def create_fs_logb_workflow_for_both_hemispheres(name="FSLOGB",
plugin_args=None,
ml=False,
config=None):
"""Creates a workflow that connects FreeSurfer with LOGISMOS-B
:param name:
:param plugin_args:
:param ml:
:param config:
:return:
"""
fslogb_wf = Workflow(name=name)
inputspec = Node(IdentityInterface([
'recoding_file', 'lut_file', 'aseg_presurf', 'rawavg', 't2_raw',
'lh_white', 'rh_white', 'hncma_atlas'
]),
name="inputspec")
inputspec.inputs.recoding_file = get_local_file_location(
"abc_fs_equivelants.json")
inputspec.inputs.lut_file = get_local_file_location(
"FreeSurferColorLUT.csv")
# create outputspec with gm and wm surfaces
outputs = [
'lh_gm_surf_file', 'lh_wm_surf_file', 'rh_gm_surf_file',
'rh_wm_surf_file'
]
outputspec = Node(IdentityInterface(outputs), name="outputspec")
for hemi in ('lh', 'rh'):
hemi_logb_wf = create_fs_compatible_logb_workflow(
"{0}_LOGBWF".format(hemi), plugin_args=plugin_args, config=config)
hemi_logb_wf.inputs.inputspec.hemi = hemi
fslogb_wf.connect([(inputspec, hemi_logb_wf,
[('aseg_presurf', 'inputspec.aseg'),
('rawavg', 'inputspec.t1_file'),
('t2_raw', 'inputspec.t2_file'),
('hncma_atlas', 'inputspec.hncma_atlas'),
('{0}_white'.format(hemi), 'inputspec.white')]),
(inputspec, hemi_logb_wf,
[('recoding_file', 'inputspec.recoding_file'),
('lut_file', 'inputspec.lut_file')])])
# move the outputs from logb to the outputspec
fslogb_wf.connect([(hemi_logb_wf, outputspec, [
('outputspec.gmsurface_file', '{0}_gm_surf_file'.format(hemi)),
('outputspec.wmsurface_file', '{0}_wm_surf_file'.format(hemi))
])])
return fslogb_wf
def wf_transform_anat(in_file_list, in_matrix_file_list, reference):
func2std_xform = MapNode(
FLIRT(output_type='NIFTI', apply_xfm=True),
name="func2std_xform",
iterfield=['in_file', 'in_matrix_file', 'reference'])
inputspec = Node(IdentityInterface(
fields=['in_file_list', 'in_matrix_file_list', 'reference']),
name="inputspec")
inputspec.inputs.in_file_list = in_file_list
inputspec.inputs.in_matrix_file_list = in_matrix_file_list
inputspec.inputs.reference = reference
wf_transform_anat = Workflow(name="wf_transform_anat")
wf_transform_anat.connect(inputspec, 'in_file_list', func2std_xform,
'in_file')
wf_transform_anat.connect(inputspec, 'in_matrix_file_list', func2std_xform,
'in_matrix_file')
wf_transform_anat.connect(inputspec, 'reference', func2std_xform,
'reference')
return wf_transform_anat
def _skullstrip_register_anat(self, input_img, output_path):
output_path = os.path.abspath(output_path)
workflow = Workflow('register_anat', base_dir=output_path)
# Skull strip
anatstrip = Node(skullstrip.Robex(in_file=input_img),
name='skullstrip')
# Register to MNI template
register = Node(ants.RegistrationSynQuick(
fixed_image=strT1TemplatePath,
num_threads=self.n_ants_jobs,
output_prefix=os.path.join(output_path, 'registered_anat_')),
name='register',
mem_gb=16,
n_procs=self.n_ants_jobs)
workflow.connect(anatstrip, 'out_file', register, 'moving_image')
workflow.run()
return os.path.join(output_path, 'registered_anat_Warped.nii.gz')
def main(paths, options_binary_string, ANAT, num_proc=7):
json_path = paths[0]
base_directory = paths[1]
motion_correction_bet_directory = paths[2]
parent_wf_directory = paths[3]
# functional_connectivity_directory=paths[4]
coreg_reg_directory = paths[5]
atlas_resize_reg_directory = paths[6]
subject_list = paths[7]
datasink_name = paths[8]
# fc_datasink_name=paths[9]
atlasPath = paths[10]
# brain_path=paths[11]
# mask_path=paths[12]
# atlas_path=paths[13]
# tr_path=paths[14]
# motion_params_path=paths[15]
# func2std_mat_path=paths[16]
# MNI3mm_path=paths[17]
# demographics_file_path = paths[18]
# phenotype_file_path = paths[19]
data_directory = paths[20]
number_of_subjects = len(subject_list)
print("Working with ", number_of_subjects, " subjects.")
# Create our own custom function - BIDSDataGrabber using a Function Interface.
# In[858]:
def get_nifti_filenames(subject_id, data_dir):
# Remember that all the necesary imports need to be INSIDE the function for the Function Interface to work!
from bids.grabbids import BIDSLayout
layout = BIDSLayout(data_dir)
run = 1
anat_file_path = [
f.filename for f in layout.get(
subject=subject_id, type='T1w', extensions=['nii', 'nii.gz'])
]
func_file_path = [
f.filename for f in layout.get(subject=subject_id,
type='bold',
run=run,
extensions=['nii', 'nii.gz'])
]
if len(anat_file_path) == 0:
return None, func_file_path[0] # No Anatomical files present
return anat_file_path[0], func_file_path[0]
BIDSDataGrabber = Node(Function(
function=get_nifti_filenames,
input_names=['subject_id', 'data_dir'],
output_names=['anat_file_path', 'func_file_path']),
name='BIDSDataGrabber')
# BIDSDataGrabber.iterables = [('subject_id',subject_list)]
BIDSDataGrabber.inputs.data_dir = data_directory
# ## Return TR
def get_TR(in_file):
from bids.grabbids import BIDSLayout
data_directory = '/home1/varunk/data/ABIDE1/RawDataBIDs'
layout = BIDSLayout(data_directory)
metadata = layout.get_metadata(path=in_file)
TR = metadata['RepetitionTime']
return TR
# ---------------- Added new Node to return TR and other slice timing correction params-------------------------------
def _getMetadata(in_file):
from bids.grabbids import BIDSLayout
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# create a file handler
handler = logging.FileHandler('progress.log')
# add the handlers to the logger
logger.addHandler(handler)
interleaved = True
index_dir = False
data_directory = '/home1/varunk/data/ABIDE1/RawDataBIDs'
layout = BIDSLayout(data_directory)
metadata = layout.get_metadata(path=in_file)
print(metadata)
logger.info('Extracting Meta Data of file: %s', in_file)
try:
tr = metadata['RepetitionTime']
except KeyError:
print(
'Key RepetitionTime not found in task-rest_bold.json so using a default of 2.0 '
)
tr = 2
logger.error(
'Key RepetitionTime not found in task-rest_bold.json for file %s so using a default of 2.0 ',
in_file)
try:
slice_order = metadata['SliceAcquisitionOrder']
except KeyError:
print(
'Key SliceAcquisitionOrder not found in task-rest_bold.json so using a default of interleaved ascending '
)
logger.error(
'Key SliceAcquisitionOrder not found in task-rest_bold.json for file %s so using a default of interleaved ascending',
in_file)
return tr, index_dir, interleaved
if slice_order.split(' ')[0] == 'Sequential':
interleaved = False
if slice_order.split(' ')[1] == 'Descending':
index_dir = True
return tr, index_dir, interleaved
getMetadata = Node(Function(
function=_getMetadata,
input_names=['in_file'],
output_names=['tr', 'index_dir', 'interleaved']),
name='getMetadata')
# ### Skipping 4 starting scans
# Extract ROI for skipping first 4 scans of the functional data
# > **Arguments:**
# t_min: (corresponds to time dimension) Denotes the starting time of the inclusion
# t_size: Denotes the number of scans to include
#
# The logic behind skipping 4 initial scans is to take scans after the subject has stabalized in the scanner.
# In[863]:
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1),
output_type='NIFTI',
name="extract")
# ### Slice time correction
# Created a Node that does slice time correction
# > **Arguments**:
# index_dir=False -> Slices were taken bottom to top i.e. in ascending order
# interleaved=True means odd slices were acquired first and then even slices [or vice versa(Not sure)]
slicetimer = Node(SliceTimer(output_type='NIFTI'), name="slicetimer")
# ### Motion Correction
# Motion correction is done using fsl's mcflirt. It alligns all the volumes of a functional scan to each other
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True, save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# Just a dummy node to transfer the output of Mcflirt to the next workflow. Needed if we didnt want to use the Mcflirt
from_mcflirt = Node(IdentityInterface(fields=['in_file']),
name="from_mcflirt")
# ### Skull striping
# I used fsl's BET
# In[868]:
skullStrip = Node(BET(mask=False, frac=0.3, robust=True),
name='skullStrip') #
# *Note*: Do not include special characters in ```name``` field above coz then wf.writegraph will cause issues
# ## Resample
# I needed to resample the anatomical file from 1mm to 3mm. Because registering a 1mm file was taking a huge amount of time.
#
# In[872]:
# Resample - resample anatomy to 3x3x3 voxel resolution
resample_mni = Node(
Resample(
voxel_size=(3, 3, 3),
resample_mode='Cu', # cubic interpolation
outputtype='NIFTI'),
name="resample_mni")
resample_anat = Node(
Resample(
voxel_size=(3, 3, 3),
resample_mode='Cu', # cubic interpolation
outputtype='NIFTI'),
name="resample_anat")
# In[873]:
resample_atlas = Node(
Resample(
voxel_size=(3, 3, 3),
resample_mode='NN', # cubic interpolation
outputtype='NIFTI'),
name="resample_atlas")
resample_atlas.inputs.in_file = atlasPath
# # Matrix operations
# ### For concatenating the transformation matrices
concat_xform = Node(ConvertXFM(concat_xfm=True), name='concat_xform')
# Node to calculate the inverse of func2std matrix
inv_mat = Node(ConvertXFM(invert_xfm=True), name='inv_mat')
# ## Extracting the mean brain
meanfunc = Node(interface=ImageMaths(op_string='-Tmean', suffix='_mean'),
name='meanfunc')
meanfuncmask = Node(interface=BET(mask=True, no_output=True, frac=0.3),
name='meanfuncmask')
# ## Apply Mask
# Does BET (masking) on the whole func scan [Not using this, creates bug for join node]
maskfunc = Node(interface=ImageMaths(suffix='_bet', op_string='-mas'),
name='maskfunc')
# Does BET (masking) on the mean func scan
maskfunc4mean = Node(interface=ImageMaths(suffix='_bet', op_string='-mas'),
name='maskfunc4mean')
# ## Datasink
# I needed to define the structure of what files are saved and where.
# Create DataSink object
dataSink = Node(DataSink(), name='datasink')
# Name of the output folder
dataSink.inputs.base_directory = opj(base_directory, datasink_name)
# Define substitution strings so that the data is similar to BIDS
substitutions = [
('_subject_id_', 'sub-'), ('_resample_brain_flirt.nii_brain', ''),
('_roi_st_mcf_flirt.nii_brain_flirt', ''),
('task-rest_run-1_bold_roi_st_mcf.nii', 'motion_params'),
('T1w_resample_brain_flirt_sub-0050002_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt',
'fun2std')
]
# Feed the substitution strings to the DataSink node
dataSink.inputs.substitutions = substitutions
# ### Apply Mask to functional data
# Mean file of the motion corrected functional scan is sent to
# skullStrip to get just the brain and the mask_image.
# Mask_image is just a binary file (containing 1 where brain is present and 0 where it isn't).
# After getting the mask_image form skullStrip, apply that mask to aligned
# functional image to extract its brain and remove the skull
# In[889]:
# Function
# in_file: The file on which you want to apply mask
# in_file2 = mask_file: The mask you want to use. Make sure that mask_file has same size as in_file
# out_file : Result of applying mask in in_file -> Gives the path of the output file
def applyMask_func(in_file, in_file2):
import numpy as np
import nibabel as nib
import os
from os.path import join as opj
# convert from unicode to string : u'/tmp/tmp8daO2Q/..' -> '/tmp/tmp8daO2Q/..' i.e. removes the prefix 'u'
mask_file = in_file2
brain_data = nib.load(in_file)
mask_data = nib.load(mask_file)
brain = brain_data.get_data().astype('float32')
mask = mask_data.get_data()
# applying mask by multiplying elementwise to the binary mask
if len(brain.shape) == 3: # Anat file
brain = np.multiply(brain, mask)
elif len(brain.shape) > 3: # Functional File
for t in range(brain.shape[-1]):
brain[:, :, :, t] = np.multiply(brain[:, :, :, t], mask)
else:
pass
# Saving the brain file
path = os.getcwd()
in_file_split_list = in_file.split('/')
in_file_name = in_file_split_list[-1]
out_file = in_file_name + '_brain.nii.gz' # changing name
brain_with_header = nib.Nifti1Image(brain,
affine=brain_data.affine,
header=brain_data.header)
nib.save(brain_with_header, out_file)
out_file = opj(path, out_file)
out_file2 = in_file2
return out_file, out_file2
# #### Things learnt:
# 1. I found out that whenever a node is being executed, it becomes the current directory and whatever file you create now, will be stored here.
# 2. #from IPython.core.debugger import Tracer; Tracer()() # Debugger doesnt work in nipype
# Wrap the above function inside a Node
# In[890]:
applyMask = Node(Function(function=applyMask_func,
input_names=['in_file', 'in_file2'],
output_names=['out_file', 'out_file2']),
name='applyMask')
# ### Some nodes needed for Co-registration and Normalization
# Node for getting the xformation matrix
func2anat_reg = Node(FLIRT(output_type='NIFTI'), name="func2anat_reg")
# Node for applying xformation matrix to functional data
func2std_xform = Node(FLIRT(output_type='NIFTI', apply_xfm=True),
name="func2std_xform")
# Node for applying xformation matrix to functional data
std2func_xform = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='nearestneighbour'),
name="std2func_xform")
# Node for Normalizing/Standardizing the anatomical and getting the xformation matrix
anat2std_reg = Node(FLIRT(output_type='NIFTI'), name="anat2std_reg")
# I wanted to use the MNI file as input to the workflow so I created an Identity
# Node that reads the MNI file path and outputs the same MNI file path.
# Then I connected this node to whereever it was needed.
MNI152_2mm = Node(IdentityInterface(fields=['standard_file', 'mask_file']),
name="MNI152_2mm")
