_SEGMENTATION_INPUT_SPECIFICATION,
"output": CAT12_SEGMENTATION_OUTPUT_SPECIFICATION,
}],
},
{
"title":
"ReconAll",
"description":
"Performs all, or any part of, the FreeSurfer cortical reconstruction process.", # noqa: E501
"versions": [{
"title": ReconAll().version or "1.0",
"description":
f"Default FreeSurfer ReconAll version for nipype {_NIPYPE_VERSION}.", # noqa: E501
"input": RECON_ALL_INPUT_SPECIFICATION,
"output": RECON_ALL_OUTPUT_SPECIFICATION,
"nested_results_attribute": "outputs.get_traitsfree",
"max_parallel": 10,
}],
},
{
"title":
"mrconvert",
"description":
"Performs conversion between different file types and optionally extract a subset of the input image", # noqa: E501
"versions": [{
"title": MRConvert.__version__ or "1.0",