# Set the mask_file and standard_file input in the Node. This setting sets the input mask_file permanently.
MNI152_2mm.inputs.mask_file = os.path.expandvars(
'$FSLDIR/data/standard/MNI152_T1_2mm_brain_mask.nii.gz')
MNI152_2mm.inputs.standard_file = os.path.expandvars(
'$FSLDIR/data/standard/MNI152_T1_2mm_brain.nii.gz')
# MNI152_2mm.inputs.mask_file = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'
# MNI152_2mm.inputs.standard_file = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz'
# ## Band Pass Filtering
# Let's do a band pass filtering on the data using the code from https://neurostars.org/t/bandpass-filtering-different-outputs-from-fsl-and-nipype-custom-function/824/2
### AFNI
bandpass = Node(afni.Bandpass(highpass=0.008,
lowpass=0.08,
despike=False,
no_detrend=True,
notrans=True,
outputtype='NIFTI_GZ'),
name='bandpass')
# ### Following is a Join Node that collects the preprocessed file paths and saves them in a file
# In[902]:
def save_file_list_function_in_brain(in_brain):
import numpy as np
import os
from os.path import join as opj
file_list = np.asarray(in_brain)
print('######################## File List ######################: \n',
file_list)
np.save('brain_file_list', file_list)
file_name = 'brain_file_list.npy'
out_brain = opj(os.getcwd(), file_name) # path
return out_brain
def save_file_list_function_in_mask(in_mask):
import numpy as np
import os
from os.path import join as opj
file_list2 = np.asarray(in_mask)
print('######################## File List ######################: \n',
file_list2)
np.save('mask_file_list', file_list2)
file_name2 = 'mask_file_list.npy'
out_mask = opj(os.getcwd(), file_name2) # path
return out_mask
def save_file_list_function_in_motion_params(in_motion_params):
import numpy as np
import os
from os.path import join as opj
file_list3 = np.asarray(in_motion_params)
print('######################## File List ######################: \n',
file_list3)
np.save('motion_params_file_list', file_list3)
file_name3 = 'motion_params_file_list.npy'
out_motion_params = opj(os.getcwd(), file_name3) # path
return out_motion_params
def save_file_list_function_in_motion_outliers(in_motion_outliers):
import numpy as np
import os
from os.path import join as opj
file_list4 = np.asarray(in_motion_outliers)
print('######################## File List ######################: \n',
file_list4)
np.save('motion_outliers_file_list', file_list4)
file_name4 = 'motion_outliers_file_list.npy'
out_motion_outliers = opj(os.getcwd(), file_name4) # path
return out_motion_outliers
def save_file_list_function_in_joint_xformation_matrix(
in_joint_xformation_matrix):
import numpy as np
import os
from os.path import join as opj
file_list5 = np.asarray(in_joint_xformation_matrix)
print('######################## File List ######################: \n',
file_list5)
np.save('joint_xformation_matrix_file_list', file_list5)
file_name5 = 'joint_xformation_matrix_file_list.npy'
out_joint_xformation_matrix = opj(os.getcwd(), file_name5) # path
return out_joint_xformation_matrix
def save_file_list_function_in_tr(in_tr):
import numpy as np
import os
from os.path import join as opj
tr_list = np.asarray(in_tr)
print('######################## TR List ######################: \n',
tr_list)
np.save('tr_list', tr_list)
file_name6 = 'tr_list.npy'
out_tr = opj(os.getcwd(), file_name6) # path
return out_tr
def save_file_list_function_in_atlas(in_atlas):
import numpy as np
import os
from os.path import join as opj
file_list7 = np.asarray(in_atlas)
print('######################## File List ######################: \n',
file_list7)
np.save('atlas_file_list', file_list7)
file_name7 = 'atlas_file_list.npy'
out_atlas = opj(os.getcwd(), file_name7) # path
return out_atlas
save_file_list_in_brain = JoinNode(Function(
function=save_file_list_function_in_brain,
input_names=['in_brain'],
output_names=['out_brain']),
joinsource="infosource",
joinfield=['in_brain'],
name="save_file_list_in_brain")
save_file_list_in_mask = JoinNode(Function(
function=save_file_list_function_in_mask,
input_names=['in_mask'],
output_names=['out_mask']),
joinsource="infosource",
joinfield=['in_mask'],
name="save_file_list_in_mask")
save_file_list_in_motion_outliers = JoinNode(
Function(function=save_file_list_function_in_motion_outliers,
input_names=['in_motion_outliers'],
output_names=['out_motion_outliers']),
joinsource="infosource",
joinfield=['in_motion_outliers'],
name="save_file_list_in_motion_outliers")
save_file_list_in_motion_params = JoinNode(
Function(function=save_file_list_function_in_motion_params,
input_names=['in_motion_params'],
output_names=['out_motion_params']),
joinsource="infosource",
joinfield=['in_motion_params'],
name="save_file_list_in_motion_params")
save_file_list_in_joint_xformation_matrix = JoinNode(
Function(function=save_file_list_function_in_joint_xformation_matrix,
input_names=['in_joint_xformation_matrix'],
output_names=['out_joint_xformation_matrix']),
joinsource="infosource",
joinfield=['in_joint_xformation_matrix'],
name="save_file_list_in_joint_xformation_matrix")
save_file_list_in_tr = JoinNode(Function(
function=save_file_list_function_in_tr,
input_names=['in_tr'],
output_names=['out_tr']),
joinsource="infosource",
joinfield=['in_tr'],
name="save_file_list_in_tr")
save_file_list_in_atlas = JoinNode(Function(
function=save_file_list_function_in_atlas,
input_names=['in_atlas'],
output_names=['out_atlas']),
joinsource="infosource",
joinfield=['in_atlas'],
name="save_file_list_in_atlas")
# save_file_list = JoinNode(Function(function=save_file_list_function, input_names=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix', 'in_tr', 'in_atlas'],
# output_names=['out_brain','out_mask','out_motion_params','out_motion_outliers','out_joint_xformation_matrix','out_tr', 'out_atlas']),
# joinsource="infosource",
# joinfield=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix','in_tr', 'in_atlas'],
# name="save_file_list")
# def save_file_list_function(in_brain, in_mask, in_motion_params, in_motion_outliers, in_joint_xformation_matrix, in_tr, in_atlas):
# # Imports
# import numpy as np
# import os
# from os.path import join as opj
#
#
# file_list = np.asarray(in_brain)
# print('######################## File List ######################: \n',file_list)
#
# np.save('brain_file_list',file_list)
# file_name = 'brain_file_list.npy'
# out_brain = opj(os.getcwd(),file_name) # path
#
#
# file_list2 = np.asarray(in_mask)
# print('######################## File List ######################: \n',file_list2)
#
# np.save('mask_file_list',file_list2)
# file_name2 = 'mask_file_list.npy'
# out_mask = opj(os.getcwd(),file_name2) # path
#
#
# file_list3 = np.asarray(in_motion_params)
# print('######################## File List ######################: \n',file_list3)
#
# np.save('motion_params_file_list',file_list3)
# file_name3 = 'motion_params_file_list.npy'
# out_motion_params = opj(os.getcwd(),file_name3) # path
#
#
# file_list4 = np.asarray(in_motion_outliers)
# print('######################## File List ######################: \n',file_list4)
#
# np.save('motion_outliers_file_list',file_list4)
# file_name4 = 'motion_outliers_file_list.npy'
# out_motion_outliers = opj(os.getcwd(),file_name4) # path
#
#
# file_list5 = np.asarray(in_joint_xformation_matrix)
# print('######################## File List ######################: \n',file_list5)
#
# np.save('joint_xformation_matrix_file_list',file_list5)
# file_name5 = 'joint_xformation_matrix_file_list.npy'
# out_joint_xformation_matrix = opj(os.getcwd(),file_name5) # path
#
# tr_list = np.asarray(in_tr)
# print('######################## TR List ######################: \n',tr_list)
#
# np.save('tr_list',tr_list)
# file_name6 = 'tr_list.npy'
# out_tr = opj(os.getcwd(),file_name6) # path
#
#
# file_list7 = np.asarray(in_atlas)
# print('######################## File List ######################: \n',file_list7)
#
# np.save('atlas_file_list',file_list7)
# file_name7 = 'atlas_file_list.npy'
# out_atlas = opj(os.getcwd(),file_name7) # path
#
#
#
#
# return out_brain, out_mask, out_motion_params, out_motion_outliers, out_joint_xformation_matrix, out_tr , out_atlas
#
#
#
# save_file_list = JoinNode(Function(function=save_file_list_function, input_names=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix', 'in_tr', 'in_atlas'],
# output_names=['out_brain','out_mask','out_motion_params','out_motion_outliers','out_joint_xformation_matrix','out_tr', 'out_atlas']),
# joinsource="infosource",
# joinfield=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix','in_tr', 'in_atlas'],
# name="save_file_list")
# ### Motion outliers
motionOutliers = Node(MotionOutliers(no_motion_correction=False,
metric='fd',
out_metric_plot='fd_plot.png',
out_metric_values='fd_raw.txt'),
name='motionOutliers')
# ## Workflow for atlas registration from std to functional
wf_atlas_resize_reg = Workflow(name=atlas_resize_reg_directory)
wf_atlas_resize_reg.connect([
# Apply the inverse matrix to the 3mm Atlas to transform it to func space
(maskfunc4mean, std2func_xform, [(('out_file', 'reference'))]),
(resample_atlas, std2func_xform, [('out_file', 'in_file')]),
# Now, applying the inverse matrix
(inv_mat, std2func_xform, [('out_file', 'in_matrix_file')]
), # output: Atlas in func space
(std2func_xform, save_file_list_in_atlas, [('out_file', 'in_atlas')]),
# ---------------------------Save the required files --------------------------------------------
(save_file_list_in_motion_params, dataSink,
[('out_motion_params', 'motion_params_paths.@out_motion_params')]),
(save_file_list_in_motion_outliers, dataSink,
[('out_motion_outliers', 'motion_outliers_paths.@out_motion_outliers')
]),
(save_file_list_in_brain, dataSink,
[('out_brain', 'preprocessed_brain_paths.@out_brain')]),
(save_file_list_in_mask, dataSink,
[('out_mask', 'preprocessed_mask_paths.@out_mask')]),
(save_file_list_in_joint_xformation_matrix, dataSink,
[('out_joint_xformation_matrix',
'joint_xformation_matrix_paths.@out_joint_xformation_matrix')]),
(save_file_list_in_tr, dataSink, [('out_tr', 'tr_paths.@out_tr')]),
(save_file_list_in_atlas, dataSink, [('out_atlas',
'atlas_paths.@out_atlas')])
])
# In[909]:
wf_coreg_reg = Workflow(name=coreg_reg_directory)
# wf_coreg_reg.base_dir = base_directory
# Dir where all the outputs will be stored(inside coregistrationPipeline folder).
if ANAT == 1:
wf_coreg_reg.connect(BIDSDataGrabber, 'anat_file_path', skullStrip,
'in_file') # Resampled the anat file to 3mm
wf_coreg_reg.connect(skullStrip, 'out_file', resample_anat, 'in_file')
wf_coreg_reg.connect(
resample_anat, 'out_file', func2anat_reg, 'reference'
) # Make the resampled file as reference in func2anat_reg
# Sec 1. The above 3 steps registers the mean image to resampled anat image and
# calculates the xformation matrix .. I hope the xformation matrix will be saved
wf_coreg_reg.connect(MNI152_2mm, 'standard_file', resample_mni,
'in_file')
wf_coreg_reg.connect(resample_mni, 'out_file', anat2std_reg,
'reference')
wf_coreg_reg.connect(resample_anat, 'out_file', anat2std_reg,
'in_file')
# Calculates the Xformationmatrix from anat3mm to MNI 3mm
# We can get those matrices by refering to func2anat_reg.outputs.out_matrix_file and similarly for anat2std_reg
wf_coreg_reg.connect(func2anat_reg, 'out_matrix_file', concat_xform,
'in_file')
wf_coreg_reg.connect(anat2std_reg, 'out_matrix_file', concat_xform,
'in_file2')
wf_coreg_reg.connect(concat_xform, 'out_file', dataSink,
'tranformation_matrix_fun2std.@out_file')
wf_coreg_reg.connect(concat_xform, 'out_file',
save_file_list_in_joint_xformation_matrix,
'in_joint_xformation_matrix')
# Now inverse the func2std MAT to std2func
wf_coreg_reg.connect(concat_xform, 'out_file', wf_atlas_resize_reg,
'inv_mat.in_file')
# ------------------------------------------------------------------------------------------------------------------------------
# Registration of Functional to MNI 3mm space w/o using anatomical
if ANAT == 0:
print('Not using Anatomical high resoulution files')
wf_coreg_reg.connect(MNI152_2mm, 'standard_file', resample_mni,
'in_file')
wf_coreg_reg.connect(
resample_mni, 'out_file', func2anat_reg, 'reference'
) # Make the resampled file as reference in func2anat_reg
wf_coreg_reg.connect(func2anat_reg, 'out_matrix_file', dataSink,
'tranformation_matrix_fun2std.@out_file')
wf_coreg_reg.connect(func2anat_reg, 'out_matrix_file',
save_file_list_in_joint_xformation_matrix,
'in_joint_xformation_matrix')
# Now inverse the func2std MAT to std2func
wf_coreg_reg.connect(func2anat_reg, 'out_matrix_file',
wf_atlas_resize_reg, 'inv_mat.in_file')
# ## Co-Registration, Normalization and Bandpass Workflow
# 1. Co-registration means alligning the func to anat
# 2. Normalization means aligning func/anat to standard
# 3. Applied band pass filtering in range - highpass=0.008, lowpass=0.08
# In[910]:
wf_motion_correction_bet = Workflow(name=motion_correction_bet_directory)
# wf_motion_correction_bet.base_dir = base_directory
wf_motion_correction_bet.connect([
(from_mcflirt, meanfunc, [('in_file', 'in_file')]),
(meanfunc, meanfuncmask, [('out_file', 'in_file')]),
(from_mcflirt, applyMask, [('in_file', 'in_file')]), # 1
(meanfuncmask, applyMask, [
('mask_file', 'in_file2')
]), # 2 output: 1&2, BET on coregistered fmri scan
(meanfunc, maskfunc4mean, [('out_file', 'in_file')]), # 3
(meanfuncmask, maskfunc4mean,
[('mask_file', 'in_file2')]), # 4 output: 3&4, BET on mean func scan
(applyMask, save_file_list_in_brain, [('out_file', 'in_brain')]),
(applyMask, save_file_list_in_mask, [('out_file2', 'in_mask')]),
(maskfunc4mean, wf_coreg_reg, [('out_file', 'func2anat_reg.in_file')])
])
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list)]
# Create the workflow
wf = Workflow(name=parent_wf_directory)
# base_dir = opj(s,'result')
wf.base_dir = base_directory # Dir where all the outputs will be stored(inside BETFlow folder).
# wf.connect([ (infosource, BIDSDataGrabber, [('subject_id','subject_id')]),
# (BIDSDataGrabber, extract, [('func_file_path','in_file')]),
#
# (BIDSDataGrabber,getMetadata, [('func_file_path','in_file')]),
#
# (getMetadata,slicetimer, [('tr','time_repetition')]),
#
#
# (getMetadata,slicetimer, [('index_dir','index_dir')]),
#
# (getMetadata,slicetimer, [('interleaved','interleaved')]),
#
# (getMetadata,save_file_list_in_tr, [('tr','in_tr')]),
#
# (extract,slicetimer,[('roi_file','in_file')]),
#
# (slicetimer, mcflirt,[('slice_time_corrected_file','in_file')])
# (mcflirt,dataSink,[('par_file','motion_params.@par_file')]), # saves the motion parameters calculated before
#
# (mcflirt,save_file_list_in_motion_params,[('par_file','in_motion_params')]),
#
# (mcflirt,wf_motion_correction_bet,[('out_file','from_mcflirt.in_file')])
# ])
# # Run it in parallel
# wf.run('MultiProc', plugin_args={'n_procs': num_proc})
#
#
#
# # Visualize the detailed graph
# # from IPython.display import Image
# wf.write_graph(graph2use='flat', format='png', simple_form=True)
# Options:
# discard 4 Volumes (extract), slicetimer, mcflirt
print('Preprocessing Options:')
print('Skipping 4 dummy volumes - ', options_binary_string[0])
print('Slicetiming correction - ', options_binary_string[1])
print('Finding Motion Outliers - ', options_binary_string[2])
print('Doing Motion Correction - ', options_binary_string[3])
# ANAT = 0
nodes = [extract, slicetimer, motionOutliers, mcflirt]
wf.connect(infosource, 'subject_id', BIDSDataGrabber, 'subject_id')
wf.connect(BIDSDataGrabber, 'func_file_path', getMetadata, 'in_file')
wf.connect(getMetadata, 'tr', save_file_list_in_tr, 'in_tr')
old_node = BIDSDataGrabber
old_node_output = 'func_file_path'
for idx, include in enumerate(options_binary_string):
if old_node == extract:
old_node_output = 'roi_file'
elif old_node == slicetimer:
old_node_output = 'slice_time_corrected_file'
# elif old_node == mcflirt:
# old_node_output = 'out_file'
if int(include):
new_node = nodes[idx]
if new_node == slicetimer:
wf.connect(getMetadata, 'tr', slicetimer, 'time_repetition')
wf.connect(getMetadata, 'index_dir', slicetimer, 'index_dir')
wf.connect(getMetadata, 'interleaved', slicetimer,
'interleaved')
new_node_input = 'in_file'
elif new_node == extract:
new_node_input = 'in_file'
elif new_node == mcflirt:
new_node_input = 'in_file'
wf.connect(mcflirt, 'par_file', dataSink,
'motion_params.@par_file'
) # saves the motion parameters calculated before
wf.connect(mcflirt, 'par_file',
save_file_list_in_motion_params, 'in_motion_params')
wf.connect(mcflirt, 'out_file', wf_motion_correction_bet,
'from_mcflirt.in_file')
elif new_node == motionOutliers:
wf.connect(meanfuncmask, 'mask_file', motionOutliers, 'mask')
wf.connect(motionOutliers, 'out_file', dataSink,
'motionOutliers.@out_file')
wf.connect(motionOutliers, 'out_metric_plot', dataSink,
'motionOutliers.@out_metric_plot')
wf.connect(motionOutliers, 'out_metric_values', dataSink,
'motionOutliers.@out_metric_values')
wf.connect(motionOutliers, 'out_file',
save_file_list_in_motion_outliers,
'in_motion_outliers')
new_node_input = 'in_file'
wf.connect(old_node, old_node_output, new_node, new_node_input)
continue
wf.connect(old_node, old_node_output, new_node, new_node_input)
old_node = new_node
else:
if idx == 3:
# new_node = from_mcflirt
# new_node_input = 'from_mcflirt.in_file'
wf.connect(old_node, old_node_output, wf_motion_correction_bet,
'from_mcflirt.in_file')
# old_node = new_node
TEMP_DIR_FOR_STORAGE = opj(base_directory, 'crash_files')
wf.config = {"execution": {"crashdump_dir": TEMP_DIR_FOR_STORAGE}}
# Visualize the detailed graph
# from IPython.display import Image
wf.write_graph(graph2use='flat', format='png', simple_form=True)
# Run it in parallel
wf.run('MultiProc', plugin_args={'n_procs': num_proc})
def intensity_norm(wf_name='intensity_norm'):
""" Workflow that uses a mask against a source from where the mean value will be taken.
This mean value will be used to demean the whole source and leave it in out_file.
Parameters
----------
wf_name: str
The name of the workflow.
Nipype Inputs
-------------
intnorm_input.source: existing file
The image from where to extract the signal values and normalize.
intnorm_input.mask: existing file
The mask to specify which voxels to use to calculate the statistics
for normalization.
Nipype Outputs
--------------
intnorm_output.out_file: existing file
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["source", "mask"]
out_fields = ["out_file"]
# input
intnorm_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="intnorm_input")
# fix the affine matrix (it's necessary for some cases)
resample = setup_node(Function(
function=resample_to_img,
input_names=["in_file", "target", "interpolation"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name="resample_mask")
resample.inputs.interpolation = "nearest"
# calculate masked mean value
mean_val = setup_node(Function(
function=math_img,
input_names=["formula", "img", "mask"],
output_names=["out_value"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='mean_value')
mean_val.inputs.formula = "np.mean(np.nonzero(img[mask > 0]))"
# normalize
norm_img = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "img", "val"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='norm_img')
norm_img.inputs.formula = "img / val"
# output
intnorm_output = setup_node(IdentityInterface(fields=out_fields),
name="intnorm_output")
# Create the workflow object
wf = Workflow(name=wf_name)
wf.connect([
# resample
(intnorm_input, resample, [("source", "target"), ("mask", "in_file")]),
# normalize
(intnorm_input, mean_val, [("source", "img")]),
(resample, mean_val, [("out_file", "mask")]),
(intnorm_input, norm_img, [
("source", "img"),
(("source", rename, "_intnormed"), "out_file"),
]),
(mean_val, norm_img, [("out_value", "val")]),
(norm_img, intnorm_output, [("out_file", "out_file")]),
])
return wf
def _main(subject_list,vols,subid_vol_dict, number_of_skipped_volumes,brain_path,\
mask_path,\
atlas_path,\
tr_path,\
motion_params_path,\
func2std_mat_path,\
MNI3mm_path,\
base_directory,\
fc_datasink_name,\
motion_param_regression,\
band_pass_filtering,\
global_signal_regression,\
smoothing,\
volcorrect,\
num_proc,\
functional_connectivity_directory ):
# ## Volume correction
# * I have already extracted 4 volumes.
# * Now extract 120 - 4 = 116 volumes from each subject
# * So define vols = 114
#
if number_of_skipped_volumes == None:
number_of_skipped_volumes = 4
vols = vols - number_of_skipped_volumes
def vol_correct(sub_id, subid_vol_dict, vols, number_of_skipped_volumes):
sub_vols = subid_vol_dict[sub_id] - number_of_skipped_volumes
if sub_vols > vols:
t_min = sub_vols - vols
elif sub_vols == vols:
t_min = 0
else:
raise Exception('Volumes of Sub ',sub_id,' less than desired!')
return int(t_min)
# In[491]:
volCorrect = Node(Function(function=vol_correct, input_names=['sub_id','subid_vol_dict','vols','number_of_skipped_volumes'],
output_names=['t_min']), name='volCorrect')
volCorrect.inputs.subid_vol_dict = subid_vol_dict
volCorrect.inputs.vols = vols
volCorrect.inputs.number_of_skipped_volumes = number_of_skipped_volumes
# ## Define a function to fetch the filenames of a particular subject ID
def get_subject_filenames(subject_id,brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path,MNI3mm_path):
import re
from itertools import zip_longest
for brain,mask,atlas,tr,motion_param,func2std_mat in zip_longest(brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path): #itertools helps to zip unequal save_file_list_in_mask
# Source : https://stackoverflow.com/questions/11318977/zipping-unequal-lists-in-python-in-to-a-list-which-does-not-drop-any-element-fro
print('*******************',brain,mask,atlas,tr,motion_param,func2std_mat)
sub_id_extracted = re.search('.+_subject_id_(\d+)', brain).group(1)
if str(subject_id) in brain:
# print("Files for subject ",subject_id,brain,mask,atlas,tr,motion_param)
return brain,mask,atlas,tr,motion_param,func2std_mat,MNI3mm_path
print ('Unable to locate Subject: ',subject_id,'extracted: ',sub_id_extracted)
# print ('Unable to locate Subject: ',subject_id)
raise Exception('Unable to locate Subject: ',subject_id,'extracted: ',sub_id_extracted)
# raise Exception('Unable to locate Subject: ',subject_id)
return 0
# Make a node
getSubjectFilenames = Node(Function(function=get_subject_filenames, input_names=['subject_id','brain_path','mask_path','atlas_path','tr_path','motion_params_path','func2std_mat_path','MNI3mm_path'],
output_names=['brain','mask','atlas','tr','motion_param','func2std_mat', 'MNI3mm_path']), name='getSubjectFilenames')
getSubjectFilenames.inputs.brain_path = brain_path
getSubjectFilenames.inputs.mask_path = mask_path
getSubjectFilenames.inputs.atlas_path = atlas_path
getSubjectFilenames.inputs.tr_path = tr_path
getSubjectFilenames.inputs.motion_params_path = motion_params_path
getSubjectFilenames.inputs.func2std_mat_path = func2std_mat_path
getSubjectFilenames.inputs.MNI3mm_path = MNI3mm_path
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id',subject_list)]
# ## Band Pass Filtering
# Let's do a band pass filtering on the data using the
# code from https://neurostars.org/t/bandpass-filtering-different-outputs-from-fsl-and-nipype-custom-function/824/2
### AFNI
bandpass = Node(afni.Bandpass(highpass=0.01, lowpass=0.1,
despike=False, no_detrend=True, notrans=True,
outputtype='NIFTI_GZ'),name='bandpass')
# bandpass = Node(afni.Bandpass(highpass=0.001, lowpass=0.01,
# despike=False, no_detrend=True, notrans=True,
# tr=2.0,outputtype='NIFTI_GZ'),name='bandpass')
# ## Highpass filtering
# In[506]:
"""
Perform temporal highpass filtering on the data
"""
# https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBandpass.html
# os.chdir('/home1/varunk/Autism-Connectome-Analysis-bids-related/')
highpass = Node(afni.Bandpass(highpass=0.009, lowpass=99999,
despike=False, no_detrend=True, notrans=True,
outputtype='NIFTI_GZ'),name='highpass')
# FSL bandpass/Highpass
# highpass = Node(interface=ImageMaths(suffix='_tempfilt'),
# iterfield=['in_file'],
# name='highpass')
#
# highpass.inputs.op_string = '-bptf 27.77775001525879 -1' # 23.64 # 31.25
# ## Smoothing
# ### Using 6mm fwhm
# sigma = 6/2.3548 = 2.547987090198743
spatialSmooth = Node(interface=ImageMaths(op_string='-s 2.5479',
suffix='_smoothed'),
name='spatialSmooth')
# ## Performs Gram Schmidt Process
# https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process
# In[509]:
def orthogonalize(in_file, mask_file):
import numpy as np
import nibabel as nib
import os
from os.path import join as opj
def gram_schmidt(voxel_time_series, mean_vector):
numerator = np.dot(voxel_time_series,mean_vector)
dinominator = np.dot(mean_vector,mean_vector)
voxel_time_series_orthogonalized = voxel_time_series - (numerator/dinominator)*mean_vector
# TO CONFIRM IF THE VECTORS ARE ORTHOGONAL
# sum_dot_prod = np.sum(np.dot(voxel_time_series_orthogonalized,mean_vector))
# print('Sum of entries of orthogonalized vector = ',sum_dot_prod)
return voxel_time_series_orthogonalized
mask_data = nib.load(mask_file)
mask = mask_data.get_data()
brain_data = nib.load(in_file)
brain = brain_data.get_data()
x_dim, y_dim, z_dim, t_dim = brain_data.shape
# Find mean brain
mean_vector = np.zeros(t_dim)
num_brain_voxels = 0
# Count the number of brain voxels
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
mean_vector = mean_vector + brain[i,j,k,:]
num_brain_voxels = num_brain_voxels + 1
mean_vector = mean_vector / num_brain_voxels
# Orthogonalize
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
brain[i,j,k,:] = gram_schmidt(brain[i,j,k,:], mean_vector)
sub_id = in_file.split('/')[-1].split('.')[0].split('_')[0].split('-')[1]
gsr_file_name = 'sub-' + sub_id + '_task-rest_run-1_bold.nii.gz'
# gsr_file_name_nii = gsr_file_name + '.nii.gz'
out_file = opj(os.getcwd(),gsr_file_name) # path
brain_with_header = nib.Nifti1Image(brain, affine=brain_data.affine,header = brain_data.header)
nib.save(brain_with_header,gsr_file_name)
return out_file
# In[510]:
globalSignalRemoval = Node(Function(function=orthogonalize, input_names=['in_file','mask_file'],
output_names=['out_file']), name='globalSignalRemoval' )
# globalSignalRemoval.inputs.mask_file = mask_file
# globalSignalRemoval.iterables = [('in_file',file_paths)]
# ## GLM for regression of motion parameters
# In[511]:
def calc_residuals(in_file,
motion_file):
"""
Calculates residuals of nuisance regressors -motion parameters for every voxel for a subject using GLM.
Parameters
----------
in_file : string
Path of a subject's motion corrected nifti file.
motion_par_file : string
path of a subject's motion parameters
Returns
-------
out_file : string
Path of residual file in nifti format
"""
import nibabel as nb
import numpy as np
import os
from os.path import join as opj
nii = nb.load(in_file)
data = nii.get_data().astype(np.float32)
global_mask = (data != 0).sum(-1) != 0
# Check and define regressors which are provided from files
if motion_file is not None:
motion = np.genfromtxt(motion_file)
if motion.shape[0] != data.shape[3]:
raise ValueError('Motion parameters {0} do not match data '
'timepoints {1}'.format(motion.shape[0],
data.shape[3]))
if motion.size == 0:
raise ValueError('Motion signal file {0} is '
'empty'.format(motion_file))
# Calculate regressors
regressor_map = {'constant' : np.ones((data.shape[3],1))}
regressor_map['motion'] = motion
X = np.zeros((data.shape[3], 1))
for rname, rval in regressor_map.items():
X = np.hstack((X, rval.reshape(rval.shape[0],-1)))
X = X[:,1:]
if np.isnan(X).any() or np.isnan(X).any():
raise ValueError('Regressor file contains NaN')
Y = data[global_mask].T
try:
B = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)
except np.linalg.LinAlgError as e:
if "Singular matrix" in e:
raise Exception("Error details: {0}\n\nSingular matrix error: "
"The nuisance regression configuration you "
"selected may have been too stringent, and the "
"regression could not be completed. Ensure your "
"parameters are not too "
"extreme.\n\n".format(e))
else:
raise Exception("Error details: {0}\n\nSomething went wrong with "
"nuisance regression.\n\n".format(e))
Y_res = Y - X.dot(B)
data[global_mask] = Y_res.T
img = nb.Nifti1Image(data, header=nii.get_header(),
affine=nii.get_affine())
subject_name = in_file.split('/')[-1].split('.')[0]
filename = subject_name + '_residual.nii.gz'
out_file = os.path.join(os.getcwd(),filename )
img.to_filename(out_file) # alt to nib.save
return out_file
# In[512]:
# Create a Node for above
calc_residuals = Node(Function(function=calc_residuals, input_names=['in_file','motion_file'],
output_names=['out_file']), name='calc_residuals')
# ## Datasink
# I needed to define the structure of what files are saved and where.
# In[513]:
# Create DataSink object
dataSink = Node(DataSink(), name='datasink')
# Name of the output folder
dataSink.inputs.base_directory = opj(base_directory,fc_datasink_name)
# To create the substitutions I looked the `datasink` folder where I was redirecting the output. I manually selected the part of file/folder name that I wanted to change and copied below to be substituted.
#
# In[514]:
# Define substitution strings so that the data is similar to BIDS
substitutions = [('_subject_id_', 'sub-')]
# Feed the substitution strings to the DataSink node
dataSink.inputs.substitutions = substitutions
# ### Following is a Join Node that collects the preprocessed file paths and saves them in a file
# In[516]:
def save_file_list_function(in_fc_map_brain_file):
# Imports
import numpy as np
import os
from os.path import join as opj
file_list = np.asarray(in_fc_map_brain_file)
print('######################## File List ######################: \n',file_list)
np.save('fc_map_brain_file_list',file_list)
file_name = 'fc_map_brain_file_list.npy'
out_fc_map_brain_file = opj(os.getcwd(),file_name) # path
return out_fc_map_brain_file
# In[517]:
save_file_list = JoinNode(Function(function=save_file_list_function, input_names=['in_fc_map_brain_file'],
output_names=['out_fc_map_brain_file']),
joinsource="infosource",
joinfield=['in_fc_map_brain_file'],
name="save_file_list")
# ## Create a FC node
#
# This node:
# 1. Exracts the average time series of the brain ROI's using the atlas and stores
# it as a matrix of size [ROIs x Volumes].
# 2. Extracts the Voxel time series and stores it in matrix of size [Voxels x Volumes]
#
# And save FC matrix files in shape of brains
def pear_coff(in_file, atlas_file, mask_file):
# code to find how many voxels are in the brain region using the mask
# imports
import numpy as np
import nibabel as nib
import os
from os.path import join as opj
mask_data = nib.load(mask_file)
mask = mask_data.get_data()
x_dim, y_dim, z_dim = mask_data.shape
atlasPath = atlas_file
# Read the atlas
atlasObject = nib.load(atlasPath)
atlas = atlasObject.get_data()
num_ROIs = int((np.max(atlas) - np.min(atlas) ))
# Read the brain in_file
brain_data = nib.load(in_file)
brain = brain_data.get_data()
x_dim, y_dim, z_dim, num_volumes = brain.shape
num_brain_voxels = 0
x_dim, y_dim, z_dim = mask_data.shape
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
num_brain_voxels = num_brain_voxels + 1
# Initialize a matrix of ROI time series and voxel time series
ROI_matrix = np.zeros((num_ROIs, num_volumes))
voxel_matrix = np.zeros((num_brain_voxels, num_volumes))
# Fill up the voxel_matrix
voxel_counter = 0
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
voxel_matrix[voxel_counter,:] = brain[i,j,k,:]
voxel_counter = voxel_counter + 1
# Fill up the ROI_matrix
# Keep track of number of voxels per ROI as well by using an array - num_voxels_in_ROI[]
num_voxels_in_ROI = np.zeros((num_ROIs,1)) # A column arrray containing number of voxels in each ROI
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
label = int(atlas[i,j,k]) - 1
if label != -1:
ROI_matrix[label,:] = np.add(ROI_matrix[label,:], brain[i,j,k,:])
num_voxels_in_ROI[label,0] = num_voxels_in_ROI[label,0] + 1
ROI_matrix = np.divide(ROI_matrix,num_voxels_in_ROI) # Check if divide is working correctly
X, Y = ROI_matrix, voxel_matrix
# Subtract mean from X and Y
X = np.subtract(X, np.mean(X, axis=1, keepdims=True))
Y = np.subtract(Y, np.mean(Y, axis=1, keepdims=True))
temp1 = np.dot(X,Y.T)
temp2 = np.sqrt(np.sum(np.multiply(X,X), axis=1, keepdims=True))
temp3 = np.sqrt(np.sum(np.multiply(Y,Y), axis=1, keepdims=True))
temp4 = np.dot(temp2,temp3.T)
coff_matrix = np.divide(temp1, (temp4 + 1e-7))
# Check if any ROI is missing and replace the NAN values in coff_matrix by 0
if np.argwhere(np.isnan(coff_matrix)).shape[0] != 0:
print("Some ROIs are not present. Replacing NAN in coff matrix by 0")
np.nan_to_num(coff_matrix, copy=False)
# TODO: when I have added 1e-7 in the dinominator, then why did I feel the need to replace NAN by zeros
sub_id = in_file.split('/')[-1].split('.')[0].split('_')[0].split('-')[1]
fc_file_name = sub_id + '_fc_map'
print ("Pear Matrix calculated for subject: ",sub_id)
roi_brain_matrix = coff_matrix
brain_file = in_file
x_dim, y_dim, z_dim, t_dim = brain.shape
(brain_data.header).set_data_shape([x_dim,y_dim,z_dim,num_ROIs])
brain_roi_tensor = np.zeros((brain_data.header.get_data_shape()))
print("Creating brain for Subject-",sub_id)
for roi in range(num_ROIs):
brain_voxel_counter = 0
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
brain_roi_tensor[i,j,k,roi] = roi_brain_matrix[roi,brain_voxel_counter]
brain_voxel_counter = brain_voxel_counter + 1
assert (brain_voxel_counter == len(roi_brain_matrix[roi,:]))
print("Created brain for Subject-",sub_id)
path = os.getcwd()
fc_file_name = fc_file_name + '.nii.gz'
out_file = opj(path,fc_file_name)
brain_with_header = nib.Nifti1Image(brain_roi_tensor, affine=brain_data.affine,header = brain_data.header)
nib.save(brain_with_header,out_file)
fc_map_brain_file = out_file
return fc_map_brain_file
# In[521]:
# Again Create the Node and set default values to paths
pearcoff = Node(Function(function=pear_coff, input_names=['in_file','atlas_file','mask_file'],
output_names=['fc_map_brain_file']), name='pearcoff')
# # IMPORTANT:
# * The ROI 255 has been removed due to resampling. Therefore the FC maps will have nan at that row. So don't use that ROI :)
# * I came to know coz I keep getting this error: RuntimeWarning: invalid value encountered in true_divide
# * To debug it, I read the coff matrix and checked its diagnol to discover the nan value.
#
#
#
# ## Extract volumes
# ExtractROI - For volCorrect
extract = Node(ExtractROI(t_size=-1),
output_type='NIFTI',
name="extract")
# ### Node for applying xformation matrix to functional data
#
# In[523]:
func2std_xform = Node(FLIRT(output_type='NIFTI_GZ',
apply_xfm=True), name="func2std_xform")
# motion_param_regression = 1
# band_pass_filtering = 0
# global_signal_regression = 0
# smoothing = 1
# volcorrect = 1
if num_proc == None:
num_proc = 7
combination = 'motionRegress' + str(int(motion_param_regression)) + \
'global' + str(int(global_signal_regression)) + 'smoothing' + str(int(smoothing)) +\
'filt' + str(int(band_pass_filtering))
print("Combination: ",combination)
binary_string = str(int(motion_param_regression)) + str(int(global_signal_regression)) + \
str(int(smoothing)) + str(int(band_pass_filtering)) + str(int(volcorrect))
base_dir = opj(base_directory,functional_connectivity_directory)
# wf = Workflow(name=functional_connectivity_directory)
wf = Workflow(name=combination)
wf.base_dir = base_dir # Dir where all the outputs will be stored.
wf.connect(infosource ,'subject_id', getSubjectFilenames, 'subject_id')
# ------- Dynamic Pipeline ------------------------
nodes = [
calc_residuals,
globalSignalRemoval,
spatialSmooth,
bandpass,
volCorrect]
# from nipype.interfaces import fsl
old_node = getSubjectFilenames
old_node_output = 'brain'
binary_string = binary_string+'0' # so that the loop runs one more time
for idx, include in enumerate(binary_string):
# 11111
# motion_param_regression
# global_signal_regression
# smoothing
# band_pass_filtering
# volcorrect
if old_node == calc_residuals:
old_node_output = 'out_file'
elif old_node == extract :
old_node_output = 'roi_file'
elif old_node == globalSignalRemoval:
old_node_output = 'out_file'
elif old_node == bandpass:
old_node_output = 'out_file'
elif old_node == highpass:
old_node_output = 'out_file'
elif old_node == spatialSmooth:
old_node_output = 'out_file'
elif old_node == volCorrect:
old_node_output = 'out_file'
if int(include):
# if old_node is None:
#
# wf.add_nodes([nodes[idx]])
#
# else:
new_node = nodes[idx]
if new_node == calc_residuals:
wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
new_node_input = 'in_file'
elif new_node == extract :
wf.connect([( volCorrect, extract, [('t_min','t_min')])])
new_node_input = 'in_file'
elif new_node == globalSignalRemoval:
wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
new_node_input = 'in_file'
elif new_node == bandpass:
wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
new_node_input = 'in_file'
elif new_node == highpass:
wf.connect([(getSubjectFilenames, highpass, [('tr','tr')])]) #Commenting for FSL
new_node_input = 'in_file'
elif new_node == spatialSmooth:
new_node_input = 'in_file'
elif new_node == volCorrect:
wf.connect([(infosource, volCorrect, [('subject_id','sub_id')])])
wf.connect([( volCorrect, extract, [('t_min','t_min')])])
new_node = extract
new_node_input = 'in_file'
wf.connect(old_node, old_node_output, new_node, new_node_input)
old_node = new_node
else:
if idx == 3: # bandpas == 0 => Highpass
new_node = highpass
wf.connect([(getSubjectFilenames, highpass, [('tr','tr')])]) #Commenting for FSL
new_node_input = 'in_file'
wf.connect(old_node, old_node_output, new_node, new_node_input)
old_node = new_node
wf.connect(old_node, old_node_output, pearcoff, 'in_file')
wf.connect(getSubjectFilenames,'atlas', pearcoff, 'atlas_file')
wf.connect(getSubjectFilenames, 'mask', pearcoff, 'mask_file')
wf.connect(pearcoff, 'fc_map_brain_file', func2std_xform ,'in_file')
wf.connect(getSubjectFilenames,'func2std_mat', func2std_xform, 'in_matrix_file')
wf.connect(getSubjectFilenames, 'MNI3mm_path', func2std_xform,'reference')
folder_name = combination + '.@fc_map_brain_file'
wf.connect(func2std_xform, 'out_file', save_file_list, 'in_fc_map_brain_file')
wf.connect(save_file_list, 'out_fc_map_brain_file', dataSink,folder_name)
TEMP_DIR_FOR_STORAGE = opj(base_directory,'crash_files')
wf.config = {"execution": {"crashdump_dir": TEMP_DIR_FOR_STORAGE}}
wf.write_graph(graph2use='flat', format='png')
wf.run('MultiProc', plugin_args={'n_procs': num_proc})
def create_realign_reference(run, whichvol_glob, name="realignref"):
"""
run (int): run's number
whichvol_glob: 'first' or 'middle' or 'last' or
'mean', if 'mean' was chosed for run then whichvol_glob does't matter
"""
realignref = Workflow(name=name)
inputnode = Node(interface=util.IdentityInterface(fields=["in_sub", "in_hand"]), name="inputspec")
ds = Node(DataGrabber(infields=["subject_id", "hand"], outfields=["func"]), name="datasource")
ds.inputs.base_directory = opap(base_directory)
ds.inputs.template = "%s/%s_Hand/*.nii*"
ds.inputs.sort_filelist = True
# ds.inputs.subject_id = 'GK011RZJA'
# ds.inputs.hand = 'Left'
realignref.connect(inputnode, "in_hand", ds, "hand")
realignref.connect(inputnode, "in_sub", ds, "subject_id")
img2float = MapNode(
interface=fsl.ImageMaths(out_data_type="float", op_string="", suffix="_dtype"),
iterfield=["in_file"],
name="img2float",
)
realignref.connect(ds, ("func", pickrun), img2float, "in_file")
# realignref.connect(inputnode, 'in_files', img2float, 'in_file')
if whichvol_glob != "mean":
extract_ref = Node(interface=fsl.ExtractROI(t_size=1), iterfield=["in_file"], name="extractref")
realignref.connect(img2float, ("out_file", pickfirst), extract_ref, "in_file")
realignref.connect(img2float, ("out_file", pickvol, 0, whichvol_glob), extract_ref, "t_min")
outputnode = pe.Node(interface=util.IdentityInterface(fields=["ref_vol"]), name="outputnode")
if whichvol_glob != "mean":
realignref.connect(extract_ref, "roi_file", outputnode, "ref_vol")
else:
meanfunc = pe.Node(interface=fsl.ImageMaths(op_string="-Tmean", suffix="_mean"), name="meanfunc")
realignref.connect(img2float, ("out_file", pickfirst), meanfunc, "in_file")
realignref.connect(meanfunc, "out_file", outputnode, "ref_vol")
return realignref
from nipype.interfaces.utility import IdentityInterface
from nipype.pipeline import Workflow, Node
from nipype.interfaces.fsl.model import FEAT
import os.path as op
from glob import glob
from .nodes import ConcatenateIterables, Combine_events_and_confounds, Custom_Level1design_Feat
tasks = ['other', 'self_run-1', 'self_run-2']
base_dir = '/home/lsnoek1/SharedStates'
out_dir = op.join(base_dir, 'firstlevel')
sub_ids = sorted([
op.basename(f)
for f in glob(op.join(base_dir, 'preproc', 'fmriprep', 'sub-???'))
])
meta_wf = Workflow('firstlevel_spynoza')
concat_iterables_node = Node(
interface=ConcatenateIterables(fields=['sub_id', 'task']),
name='concat_iterables')
input_node = Node(IdentityInterface(fields=['sub_id', 'task']),
name='inputspec')
input_node.iterables = [('sub_id', sub_ids), ('task', tasks)]
meta_wf.connect(input_node, 'sub_id', concat_iterables_node, 'sub_id')
meta_wf.connect(input_node, 'task', concat_iterables_node, 'task')
templates = {
'func': '{sub_id}/func/{sub_id}_task-{task}*_preproc.nii.gz',
'func_mask': '{sub_id}/func/{sub_id}_task-{task}*_brainmask.nii.gz',
def create_workflow(self):
"""Create the Niype workflow of the super-resolution pipeline.
It is composed of a succession of Nodes and their corresponding parameters,
where the output of node i goes to the input of node i+1.
"""
sub_ses = self.subject
if self.session is not None:
sub_ses = ''.join([sub_ses, '_', self.session])
if self.session is None:
wf_base_dir = os.path.join(
self.output_dir, '-'.join(["nipype", __nipype_version__]),
self.subject, "rec-{}".format(self.sr_id))
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk",
__version__]), self.subject)
else:
wf_base_dir = os.path.join(
self.output_dir, '-'.join(["nipype", __nipype_version__]),
self.subject, self.session, "rec-{}".format(self.sr_id))
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk", __version__]),
self.subject, self.session)
if not os.path.exists(wf_base_dir):
os.makedirs(wf_base_dir)
print("Process directory: {}".format(wf_base_dir))
# Initialization (Not sure we can control the name of nipype log)
if os.path.isfile(os.path.join(wf_base_dir, "pypeline.log")):
os.unlink(os.path.join(wf_base_dir, "pypeline.log"))
self.wf = Workflow(name=self.pipeline_name, base_dir=wf_base_dir)
config.update_config({
'logging': {
'log_directory': os.path.join(wf_base_dir),
'log_to_file': True
},
'execution': {
'remove_unnecessary_outputs': False,
'stop_on_first_crash': True,
'stop_on_first_rerun': False,
'crashfile_format': "txt",
'use_relative_paths': True,
'write_provenance': False
}
})
# Update nypipe logging with config
nipype_logging.update_logging(config)
# config.enable_provenance()
if self.use_manual_masks:
dg = Node(interface=DataGrabber(outfields=['T2ws', 'masks']),
name='data_grabber')
dg.inputs.base_directory = self.bids_dir
dg.inputs.template = '*'
dg.inputs.raise_on_empty = False
dg.inputs.sort_filelist = True
if self.session is not None:
t2ws_template = os.path.join(
self.subject, self.session, 'anat',
'_'.join([sub_ses, '*run-*', '*T2w.nii.gz']))
if self.m_masks_desc is not None:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat', '_'.join([
sub_ses, '*_run-*', '_desc-' + self.m_masks_desc,
'*mask.nii.gz'
]))
else:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat',
'_'.join([sub_ses, '*run-*', '*mask.nii.gz']))
else:
t2ws_template = os.path.join(self.subject, 'anat',
sub_ses + '*_run-*_T2w.nii.gz')
if self.m_masks_desc is not None:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat', '_'.join([
sub_ses, '*_run-*', '_desc-' + self.m_masks_desc,
'*mask.nii.gz'
]))
else:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, 'anat', sub_ses + '*_run-*_*mask.nii.gz')
dg.inputs.field_template = dict(T2ws=t2ws_template,
masks=masks_template)
brainMask = MapNode(
interface=IdentityInterface(fields=['out_file']),
name='brain_masks_bypass',
iterfield=['out_file'])
if self.m_stacks is not None:
custom_masks_filter = Node(
interface=preprocess.FilteringByRunid(),
name='custom_masks_filter')
custom_masks_filter.inputs.stacks_id = self.m_stacks
else:
dg = Node(interface=DataGrabber(outfields=['T2ws']),
name='data_grabber')
dg.inputs.base_directory = self.bids_dir
dg.inputs.template = '*'
dg.inputs.raise_on_empty = False
dg.inputs.sort_filelist = True
dg.inputs.field_template = dict(
T2ws=os.path.join(self.subject, 'anat', sub_ses +
'*_run-*_T2w.nii.gz'))
if self.session is not None:
dg.inputs.field_template = dict(T2ws=os.path.join(
self.subject, self.session, 'anat', '_'.join(
[sub_ses, '*run-*', '*T2w.nii.gz'])))
if self.m_stacks is not None:
t2ws_filter_prior_masks = Node(
interface=preprocess.FilteringByRunid(),
name='t2ws_filter_prior_masks')
t2ws_filter_prior_masks.inputs.stacks_id = self.m_stacks
brainMask = MapNode(interface=preprocess.BrainExtraction(),
name='brainExtraction',
iterfield=['in_file'])
brainMask.inputs.bids_dir = self.bids_dir
brainMask.inputs.in_ckpt_loc = pkg_resources.resource_filename(
"pymialsrtk",
os.path.join("data", "Network_checkpoints",
"Network_checkpoints_localization",
"Unet.ckpt-88000.index")).split('.index')[0]
brainMask.inputs.threshold_loc = 0.49
brainMask.inputs.in_ckpt_seg = pkg_resources.resource_filename(
"pymialsrtk",
os.path.join("data", "Network_checkpoints",
"Network_checkpoints_segmentation",
"Unet.ckpt-20000.index")).split('.index')[0]
brainMask.inputs.threshold_seg = 0.5
t2ws_filtered = Node(interface=preprocess.FilteringByRunid(),
name='t2ws_filtered')
masks_filtered = Node(interface=preprocess.FilteringByRunid(),
name='masks_filtered')
if not self.m_skip_stacks_ordering:
stacksOrdering = Node(interface=preprocess.StacksOrdering(),
name='stackOrdering')
else:
stacksOrdering = Node(
interface=IdentityInterface(fields=['stacks_order']),
name='stackOrdering')
stacksOrdering.inputs.stacks_order = self.m_stacks
if not self.m_skip_nlm_denoising:
nlmDenoise = MapNode(interface=preprocess.BtkNLMDenoising(),
name='nlmDenoise',
iterfield=['in_file', 'in_mask'])
nlmDenoise.inputs.bids_dir = self.bids_dir
# Sans le mask le premier correct slice intensity...
srtkCorrectSliceIntensity01_nlm = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity01_nlm',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity01_nlm.inputs.bids_dir = self.bids_dir
srtkCorrectSliceIntensity01_nlm.inputs.out_postfix = '_uni'
srtkCorrectSliceIntensity01 = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity01',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity01.inputs.bids_dir = self.bids_dir
srtkCorrectSliceIntensity01.inputs.out_postfix = '_uni'
srtkSliceBySliceN4BiasFieldCorrection = MapNode(
interface=preprocess.MialsrtkSliceBySliceN4BiasFieldCorrection(),
name='srtkSliceBySliceN4BiasFieldCorrection',
iterfield=['in_file', 'in_mask'])
srtkSliceBySliceN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
srtkSliceBySliceCorrectBiasField = MapNode(
interface=preprocess.MialsrtkSliceBySliceCorrectBiasField(),
name='srtkSliceBySliceCorrectBiasField',
iterfield=['in_file', 'in_mask', 'in_field'])
srtkSliceBySliceCorrectBiasField.inputs.bids_dir = self.bids_dir
# 4-modules sequence to be defined as a stage.
if not self.m_skip_nlm_denoising:
srtkCorrectSliceIntensity02_nlm = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity02_nlm',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity02_nlm.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization01_nlm = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization01_nlm')
srtkIntensityStandardization01_nlm.inputs.bids_dir = self.bids_dir
srtkHistogramNormalization_nlm = Node(
interface=preprocess.MialsrtkHistogramNormalization(),
name='srtkHistogramNormalization_nlm')
srtkHistogramNormalization_nlm.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization02_nlm = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization02_nlm')
srtkIntensityStandardization02_nlm.inputs.bids_dir = self.bids_dir
# 4-modules sequence to be defined as a stage.
srtkCorrectSliceIntensity02 = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity02',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity02.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization01 = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization01')
srtkIntensityStandardization01.inputs.bids_dir = self.bids_dir
srtkHistogramNormalization = Node(
interface=preprocess.MialsrtkHistogramNormalization(),
name='srtkHistogramNormalization')
srtkHistogramNormalization.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization02 = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization02')
srtkIntensityStandardization02.inputs.bids_dir = self.bids_dir
srtkMaskImage01 = MapNode(interface=preprocess.MialsrtkMaskImage(),
name='srtkMaskImage01',
iterfield=['in_file', 'in_mask'])
srtkMaskImage01.inputs.bids_dir = self.bids_dir
srtkImageReconstruction = Node(
interface=reconstruction.MialsrtkImageReconstruction(),
name='srtkImageReconstruction')
srtkImageReconstruction.inputs.bids_dir = self.bids_dir
srtkImageReconstruction.inputs.sub_ses = sub_ses
srtkImageReconstruction.inputs.no_reg = self.m_skip_svr
srtkTVSuperResolution = Node(
interface=reconstruction.MialsrtkTVSuperResolution(),
name='srtkTVSuperResolution')
srtkTVSuperResolution.inputs.bids_dir = self.bids_dir
srtkTVSuperResolution.inputs.sub_ses = sub_ses
srtkTVSuperResolution.inputs.in_loop = self.primal_dual_loops
srtkTVSuperResolution.inputs.in_deltat = self.deltatTV
srtkTVSuperResolution.inputs.in_lambda = self.lambdaTV
srtkTVSuperResolution.inputs.use_manual_masks = self.use_manual_masks
srtkN4BiasFieldCorrection = Node(
interface=postprocess.MialsrtkN4BiasFieldCorrection(),
name='srtkN4BiasFieldCorrection')
srtkN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
if self.m_do_refine_hr_mask:
srtkHRMask = Node(
interface=postprocess.MialsrtkRefineHRMaskByIntersection(),
name='srtkHRMask')
srtkHRMask.inputs.bids_dir = self.bids_dir
else:
srtkHRMask = Node(interface=postprocess.BinarizeImage(),
name='srtkHRMask')
srtkMaskImage02 = Node(interface=preprocess.MialsrtkMaskImage(),
name='srtkMaskImage02')
srtkMaskImage02.inputs.bids_dir = self.bids_dir
# Build workflow : connections of the nodes
# Nodes ready : Linking now
if self.use_manual_masks:
if self.m_stacks is not None:
self.wf.connect(dg, "masks", custom_masks_filter,
"input_files")
self.wf.connect(custom_masks_filter, "output_files", brainMask,
"out_file")
else:
self.wf.connect(dg, "masks", brainMask, "out_file")
else:
if self.m_stacks is not None:
self.wf.connect(dg, "T2ws", t2ws_filter_prior_masks,
"input_files")
self.wf.connect(t2ws_filter_prior_masks, "output_files",
brainMask, "in_file")
else:
self.wf.connect(dg, "T2ws", brainMask, "in_file")
if not self.m_skip_stacks_ordering:
self.wf.connect(brainMask, "out_file", stacksOrdering,
"input_masks")
self.wf.connect(stacksOrdering, "stacks_order", t2ws_filtered,
"stacks_id")
self.wf.connect(dg, "T2ws", t2ws_filtered, "input_files")
self.wf.connect(stacksOrdering, "stacks_order", masks_filtered,
"stacks_id")
self.wf.connect(brainMask, "out_file", masks_filtered, "input_files")
if not self.m_skip_nlm_denoising:
self.wf.connect(t2ws_filtered,
("output_files", utils.sort_ascending), nlmDenoise,
"in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending), nlmDenoise,
"in_mask") ## Comment to match docker process
self.wf.connect(nlmDenoise, ("out_file", utils.sort_ascending),
srtkCorrectSliceIntensity01_nlm, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01_nlm, "in_mask")
self.wf.connect(t2ws_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01, "in_mask")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkCorrectSliceIntensity01_nlm,
("out_file", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_file")
else:
self.wf.connect(srtkCorrectSliceIntensity01,
("out_file", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity01,
("out_file", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_file")
self.wf.connect(srtkSliceBySliceN4BiasFieldCorrection,
("out_fld_file", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_field")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_mask")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkSliceBySliceN4BiasFieldCorrection,
("out_im_file", utils.sort_ascending),
srtkCorrectSliceIntensity02_nlm, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity02_nlm, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity02_nlm,
("out_file", utils.sort_ascending),
srtkIntensityStandardization01_nlm, "input_images")
self.wf.connect(srtkIntensityStandardization01_nlm,
("output_images", utils.sort_ascending),
srtkHistogramNormalization_nlm, "input_images")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkHistogramNormalization_nlm, "input_masks")
self.wf.connect(srtkHistogramNormalization_nlm,
("output_images", utils.sort_ascending),
srtkIntensityStandardization02_nlm, "input_images")
self.wf.connect(srtkSliceBySliceCorrectBiasField,
("out_im_file", utils.sort_ascending),
srtkCorrectSliceIntensity02, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity02, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity02,
("out_file", utils.sort_ascending),
srtkIntensityStandardization01, "input_images")
self.wf.connect(srtkIntensityStandardization01,
("output_images", utils.sort_ascending),
srtkHistogramNormalization, "input_images")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkHistogramNormalization, "input_masks")
self.wf.connect(srtkHistogramNormalization,
("output_images", utils.sort_ascending),
srtkIntensityStandardization02, "input_images")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkIntensityStandardization02_nlm,
("output_images", utils.sort_ascending),
srtkMaskImage01, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkMaskImage01, "in_mask")
else:
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending),
srtkMaskImage01, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkMaskImage01, "in_mask")
self.wf.connect(srtkMaskImage01, "out_im_file",
srtkImageReconstruction, "input_images")
self.wf.connect(masks_filtered, "output_files",
srtkImageReconstruction, "input_masks")
self.wf.connect(stacksOrdering, "stacks_order",
srtkImageReconstruction, "stacks_order")
self.wf.connect(srtkIntensityStandardization02, "output_images",
srtkTVSuperResolution, "input_images")
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending),
srtkTVSuperResolution, "input_transforms")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkTVSuperResolution, "input_masks")
self.wf.connect(stacksOrdering, "stacks_order", srtkTVSuperResolution,
"stacks_order")
self.wf.connect(srtkImageReconstruction, "output_sdi",
srtkTVSuperResolution, "input_sdi")
if self.m_do_refine_hr_mask:
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending),
srtkHRMask, "input_images")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending), srtkHRMask,
"input_masks")
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending),
srtkHRMask, "input_transforms")
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkHRMask,
"input_sr")
else:
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkHRMask,
"input_image")
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkMaskImage02,
"in_file")
self.wf.connect(srtkHRMask, "output_srmask", srtkMaskImage02,
"in_mask")
self.wf.connect(srtkTVSuperResolution, "output_sr",
srtkN4BiasFieldCorrection, "input_image")
self.wf.connect(srtkHRMask, "output_srmask", srtkN4BiasFieldCorrection,
"input_mask")
# Datasinker
finalFilenamesGeneration = Node(
interface=postprocess.FilenamesGeneration(), name='filenames_gen')
finalFilenamesGeneration.inputs.sub_ses = sub_ses
finalFilenamesGeneration.inputs.sr_id = self.sr_id
finalFilenamesGeneration.inputs.use_manual_masks = self.use_manual_masks
self.wf.connect(stacksOrdering, "stacks_order",
finalFilenamesGeneration, "stacks_order")
datasink = Node(interface=DataSink(), name='data_sinker')
datasink.inputs.base_directory = final_res_dir
if not self.m_skip_stacks_ordering:
self.wf.connect(stacksOrdering, "report_image", datasink,
'figures.@stackOrderingQC')
self.wf.connect(stacksOrdering, "motion_tsv", datasink,
'anat.@motionTSV')
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
datasink, 'anat.@LRmasks')
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending), datasink,
'anat.@LRsPreproc')
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending), datasink,
'xfm.@transforms')
self.wf.connect(finalFilenamesGeneration, "substitutions", datasink,
"substitutions")
self.wf.connect(srtkMaskImage01, ("out_im_file", utils.sort_ascending),
datasink, 'anat.@LRsDenoised')
self.wf.connect(srtkImageReconstruction, "output_sdi", datasink,
'anat.@SDI')
self.wf.connect(srtkN4BiasFieldCorrection, "output_image", datasink,
'anat.@SR')
self.wf.connect(srtkTVSuperResolution, "output_json_path", datasink,
'anat.@SRjson')
self.wf.connect(srtkTVSuperResolution, "output_sr_png", datasink,
'figures.@SRpng')
self.wf.connect(srtkHRMask, "output_srmask", datasink, 'anat.@SRmask')
def create_logb_workflow(name="LOGISMOSB_WF",
master_config=None,
plugin_args=None):
logb_wf = Workflow(name=name)
config = read_json_config("config.json")
config['atlas_info'] = get_local_file_location(config['atlas_info'])
inputs_node = Node(IdentityInterface(fields=[
't1_file', 't2_file', 'posterior_files', 'joint_fusion_file',
'brainlabels_file', 'hncma_atlas'
]),
name="inputspec")
inputs_node.run_without_submitting = True
# ensure that t1 and t2 are in the same voxel lattice
input_t2 = Node(BRAINSResample(), "ResampleInputT2Volume")
input_t2.inputs.outputVolume = "t2_resampled.nii.gz"
input_t2.inputs.pixelType = 'ushort'
input_t2.inputs.interpolationMode = "Linear"
logb_wf.connect([(inputs_node, input_t2, [('t1_file', 'referenceVolume'),
('t2_file', 'inputVolume')])])
white_matter_masking_node = Node(interface=WMMasking(), name="WMMasking")
white_matter_masking_node.inputs.dilation = config['WMMasking']['dilation']
white_matter_masking_node.inputs.csf_threshold = config['WMMasking'][
'csf_threshold']
if master_config and master_config['labelmap_colorlookup_table']:
white_matter_masking_node.inputs.atlas_info = master_config[
'labelmap_colorlookup_table']
else:
white_matter_masking_node.inputs.atlas_info = config['atlas_info']
logb_wf.connect([(inputs_node, white_matter_masking_node,
[("posterior_files", "posterior_files"),
("joint_fusion_file", "atlas_file"),
("brainlabels_file", "brainlabels_file"),
("hncma_atlas", "hncma_file")])])
gm_labels = Node(interface=CreateGMLabelMap(), name="GM_Labelmap")
gm_labels.inputs.atlas_info = config['atlas_info']
logb_wf.connect([(inputs_node, gm_labels, [('joint_fusion_file',
'atlas_file')])])
logismosb_output_node = create_output_spec(
["wmsurface_file", "gmsurface_file"],
config["hemisphere_names"],
name="outputspec")
for hemisphere in config["hemisphere_names"]:
genus_zero_filter = Node(
interface=GenusZeroImageFilter(),
name="{0}_GenusZeroImageFilter".format(hemisphere))
genus_zero_filter.inputs.connectivity = config['GenusZeroImageFilter'][
'connectivity']
genus_zero_filter.inputs.biggestComponent = config[
'GenusZeroImageFilter']['biggestComponent']
genus_zero_filter.inputs.connectedComponent = config[
'GenusZeroImageFilter']['connectedComponent']
genus_zero_filter.inputs.out_mask = "{0}_genus_zero_white_matter.nii.gz".format(
hemisphere)
logb_wf.connect([(white_matter_masking_node, genus_zero_filter,
[('{0}_wm'.format(hemisphere), 'in_file')])])
surface_generation = Node(
interface=BRAINSSurfaceGeneration(),
name="{0}_BRAINSSurfaceGeneration".format(hemisphere))
surface_generation.inputs.smoothSurface = config[
'BRAINSSurfaceGeneration']['smoothSurface']
surface_generation.inputs.numIterations = config[
'BRAINSSurfaceGeneration']['numIterations']
surface_generation.inputs.out_file = "{0}_white_matter_surface.vtk".format(
hemisphere)
logb_wf.connect([(genus_zero_filter, surface_generation,
[('out_file', 'in_file')])])
logismosb = Node(interface=LOGISMOSB(),
name="{0}_LOGISMOSB".format(hemisphere))
logismosb.inputs.smoothnessConstraint = config['LOGISMOSB'][
'smoothnessConstraint']
logismosb.inputs.nColumns = config['LOGISMOSB']['nColumns']
logismosb.inputs.columnChoice = config['LOGISMOSB']['columnChoice']
logismosb.inputs.columnHeight = config['LOGISMOSB']['columnHeight']
logismosb.inputs.nodeSpacing = config['LOGISMOSB']['nodeSpacing']
logismosb.inputs.w = config['LOGISMOSB']['w']
logismosb.inputs.a = config['LOGISMOSB']['a']
logismosb.inputs.nPropagate = config['LOGISMOSB']['nPropagate']
logismosb.inputs.basename = hemisphere
if config['LOGISMOSB']['thickRegions']:
logismosb.inputs.thick_regions = config['LOGISMOSB'][
'thickRegions']
else:
logismosb.inputs.useHNCMALabels = True
if plugin_args:
logismosb.plugin_args = plugin_args
logb_wf.connect([
(inputs_node, logismosb, [("t1_file", "t1_file"),
('hncma_atlas', 'atlas_file')]),
(input_t2, logismosb, [("outputVolume", "t2_file")]),
(genus_zero_filter, logismosb, [("out_file", "wm_file")]),
(surface_generation, logismosb, [("out_file", "mesh_file")]),
(white_matter_masking_node, logismosb,
[('{0}_boundary'.format(hemisphere), 'brainlabels_file')]),
(logismosb, logismosb_output_node,
[("gmsurface_file", "{0}_gmsurface_file".format(hemisphere)),
("wmsurface_file", "{0}_wmsurface_file".format(hemisphere))])
])
return logb_wf
def pals(config: dict):
# Get config file defining workflow
# configs = json.load(open(config_file, 'r'))
print('Starting: initializing workflow.')
# Build pipelie
wf = Workflow(name='PALS')
# bidsLayout = bids.BIDSLayout(config['BIDSRoot'])
# Get data
loader = BIDSDataGrabber(index_derivatives=False)
loader.inputs.base_dir = config['BIDSRoot']
loader.inputs.subject = config['Subject']
if (config['Session'] is not None):
loader.inputs.session = config['Session']
loader.inputs.output_query = {
't1w': dict(**config['T1Entities'], invalid_filters='allow')
}
loader.inputs.extra_derivatives = [config['BIDSRoot']]
loader = Node(loader, name='BIDSgrabber')
entities = {
'subject': config['Subject'],
'session': config['Session'],
'suffix': 'T1w',
'extension': '.nii.gz'
}
# Reorient to radiological
if (config['Analysis']['Reorient']):
radio = MapNode(
Reorient(orientation=config['Analysis']['Orientation']),
name="reorientation",
iterfield='in_file')
if ('Reorient' in config['Outputs'].keys()):
reorient_sink = MapNode(Function(function=copyfile,
input_names=['src', 'dst']),
name='reorient_copy',
iterfield='src')
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-' + config[
'Analysis']['Orientation'] + '_{suffix}{extension}'
reorient_filename = join(config['Outputs']['Reorient'],
path_pattern.format(**entities))
pathlib.Path(os.path.dirname(reorient_filename)).mkdir(
parents=True, exist_ok=True)
reorient_sink.inputs.dst = reorient_filename
wf.connect([(radio, reorient_sink, [('out_file', 'src')])])
else:
radio = MapNode(Function(function=infile_to_outfile,
input_names='in_file',
output_names='out_file'),
name='identity',
iterfield='in_file')
# Brain extraction
bet = node_fetch.extraction_node(config, **config['BrainExtraction'])
if ('BrainExtraction' in config['Outputs'].keys()):
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-brain_mask{extension}'
brain_mask_sink = MapNode(Function(function=copyfile,
input_names=['src', 'dst']),
name='brain_mask_sink',
iterfield='src')
brain_mask_out = join(config['Outputs']['BrainExtraction'],
path_pattern.format(**entities))
pathlib.Path(os.path.dirname(brain_mask_out)).mkdir(parents=True,
exist_ok=True)
brain_mask_sink.inputs.dst = brain_mask_out
## Lesion load calculation
# Registration
reg = node_fetch.registration_node(config, **config['Registration'])
if ('RegistrationTransform' in config['Outputs'].keys()):
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-transform.mat'
registration_transform_filename = join(
config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
registration_transform_sink = MapNode(Function(
function=copyfile, input_names=['src', 'dst']),
name='registration_transf_sink',
iterfield='src')
pathlib.Path(os.path.dirname(registration_transform_filename)).mkdir(
parents=True, exist_ok=True)
registration_transform_sink.inputs.dst = registration_transform_filename
wf.connect([(reg, registration_transform_sink, [('out_matrix_file',
'src')])])
# Get mask
mask_path_fetcher = Node(BIDSDataGrabber(
base_dir=config['LesionRoot'],
subject=config['Subject'],
index_derivatives=False,
output_query={
'mask': dict(**config['LesionEntities'], invalid_filters='allow')
},
extra_derivatives=[config['LesionRoot']]),
name='mask_grabber')
if (config['Session'] is not None):
mask_path_fetcher.inputs.session = config['Session']
# Apply reg file to lesion mask
apply_xfm = node_fetch.apply_xfm_node(config)
# Lesion load calculation
if (config['Analysis']['LesionLoadCalculation']):
lesion_load = MapNode(Function(function=overlap,
input_names=['ref_mask', 'roi_list'],
output_names='out_list'),
name='overlap_calc',
iterfield=['ref_mask'])
roi_list = []
if (os.path.exists(config['ROIDir'])):
buf = os.listdir(config['ROIDir'])
roi_list = [
os.path.abspath(os.path.join(config['ROIDir'], b)) for b in buf
]
else:
warnings.warn(f"ROIDir ({config['ROIDir']}) doesn't exist.")
buf = config['ROIList']
roi_list += [os.path.abspath(b) for b in buf]
lesion_load.inputs.roi_list = roi_list
# CSV output
csv_output = MapNode(Function(
function=csv_writer,
input_names=['filename', 'data_dict', 'subject', 'session']),
name='csv_output',
iterfield=['data_dict'])
csv_output.inputs.subject = config['Subject']
csv_output.inputs.session = config['Session']
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-LesionLoad.csv'
csv_out_filename = join(config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
csv_output.inputs.filename = csv_out_filename
wf.connect([(apply_xfm, lesion_load, [('out_file', 'ref_mask')]),
(lesion_load, csv_output, [('out_list', 'data_dict')])])
## Lesion correction
if (config['Analysis']['LesionCorrection']):
## White matter removal node. Does the white matter correction; has multiple inputs that need to be supplied.
wm_removal = MapNode(Function(
function=white_matter_correction,
input_names=[
'image', 'wm_mask', 'lesion_mask', 'max_difference_fraction'
],
output_names=['out_data', 'corrected_volume']),
name='wm_removal',
iterfield=['image', 'wm_mask', 'lesion_mask'])
wm_removal.inputs.max_difference_fraction = config['LesionCorrection'][
'WhiteMatterSpread']
## File loaders
# Loads the subject image, passes it to wm_removal node
subject_image_loader = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load0',
iterfield='in_filename')
wf.connect([
(radio, subject_image_loader, [('out_file', 'in_filename')]),
(subject_image_loader, wm_removal, [('out_image', 'image')])
])
# Loads the mask image, passes it to wm_removal node
mask_image_loader = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load2',
iterfield='in_filename')
wf.connect([
(mask_path_fetcher, mask_image_loader, [('mask', 'in_filename')]),
(mask_image_loader, wm_removal, [('out_image', 'lesion_mask')])
])
# Save lesion mask with white matter voxels removed
output_image = MapNode(Function(
function=image_write,
input_names=['image', 'reference', 'file_name']),
name='image_writer0',
iterfield=['image', 'reference'])
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-CorrectedLesion_mask{extension}'
lesion_corrected_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
output_image.inputs.file_name = lesion_corrected_filename
wf.connect([(wm_removal, output_image, [('out_data', 'image')]),
(mask_path_fetcher, output_image, [('mask', 'reference')])
])
## CSV output
csv_output_corr = MapNode(Function(function=csv_writer,
input_names=[
'filename', 'subject',
'session', 'data', 'data_name'
]),
name='csv_output_corr',
iterfield=['data'])
csv_output_corr.inputs.subject = config['Subject']
csv_output_corr.inputs.session = config['Session']
csv_output_corr.inputs.data_name = 'CorrectedVolume'
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-LesionLoad.csv'
csv_out_filename = join(config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
csv_output_corr.inputs.filename = csv_out_filename
wf.connect([(wm_removal, csv_output_corr, [('corrected_volume', 'data')
])])
## White matter segmentation; either do segmentation or load the file
if (config['Analysis']['WhiteMatterSegmentation']):
# Config is set to do white matter segmentation
# T1 intensity normalization
t1_norm = MapNode(Function(
function=rescale_image,
input_names=['image', 'range_min', 'range_max', 'save_image'],
output_names='out_file'),
name='normalization',
iterfield=['image'])
t1_norm.inputs.range_min = config['LesionCorrection'][
'ImageNormMin']
t1_norm.inputs.range_max = config['LesionCorrection'][
'ImageNormMax']
t1_norm.inputs.save_image = True
wf.connect([(bet, t1_norm, [('out_file', 'image')])])
# White matter segmentation
wm_seg = MapNode(FAST(), name="wm_seg", iterfield='in_files')
wm_seg.inputs.out_basename = "segmentation"
wm_seg.inputs.img_type = 1
wm_seg.inputs.number_classes = 3
wm_seg.inputs.hyper = 0.1
wm_seg.inputs.iters_afterbias = 4
wm_seg.inputs.bias_lowpass = 20
wm_seg.inputs.segments = True
wm_seg.inputs.no_pve = True
ex_last = MapNode(Function(function=extract_last,
input_names=['in_list'],
output_names='out_entry'),
name='ex_last',
iterfield='in_list')
file_load1 = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load1',
iterfield='in_filename')
# White matter output; only necessary if white matter is segmented
wm_map = MapNode(Function(
function=image_write,
input_names=['image', 'reference', 'file_name']),
name='image_writer1',
iterfield=['image', 'reference'])
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-WhiteMatter_mask{extension}'
wm_map_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
wm_map.inputs.file_name = wm_map_filename
wf.connect([(file_load1, wm_map, [('out_image', 'image')]),
(mask_path_fetcher, wm_map, [('mask', 'reference')])])
# Connect nodes in workflow
wf.connect([
(wm_seg, ex_last, [('tissue_class_files', 'in_list')]),
(t1_norm, wm_seg, [('out_file', 'in_files')]),
# (ex_last, wm_map, [('out_entry', 'image')]),
(ex_last, file_load1, [('out_entry', 'in_filename')]),
(file_load1, wm_removal, [('out_image', 'wm_mask')])
])
elif (config['Analysis']['LesionCorrection']):
# No white matter segmentation should be done, but lesion correction is expected.
# White matter segmentation must be supplied
wm_seg_path = config['WhiteMatterSegmentationFile']
if (len(wm_seg_path) == 0 or not os.path.exists(wm_seg_path)):
# Check if file exists at output
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-WhiteMatter_mask{extension}'
wm_map_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
if (os.path.exists(wm_map_filename)):
wm_seg_path = wm_map_filename
else:
raise ValueError(
'Config file is inconsistent; if WhiteMatterSegmentation is false but LesionCorrection'
' is true, then WhiteMatterSegmentationFile must be defined and must exist.'
)
file_load1 = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load1',
iterfield='in_filename')
file_load1.inputs.in_filename = wm_seg_path
# Connect nodes in workflow
wf.connect([(file_load1, wm_removal, [('out_image', 'wm_mask')])])
# Connecting workflow.
wf.connect([
# Starter
(loader, radio, [('t1w', 'in_file')]),
(radio, bet, [('out_file', 'in_file')]),
(bet, reg, [('out_file', 'in_file')]),
(reg, apply_xfm, [('out_matrix_file', 'in_matrix_file')]),
(mask_path_fetcher, apply_xfm, [('mask', 'in_file')]),
])
try:
graph_out = config['Outputs'][
'LesionCorrected'] + '/sub-{subject}/ses-{session}/anat/'.format(
**entities)
wf.write_graph(graph2use='orig',
dotfilename=join(graph_out, 'graph.dot'),
format='png')
os.remove(graph_out + 'graph.dot')
os.remove(graph_out + 'graph_detailed.dot')
except OSError:
warnings.warn(
"graphviz not installed; can't produce graph. See http://www.graphviz.org/download/ for "
"installation instructions.")
wf.run()
return wf
def petpvc_mask(wf_name="petpvc_mask"):
""" A Workflow that returns a 4D merge of 4 volumes for PETPVC: GM, WM, CSF and background.
Parameters
----------
wf_name: str
The name of the workflow.
Nipype.Inputs
-------------
pvcmask_input.tissues: list of existing files
List of tissue files in anatomical space, the 3 file
paths must be in this order: GM, WM, CSF
Nipype.Outputs
--------------
pvcmask_output.petpvc_mask: existing file
A 4D volume file with these maps in order: GM, WM, CSF, background
pvcmask_output.brain_mask: existing file
A mask that is a binarised sum of the tissues file with fslmaths.
Can be used as brain mask in anatomical space for the PET image.
Returns
-------
wf: nipype Workflow
"""
# define nodes
# specify input and output fields
in_fields = ["tissues"]
out_fields = ["petpvc_mask",
"brain_mask",]
# input
pvcmask_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="pvcmask_input")
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"], mandatory_inputs=True),
name="tissues")
merge_list = setup_node(Merge(4), name="merge_list")
## maths for background
img_bkg = setup_node(Function(function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name='background')
img_bkg.inputs.out_file = "tissue_bkg.nii.gz"
img_bkg.inputs.formula = "np.maximum((-((gm + wm + csf) - 1)), 0)"
## maths for brain mask
brain_mask = setup_node(Function(function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name='brain_mask')
brain_mask.inputs.out_file = "tissues_brain_mask.nii.gz"
brain_mask.inputs.formula = "np.abs(gm + wm + csf) > 0"
## concat the tissues images and the background for PETPVC
merge_tissues = setup_node(Function(function=concat_imgs,
input_names=["in_files"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name='merge_tissues')
merge_tissues.inputs.out_file = "petpvc_mask.nii.gz"
# output
pvcmask_output = setup_node(IdentityInterface(fields=out_fields), name="pvcmask_output")
# Create the workflow object
wf = Workflow(name=wf_name)
# Connect the nodes
wf.connect([
# separate [GM, WM, CSF] into [GM] and [WM, CSF]
(pvcmask_input, tissues, [(("tissues", selectindex, [0]), "gm"),
(("tissues", selectindex, [1]), "wm"),
(("tissues", selectindex, [2]), "csf"),
]),
(tissues, img_bkg, [("gm", "gm" ), ("wm", "wm" ), ("csf", "csf"),]),
(tissues, brain_mask, [("gm", "gm" ), ("wm", "wm" ), ("csf", "csf"),]),
(tissues, merge_list, [("gm", "in1"), ("wm", "in2"), ("csf", "in3"),]),
# create a list of [GM, WM, CSF, BKG]
(img_bkg, merge_list, [("out_file", "in4")]),
# merge into 4D: [GM, WM, CSF, BKG]
(merge_list, merge_tissues, [("out", "in_files")]),
# output
(merge_tissues, pvcmask_output, [("out_file", "petpvc_mask")]),
(brain_mask, pvcmask_output, [("out_file", "brain_mask")]),
])
return wf
def intensity_norm(wf_name='intensity_norm'):
""" Workflow that uses a mask against a source from where the mean value will be taken.
This mean value will be used to demean the whole source and leave it in out_file.
Parameters
----------
wf_name: str
The name of the workflow.
Nipype Inputs
-------------
intnorm_input.source: existing file
The image from where to extract the signal values and normalize.
intnorm_input.mask: existing file
The mask to specify which voxels to use to calculate the statistics
for normalization.
Nipype Outputs
--------------
intnorm_output.out_file: existing file
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["source",
"mask"]
out_fields = ["out_file"]
# input
intnorm_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="intnorm_input")
# fix the affine matrix (it's necessary for some cases)
resample = setup_node(Function(function=resample_to_img,
input_names=["source", "target", "interpolation"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name="resample_mask")
resample.inputs.interpolation = "nearest"
# calculate masked mean value
mean_val = setup_node(Function(function=math_img,
input_names=["formula", "img", "mask"],
output_names=["out_value"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name='mean_value')
mean_val.inputs.formula = "np.mean(np.nonzero(img[mask > 0]))"
# normalize
norm_img = setup_node(Function(function=math_img,
input_names=["formula", "out_file", "img", "val"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name='norm_img')
norm_img.inputs.formula = "img / val"
# output
intnorm_output = setup_node(IdentityInterface(fields=out_fields),
name="intnorm_output")
# Create the workflow object
wf = Workflow(name=wf_name)
wf.connect([
# resample
(intnorm_input, resample, [("source", "target"),
("mask", "source")]),
# normalize
(intnorm_input, mean_val, [("source", "img" )]),
(resample, mean_val, [("out_file", "mask")]),
(intnorm_input, norm_img, [("source", "img"),
(("source", rename, "_intnormed"), "out_file"),
]),
(mean_val, norm_img, [("out_value", "val")]),
(norm_img, intnorm_output, [("out_file", "out_file")]),
])
return wf
ValueError: BIDS root does not exist: /tmp/tmp9g7ddldw/bids-grabber/examples/examples/BIDS
#problem with base_dir
bg.inputs.base_dir = "/mnt/Filbey/Evan/MJXProcessing/examples/examples/BIDS"
bg.inputs.subject = 'M7500516'
#bg.inputs.output_query = {'T1w': dict(type='anat')}
res = bg.run()
res.outputs
print("done")
"""
def printMe(paths):
print("\n\nanalyzing " + str(paths) + "\n\n")
analyzeANAT = Node(Function(function=printMe, input_names=["paths"],
output_names=[]), name="analyzeANAT")
bg_all = Node(BIDSDataGrabber(), name='bids-grabber')
bg_all.inputs.base_dir = '/mnt/Filbey/Evan/MJXProcessing/examples/examples/BIDS'
bg_all.inputs.output_query = {'ses': dict(type='session')}
bg_all.iterables = ('subject', layout.get_subjects()[0])
wf = Workflow(name="bids_demo")
wf.connect(bg_all, "session", analyzeANAT, "paths")
wf.run()
def create_workflow(bids_dir, output_dir, subject):
wf_base_dir = os.path.join("{}".format(output_dir), "superres-mri",
"sub-{}".format(subject), "nipype")
print("Ouput directory: {}".format(wf_base_dir))
wf = Workflow(name="sinapp_nlmdenoise", base_dir=wf_base_dir)
# Initialization
if os.path.isfile(os.path.join(output_dir, "pypeline.log")):
os.unlink(os.path.join(output_dir, "pypeline.log"))
config.update_config({
'logging': {
'log_directory': os.path.join(output_dir),
'log_to_file': True
},
'execution': {
'remove_unnecessary_outputs': False,
'stop_on_first_crash': True,
'stop_on_first_rerun': False,
'crashfile_format': "txt",
'write_provenance': False,
},
'monitoring': {
'enabled': True
}
})
logging.update_logging(config)
iflogger = logging.getLogger('nipype.interface')
iflogger.info("**** Processing ****")
bg = Node(BIDSDataGrabber(infields=['subject']), name='bids_grabber')
bg.inputs.base_dir = bids_dir
bg.inputs.subject = subject
bg.inputs.index_derivatives = True
bg.inputs.output_query = {
'T2ws':
dict(suffix='T2w', datatype='anat', extensions=[".nii", ".nii.gz"]),
'masks':
dict(suffix='mask', datatype='anat', extensions=[".nii", ".nii.gz"])
}
preparePaths = Node(interface=prepareDockerPaths(), name="preparePaths")
preparePaths.inputs.local_dir = bids_dir
preparePaths.inputs.docker_dir = '/fetaldata'
nlmDenoise = Node(interface=MultipleBtkNLMDenoising(),
base_dir=os.path.join(output_dir, 'bids_demo'),
name='nlmDenoise')
nlmDenoise.inputs.bids_dir = bids_dir
nlmDenoise.inputs.weight = 0.1
wf.connect(bg, "T2ws", preparePaths, "local_T2ws_paths")
wf.connect(bg, "masks", preparePaths, "local_masks_paths")
wf.connect(preparePaths, "docker_T2ws_paths", nlmDenoise, "input_images")
wf.connect(preparePaths, "docker_masks_paths", nlmDenoise, "input_masks")
return wf
Perform Feat preprocessing on given data files and then merge ouputs. Inputs are taken using DataGrabber interface. ''' ############################################################################### # # CREATE MAIN WORKFLOW # ############################################################################### from nipype.pipeline import Workflow, Node featreg_merge = Workflow(name='featreg_merge') ############################################################################### # # CREATE FEAT REGISTRATION WORKFLOW NODE # ############################################################################### from nipype.workflows.fmri.fsl import create_featreg_preproc import nipype.interfaces.fsl as fsl preproc = create_featreg_preproc(highpass=True, whichvol='mean') preproc.inputs.inputspec.fwhm = 0 preproc.inputs.inputspec.highpass = 128./(2*2.5)
def get_wf_main(name='wf_main'):
wf_main = Workflow(name=name)
inputspec = Node(IdentityInterface(fields=[
'resampled_anat_file_path', 'func2anat_mat_path',
'reference_func_file_path', 'csf_tissue_prior_path',
'wm_tissue_prior_path', 'threshold', 'std2func_mat_path',
'brain_mask_eroded'
]),
name="inputspec")
outputspec = Node(IdentityInterface(fields=[
'csf_tissue_prior_path', 'wm_tissue_prior_path', 'qc_stats_dict'
]),
name="outputspec")
tissue_priors = tp.get_wf_tissue_priors(name='wf_tissue_priors')
tissue_masks = gm.get_wf_tissue_masks(name='wf_tissue_masks')
def compute_qc_stats(anat_file_path, csf_mask, csf_prior, wm_mask,
wm_prior):
import numpy as np
import nibabel as nib
from collections import OrderedDict as od
# print('$$$$$$$$$$$$Inside$$$$$$$$$QCFUNC')
csf_prior_data = nib.load(csf_prior).get_data()
wm_prior_data = nib.load(wm_prior).get_data()
csf_mask_data = nib.load(csf_mask).get_data()
wm_mask_data = nib.load(wm_mask).get_data()
# A
voxels_count_csf_prior = len((np.where(csf_prior_data == 1))[0])
voxels_count_wm_prior = len((np.where(wm_prior_data == 1))[0])
# B
voxels_count_csf_mask = len((np.where(csf_mask_data == 1))[0])
voxels_count_wm_mask = len((np.where(wm_mask_data == 1))[0])
# A - B
A_minus_B_csf = len(np.where((csf_prior_data - csf_mask_data) == 1)[0])
A_minus_B_wm = len(np.where((wm_prior_data - wm_mask_data) == 1)[0])
# B - A
B_minus_A_csf = len(
np.where((csf_prior_data - csf_mask_data) == -1)[0])
B_minus_A_wm = len(np.where((wm_prior_data - wm_mask_data) == -1)[0])
# A U B
A_union_B_csf = len(np.where((csf_prior_data + csf_mask_data) != 0)[0])
A_union_B_wm = len(np.where((wm_prior_data + wm_mask_data) != 0)[0])
# A I B
A_intersection_B_csf = len(
np.where((csf_prior_data * csf_mask_data) == 1)[0])
A_intersection_B_wm = len(
np.where((wm_prior_data * wm_mask_data) == 1)[0])
print('voxels_count_csf_prior ', voxels_count_csf_prior)
print('voxels_count_wm_prior ', voxels_count_wm_prior)
print('voxels_count_csf_mask ', voxels_count_csf_mask)
print('voxels_count_wm_mask ', voxels_count_wm_mask)
print('prior_minus_mask_csf ', A_minus_B_csf)
print('prior_minus_mask_wm ', A_minus_B_wm)
print('mask_minus_prior_csf ', B_minus_A_csf)
print('mask_minus_prior_wm ', B_minus_A_wm)
print('prior_union_mask_csf ', A_union_B_csf)
print('prior_union_mask_wm ', A_union_B_wm)
print('prior_intersection_mask_csf ', A_intersection_B_csf)
print('prior_intersection_mask_wm ', A_intersection_B_wm)
quality_csf = A_intersection_B_csf / A_union_B_csf
quality_wm = A_intersection_B_wm / A_union_B_wm
print('quality_csf ', quality_csf)
print('quality_wm ', quality_wm)
# A : Prior and B :Mask
print('Anat File path ', anat_file_path)
sub_id = anat_file_path.split('/')[-1].split('_')[0].split('-')[1]
print('Sub ID ', sub_id)
dict = od()
dict['sub_id'] = [sub_id]
dict['voxels_count_csf_prior'] = [voxels_count_csf_prior]
dict['voxels_count_wm_prior'] = [voxels_count_wm_prior]
dict['voxels_count_csf_mask'] = [voxels_count_csf_mask]
dict['voxels_count_wm_mask'] = [voxels_count_wm_mask]
dict['prior_minus_mask_csf'] = [A_minus_B_csf]
dict['prior_minus_mask_wm'] = [A_minus_B_wm]
dict['mask_minus_prior_csf'] = [B_minus_A_csf]
dict['mask_minus_prior_wm'] = [B_minus_A_wm]
dict['prior_union_mask_csf'] = [A_union_B_csf]
dict['prior_union_mask_wm'] = [A_union_B_wm]
dict['prior_intersection_mask_csf'] = [A_intersection_B_csf]
dict['prior_intersection_mask_wm'] = [A_intersection_B_wm]
dict['quality_csf'] = [quality_csf]
dict['quality_wm'] = [quality_wm]
return dict
qc_stats = Node(Function(function=compute_qc_stats,
input_names=[
'anat_file_path', 'csf_mask', 'csf_prior',
'wm_mask', 'wm_prior'
],
output_names=['dict']),
name='qc_stats')
wf_main.connect(inputspec, 'csf_tissue_prior_path', tissue_priors,
'inputspec.csf_tissue_prior_path')
wf_main.connect(inputspec, 'wm_tissue_prior_path', tissue_priors,
'inputspec.wm_tissue_prior_path')
wf_main.connect(inputspec, 'threshold', tissue_priors,
'inputspec.threshold')
wf_main.connect(inputspec, 'reference_func_file_path', tissue_priors,
'inputspec.reference_func_file_path')
wf_main.connect(inputspec, 'std2func_mat_path', tissue_priors,
'inputspec.std2func_mat_path')
wf_main.connect(tissue_priors, 'outputspec.csf_tissue_prior_path',
outputspec, 'csf_tissue_prior_path')
wf_main.connect(tissue_priors, 'outputspec.wm_tissue_prior_path',
outputspec, 'wm_tissue_prior_path')
wf_main.connect(inputspec, 'resampled_anat_file_path', tissue_masks,
'inputspec.resampled_anat_file_path')
wf_main.connect(inputspec, 'reference_func_file_path', tissue_masks,
'inputspec.reference_func_file_path')
wf_main.connect(inputspec, 'func2anat_mat_path', tissue_masks,
'inputspec.func2anat_mat_path')
wf_main.connect(inputspec, 'std2func_mat_path', tissue_masks,
'inputspec.std2func_mat_path')
wf_main.connect(inputspec, 'brain_mask_eroded', tissue_masks,
'inputspec.brain_mask_eroded')
wf_main.connect(inputspec, 'threshold', tissue_masks,
'inputspec.threshold')
# wf_main.connect(tissue_masks, 'outputspec.csf_mask', outputspec,'csf_mask')
# wf_main.connect(tissue_masks, 'outputspec.wm_mask', outputspec,'wm_mask')
wf_main.connect(tissue_priors, 'outputspec.csf_tissue_prior_path',
qc_stats, 'csf_prior')
wf_main.connect(tissue_priors, 'outputspec.wm_tissue_prior_path', qc_stats,
'wm_prior')
wf_main.connect(tissue_masks, 'outputspec.csf_mask', qc_stats, 'csf_mask')
wf_main.connect(tissue_masks, 'outputspec.wm_mask', qc_stats, 'wm_mask')
wf_main.connect(inputspec, 'resampled_anat_file_path', qc_stats,
'anat_file_path')
wf_main.connect(qc_stats, 'dict', outputspec, 'qc_stats_dict')
return wf_main
def get_wf_tissue_masks(name='wf_tissue_masks'):
'''
This Function gives a workflow that resamples the T1 brains, extracts the
tissue types thresholds at 0.5 and registers them to T2* space
It then registers the tissue priors to the T2* space and then performs a
bitwise AND between two maps.
'''
# csf_tissue_prior_path, gm_tissue_prior_path, wm_tissue_prior_path,
# threshold = 0.5
wf_tissue_masks = Workflow(name=name)
inputspec = Node(IdentityInterface(fields=[
'resampled_anat_file_path', 'func2anat_mat_path', 'std2func_mat_path',
'reference_func_file_path', 'brain_mask_eroded', 'threshold'
]),
name="inputspec")
# FSL FAST node to segment the T1 brain
fast = Node(FAST(out_basename='fast_'), name='fast')
# probability_maps=True,segments=True,
wf_tissue_masks.connect(inputspec, 'resampled_anat_file_path', fast,
'in_files')
# Invert the func2anat matrix to get anat2func
inv_mat = Node(ConvertXFM(invert_xfm=True), name='inv_mat')
wf_tissue_masks.connect(inputspec, 'func2anat_mat_path', inv_mat,
'in_file')
# Transform the above segmented tissue masks to the functional space using the inverse matrix
anat2func_xform_csf = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='sinc'),
name='anat2func_xform_csf')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
anat2func_xform_csf, 'reference')
wf_tissue_masks.connect(inv_mat, 'out_file', anat2func_xform_csf,
'in_matrix_file')
anat2func_xform_wm = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='sinc'),
name='anat2func_xform_wm')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
anat2func_xform_wm, 'reference')
wf_tissue_masks.connect(inv_mat, 'out_file', anat2func_xform_wm,
'in_matrix_file')
std2func_xform_eroded_brain = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='nearestneighbour'),
name='std2func_xform_eroded_brain')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
std2func_xform_eroded_brain, 'reference')
wf_tissue_masks.connect(inputspec, 'std2func_mat_path',
std2func_xform_eroded_brain, 'in_matrix_file')
def select_item_from_array(arr, index=0):
import numpy as np
arr = np.array(arr)
return arr[index]
wf_tissue_masks.connect(
fast, ('partial_volume_files', select_item_from_array, 0),
anat2func_xform_csf, 'in_file')
wf_tissue_masks.connect(
fast, ('partial_volume_files', select_item_from_array, 2),
anat2func_xform_wm, 'in_file')
wf_tissue_masks.connect(inputspec, 'brain_mask_eroded',
std2func_xform_eroded_brain, 'in_file')
# Threshold
def get_opstring(threshold):
op = '-thr ' + str(threshold) + ' -bin'
return op
# print(inputspec.outputs)
# ----- CSF ------
threshold_csf = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_csf')
# threshold_csf.inputs.op_string = '-thresh '+str(inputspec.outputs.threshold)+' -bin'
wf_tissue_masks.connect(inputspec, ('threshold', get_opstring),
threshold_csf, 'op_string')
wf_tissue_masks.connect(anat2func_xform_csf, 'out_file', threshold_csf,
'in_file')
# ------- GM --------
# threshold_gm = Node(interface=ImageMaths(op_string='-thresh',
# suffix='_thresh'),
# name='threshold_gm')
#
#
# wf_tissue_priors.connect(inputspec, ('threshold', get_opstring), threshold_gm, 'op_string' )
# wf_tissue_priors.connect(fast, partial_volume_map[1], threshold_gm, 'in_file')
#
# -------- WM --------
threshold_wm = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_wm')
wf_tissue_masks.connect(inputspec, ('threshold', get_opstring),
threshold_wm, 'op_string')
wf_tissue_masks.connect(anat2func_xform_wm, 'out_file', threshold_wm,
'in_file')
# -------------------
#
# wf_tissue_masks.connect(threshold_csf, 'out_file', std2func_xform_csf, 'in_file')
# wf_tissue_masks.connect(threshold_wm, 'out_file', std2func_xform_wm, 'in_file')
# Masking the outer brain CSF
csf_mask = Node(interface=ApplyMask(), name='csf_mask')
wf_tissue_masks.connect(threshold_csf, 'out_file', csf_mask, 'in_file')
wf_tissue_masks.connect(std2func_xform_eroded_brain, 'out_file', csf_mask,
'mask_file')
# Masking the outer brain WM that might be present due to poor BET
wm_mask = Node(interface=ApplyMask(), name='wm_mask')
wf_tissue_masks.connect(threshold_wm, 'out_file', wm_mask, 'in_file')
wf_tissue_masks.connect(std2func_xform_eroded_brain, 'out_file', wm_mask,
'mask_file')
# wm_mask = Node(interface=ApplyMask(),
# name='wm_mask')
# wf_tissue_masks.connect(std2func_xform_wm, 'out_file', wm_mask, 'in_file')
# wf_tissue_masks.connect(std2func_xform_wm_prior, 'out_file', wm_mask, 'mask_file')
outputspec = Node(IdentityInterface(fields=['csf_mask', 'wm_mask']),
name="outputspec")
wf_tissue_masks.connect(csf_mask, 'out_file', outputspec, 'csf_mask')
# wf_tissue_priors.connect(threshold_gm, 'out_file', outputspec, 'gm_tissue_prior_path')
wf_tissue_masks.connect(wm_mask, 'out_file', outputspec, 'wm_mask')
return wf_tissue_masks
'''
featreg_merge.py
script
Perform Feat preprocessing on given data files and then merge ouputs.
'''
from nipype.workflows.fmri.fsl import create_featreg_preproc
import nipype.interfaces.fsl as fsl
from nipype.pipeline import Workflow, Node
# get filelist from file
nifti_filelist = open('nifti_filelist.txt').read().splitlines()
featreg_merge = Workflow(name='featreg_merge')
preproc = create_featreg_preproc(highpass=True, whichvol='mean')
preproc.inputs.inputspec.func = nifti_filelist
preproc.inputs.inputspec.fwhm = 0
preproc.inputs.inputspec.highpass = 128. / (2 * 2.5)
# preproc.base_dir = '/tmp/pre/working_dir'
# preproc.run()
merge = Node(interface=fsl.utils.Merge(dimension='t',
output_type='NIFTI_GZ',
merged_file='merged.nii.gz'),
name='merge')
featreg_merge.connect(preproc, 'outputspec.highpassed_files', merge,
'in_files')
script Perform Feat preprocessing on given data files and then merge ouputs. Inputs are taken using DataGrabber interface. ''' ############################################################################### # # CREATE MAIN WORKFLOW # ############################################################################### from nipype.pipeline import Workflow, Node featreg_merge = Workflow(name='featreg_merge') ############################################################################### # # CREATE FEAT REGISTRATION WORKFLOW NODE # ############################################################################### from nipype.workflows.fmri.fsl import create_featreg_preproc import nipype.interfaces.fsl as fsl preproc = create_featreg_preproc(highpass=True, whichvol='mean') preproc.inputs.inputspec.fwhm = 0 preproc.inputs.inputspec.highpass = 128. / (2 * 2.5) ###############################################################################
class AnatomicalPipeline:
"""Class used to represent the workflow of the Super-Resolution reconstruction pipeline.
Attributes
-----------
bids_dir : string
BIDS root directory (required)
output_dir : string
Output derivatives directory (required)
subject : string
Subject ID (in the form ``sub-XX``)
wf : nipype.pipeline.Workflow
Nipype workflow of the reconstruction pipeline
deltatTV : string
Super-resolution optimization time-step
lambdaTV : float
Regularization weight (default is 0.75)
primal_dual_loops : string
Number of primal/dual loops used in the optimization of the total-variation
super-resolution algorithm.
sr_id : string
ID of the reconstruction useful to distinguish when multiple reconstructions
with different order of stacks are run on the same subject
session : string
Session ID if applicable (in the form ``ses-YY``)
m_stacks : list(int)
List of stack to be used in the reconstruction.
The specified order is kept if `skip_stacks_ordering` is True.
m_masks_derivatives_dir : string
directory basename in BIDS directory derivatives where to search for masks (optional)
m_skip_svr : bool
Weither the Slice-to-Volume Registration should be skipped in the image reconstruction.
(default is False)
m_do_refine_hr_mask : bool
Weither a refinement of the HR mask should be performed. (default is False)
m_skip_nlm_denoising : bool
Weither the NLM denoising preprocessing should be skipped. (default is False)
m_skip_stacks_ordering : bool (optional)
Weither the automatic stacks ordering should be skipped. (default is False)
Examples
--------
>>> from pymialsrtk.pipelines.anatomical.srr import AnatomicalPipeline
>>> # Create a new instance
>>> pipeline = AnatomicalPipeline(bids_dir='/path/to/bids_dir',
output_dir='/path/to/output_dir',
subject='sub-01',
p_stacks=[1,3,2,0],
sr_id=1,
session=None,
paramTV={deltatTV = "0.001",
lambdaTV = "0.75",
primal_dual_loops = "20"},
masks_derivatives_dir="/custom/mask_dir",
masks_desc=None,
p_dict_custom_interfaces=None)
>>> # Create the super resolution Nipype workflow
>>> pipeline.create_workflow()
>>> # Execute the workflow
>>> res = pipeline.run(number_of_cores=1) # doctest: +SKIP
"""
pipeline_name = "srr_pipeline"
run_start_time = None
run_end_time = None
run_elapsed_time = None
bids_dir = None
output_dir = None
subject = None
wf = None
deltatTV = "0.75"
lambdaTV = "0.001"
primal_dual_loops = "20"
sr_id = None
session = None
m_stacks = None
# Custom interfaces options
m_skip_svr = None
m_skip_nlm_denoising = None
m_skip_stacks_ordering = None
m_do_refine_hr_mask = None
m_masks_derivatives_dir = None
use_manual_masks = False
m_masks_desc = None
openmp_number_of_cores = None
nipype_number_of_cores = None
def __init__(self,
bids_dir,
output_dir,
subject,
p_stacks=None,
sr_id=1,
session=None,
paramTV=None,
p_masks_derivatives_dir=None,
p_masks_desc=None,
p_dict_custom_interfaces=None,
openmp_number_of_cores=None,
nipype_number_of_cores=None):
"""Constructor of AnatomicalPipeline class instance."""
# BIDS processing parameters
self.bids_dir = bids_dir
self.output_dir = output_dir
self.subject = subject
self.sr_id = sr_id
self.session = session
self.m_stacks = p_stacks
self.openmp_number_of_cores = openmp_number_of_cores
self.nipype_number_of_cores = nipype_number_of_cores
# (default) sr tv parameters
if paramTV is None:
paramTV = dict()
self.deltatTV = paramTV["deltatTV"] if "deltatTV" in paramTV.keys(
) else 0.01
self.lambdaTV = paramTV["lambdaTV"] if "lambdaTV" in paramTV.keys(
) else 0.75
self.primal_dual_loops = paramTV[
"primal_dual_loops"] if "primal_dual_loops" in paramTV.keys(
) else 10
# Use manual/custom brain masks
# If masks directory is not specified use the automated brain extraction method.
self.m_masks_derivatives_dir = p_masks_derivatives_dir
self.use_manual_masks = True if self.m_masks_derivatives_dir is not None else False
self.m_masks_desc = p_masks_desc if self.use_manual_masks else None
# Custom interfaces and default values.
if p_dict_custom_interfaces is not None:
self.m_skip_svr = p_dict_custom_interfaces[
'skip_svr'] if 'skip_svr' in p_dict_custom_interfaces.keys(
) else False
self.m_do_refine_hr_mask = p_dict_custom_interfaces[
'do_refine_hr_mask'] if 'do_refine_hr_mask' in p_dict_custom_interfaces.keys(
) else False
self.m_skip_nlm_denoising = p_dict_custom_interfaces[
'skip_nlm_denoising'] if 'skip_nlm_denoising' in p_dict_custom_interfaces.keys(
) else False
self.m_skip_stacks_ordering = p_dict_custom_interfaces['skip_stacks_ordering'] if \
((self.m_stacks is not None) and ('skip_stacks_ordering' in p_dict_custom_interfaces.keys())) else False
else:
self.m_skip_svr = False
self.m_do_refine_hr_mask = False
self.m_skip_nlm_denoising = False
self.m_skip_stacks_ordering = False
def create_workflow(self):
"""Create the Niype workflow of the super-resolution pipeline.
It is composed of a succession of Nodes and their corresponding parameters,
where the output of node i goes to the input of node i+1.
"""
sub_ses = self.subject
if self.session is not None:
sub_ses = ''.join([sub_ses, '_', self.session])
if self.session is None:
wf_base_dir = os.path.join(
self.output_dir, '-'.join(["nipype", __nipype_version__]),
self.subject, "rec-{}".format(self.sr_id))
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk",
__version__]), self.subject)
else:
wf_base_dir = os.path.join(
self.output_dir, '-'.join(["nipype", __nipype_version__]),
self.subject, self.session, "rec-{}".format(self.sr_id))
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk", __version__]),
self.subject, self.session)
if not os.path.exists(wf_base_dir):
os.makedirs(wf_base_dir)
print("Process directory: {}".format(wf_base_dir))
# Initialization (Not sure we can control the name of nipype log)
if os.path.isfile(os.path.join(wf_base_dir, "pypeline.log")):
os.unlink(os.path.join(wf_base_dir, "pypeline.log"))
self.wf = Workflow(name=self.pipeline_name, base_dir=wf_base_dir)
config.update_config({
'logging': {
'log_directory': os.path.join(wf_base_dir),
'log_to_file': True
},
'execution': {
'remove_unnecessary_outputs': False,
'stop_on_first_crash': True,
'stop_on_first_rerun': False,
'crashfile_format': "txt",
'use_relative_paths': True,
'write_provenance': False
}
})
# Update nypipe logging with config
nipype_logging.update_logging(config)
# config.enable_provenance()
if self.use_manual_masks:
dg = Node(interface=DataGrabber(outfields=['T2ws', 'masks']),
name='data_grabber')
dg.inputs.base_directory = self.bids_dir
dg.inputs.template = '*'
dg.inputs.raise_on_empty = False
dg.inputs.sort_filelist = True
if self.session is not None:
t2ws_template = os.path.join(
self.subject, self.session, 'anat',
'_'.join([sub_ses, '*run-*', '*T2w.nii.gz']))
if self.m_masks_desc is not None:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat', '_'.join([
sub_ses, '*_run-*', '_desc-' + self.m_masks_desc,
'*mask.nii.gz'
]))
else:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat',
'_'.join([sub_ses, '*run-*', '*mask.nii.gz']))
else:
t2ws_template = os.path.join(self.subject, 'anat',
sub_ses + '*_run-*_T2w.nii.gz')
if self.m_masks_desc is not None:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, self.session, 'anat', '_'.join([
sub_ses, '*_run-*', '_desc-' + self.m_masks_desc,
'*mask.nii.gz'
]))
else:
masks_template = os.path.join(
'derivatives', self.m_masks_derivatives_dir,
self.subject, 'anat', sub_ses + '*_run-*_*mask.nii.gz')
dg.inputs.field_template = dict(T2ws=t2ws_template,
masks=masks_template)
brainMask = MapNode(
interface=IdentityInterface(fields=['out_file']),
name='brain_masks_bypass',
iterfield=['out_file'])
if self.m_stacks is not None:
custom_masks_filter = Node(
interface=preprocess.FilteringByRunid(),
name='custom_masks_filter')
custom_masks_filter.inputs.stacks_id = self.m_stacks
else:
dg = Node(interface=DataGrabber(outfields=['T2ws']),
name='data_grabber')
dg.inputs.base_directory = self.bids_dir
dg.inputs.template = '*'
dg.inputs.raise_on_empty = False
dg.inputs.sort_filelist = True
dg.inputs.field_template = dict(
T2ws=os.path.join(self.subject, 'anat', sub_ses +
'*_run-*_T2w.nii.gz'))
if self.session is not None:
dg.inputs.field_template = dict(T2ws=os.path.join(
self.subject, self.session, 'anat', '_'.join(
[sub_ses, '*run-*', '*T2w.nii.gz'])))
if self.m_stacks is not None:
t2ws_filter_prior_masks = Node(
interface=preprocess.FilteringByRunid(),
name='t2ws_filter_prior_masks')
t2ws_filter_prior_masks.inputs.stacks_id = self.m_stacks
brainMask = MapNode(interface=preprocess.BrainExtraction(),
name='brainExtraction',
iterfield=['in_file'])
brainMask.inputs.bids_dir = self.bids_dir
brainMask.inputs.in_ckpt_loc = pkg_resources.resource_filename(
"pymialsrtk",
os.path.join("data", "Network_checkpoints",
"Network_checkpoints_localization",
"Unet.ckpt-88000.index")).split('.index')[0]
brainMask.inputs.threshold_loc = 0.49
brainMask.inputs.in_ckpt_seg = pkg_resources.resource_filename(
"pymialsrtk",
os.path.join("data", "Network_checkpoints",
"Network_checkpoints_segmentation",
"Unet.ckpt-20000.index")).split('.index')[0]
brainMask.inputs.threshold_seg = 0.5
t2ws_filtered = Node(interface=preprocess.FilteringByRunid(),
name='t2ws_filtered')
masks_filtered = Node(interface=preprocess.FilteringByRunid(),
name='masks_filtered')
if not self.m_skip_stacks_ordering:
stacksOrdering = Node(interface=preprocess.StacksOrdering(),
name='stackOrdering')
else:
stacksOrdering = Node(
interface=IdentityInterface(fields=['stacks_order']),
name='stackOrdering')
stacksOrdering.inputs.stacks_order = self.m_stacks
if not self.m_skip_nlm_denoising:
nlmDenoise = MapNode(interface=preprocess.BtkNLMDenoising(),
name='nlmDenoise',
iterfield=['in_file', 'in_mask'])
nlmDenoise.inputs.bids_dir = self.bids_dir
# Sans le mask le premier correct slice intensity...
srtkCorrectSliceIntensity01_nlm = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity01_nlm',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity01_nlm.inputs.bids_dir = self.bids_dir
srtkCorrectSliceIntensity01_nlm.inputs.out_postfix = '_uni'
srtkCorrectSliceIntensity01 = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity01',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity01.inputs.bids_dir = self.bids_dir
srtkCorrectSliceIntensity01.inputs.out_postfix = '_uni'
srtkSliceBySliceN4BiasFieldCorrection = MapNode(
interface=preprocess.MialsrtkSliceBySliceN4BiasFieldCorrection(),
name='srtkSliceBySliceN4BiasFieldCorrection',
iterfield=['in_file', 'in_mask'])
srtkSliceBySliceN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
srtkSliceBySliceCorrectBiasField = MapNode(
interface=preprocess.MialsrtkSliceBySliceCorrectBiasField(),
name='srtkSliceBySliceCorrectBiasField',
iterfield=['in_file', 'in_mask', 'in_field'])
srtkSliceBySliceCorrectBiasField.inputs.bids_dir = self.bids_dir
# 4-modules sequence to be defined as a stage.
if not self.m_skip_nlm_denoising:
srtkCorrectSliceIntensity02_nlm = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity02_nlm',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity02_nlm.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization01_nlm = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization01_nlm')
srtkIntensityStandardization01_nlm.inputs.bids_dir = self.bids_dir
srtkHistogramNormalization_nlm = Node(
interface=preprocess.MialsrtkHistogramNormalization(),
name='srtkHistogramNormalization_nlm')
srtkHistogramNormalization_nlm.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization02_nlm = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization02_nlm')
srtkIntensityStandardization02_nlm.inputs.bids_dir = self.bids_dir
# 4-modules sequence to be defined as a stage.
srtkCorrectSliceIntensity02 = MapNode(
interface=preprocess.MialsrtkCorrectSliceIntensity(),
name='srtkCorrectSliceIntensity02',
iterfield=['in_file', 'in_mask'])
srtkCorrectSliceIntensity02.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization01 = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization01')
srtkIntensityStandardization01.inputs.bids_dir = self.bids_dir
srtkHistogramNormalization = Node(
interface=preprocess.MialsrtkHistogramNormalization(),
name='srtkHistogramNormalization')
srtkHistogramNormalization.inputs.bids_dir = self.bids_dir
srtkIntensityStandardization02 = Node(
interface=preprocess.MialsrtkIntensityStandardization(),
name='srtkIntensityStandardization02')
srtkIntensityStandardization02.inputs.bids_dir = self.bids_dir
srtkMaskImage01 = MapNode(interface=preprocess.MialsrtkMaskImage(),
name='srtkMaskImage01',
iterfield=['in_file', 'in_mask'])
srtkMaskImage01.inputs.bids_dir = self.bids_dir
srtkImageReconstruction = Node(
interface=reconstruction.MialsrtkImageReconstruction(),
name='srtkImageReconstruction')
srtkImageReconstruction.inputs.bids_dir = self.bids_dir
srtkImageReconstruction.inputs.sub_ses = sub_ses
srtkImageReconstruction.inputs.no_reg = self.m_skip_svr
srtkTVSuperResolution = Node(
interface=reconstruction.MialsrtkTVSuperResolution(),
name='srtkTVSuperResolution')
srtkTVSuperResolution.inputs.bids_dir = self.bids_dir
srtkTVSuperResolution.inputs.sub_ses = sub_ses
srtkTVSuperResolution.inputs.in_loop = self.primal_dual_loops
srtkTVSuperResolution.inputs.in_deltat = self.deltatTV
srtkTVSuperResolution.inputs.in_lambda = self.lambdaTV
srtkTVSuperResolution.inputs.use_manual_masks = self.use_manual_masks
srtkN4BiasFieldCorrection = Node(
interface=postprocess.MialsrtkN4BiasFieldCorrection(),
name='srtkN4BiasFieldCorrection')
srtkN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
if self.m_do_refine_hr_mask:
srtkHRMask = Node(
interface=postprocess.MialsrtkRefineHRMaskByIntersection(),
name='srtkHRMask')
srtkHRMask.inputs.bids_dir = self.bids_dir
else:
srtkHRMask = Node(interface=postprocess.BinarizeImage(),
name='srtkHRMask')
srtkMaskImage02 = Node(interface=preprocess.MialsrtkMaskImage(),
name='srtkMaskImage02')
srtkMaskImage02.inputs.bids_dir = self.bids_dir
# Build workflow : connections of the nodes
# Nodes ready : Linking now
if self.use_manual_masks:
if self.m_stacks is not None:
self.wf.connect(dg, "masks", custom_masks_filter,
"input_files")
self.wf.connect(custom_masks_filter, "output_files", brainMask,
"out_file")
else:
self.wf.connect(dg, "masks", brainMask, "out_file")
else:
if self.m_stacks is not None:
self.wf.connect(dg, "T2ws", t2ws_filter_prior_masks,
"input_files")
self.wf.connect(t2ws_filter_prior_masks, "output_files",
brainMask, "in_file")
else:
self.wf.connect(dg, "T2ws", brainMask, "in_file")
if not self.m_skip_stacks_ordering:
self.wf.connect(brainMask, "out_file", stacksOrdering,
"input_masks")
self.wf.connect(stacksOrdering, "stacks_order", t2ws_filtered,
"stacks_id")
self.wf.connect(dg, "T2ws", t2ws_filtered, "input_files")
self.wf.connect(stacksOrdering, "stacks_order", masks_filtered,
"stacks_id")
self.wf.connect(brainMask, "out_file", masks_filtered, "input_files")
if not self.m_skip_nlm_denoising:
self.wf.connect(t2ws_filtered,
("output_files", utils.sort_ascending), nlmDenoise,
"in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending), nlmDenoise,
"in_mask") ## Comment to match docker process
self.wf.connect(nlmDenoise, ("out_file", utils.sort_ascending),
srtkCorrectSliceIntensity01_nlm, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01_nlm, "in_mask")
self.wf.connect(t2ws_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity01, "in_mask")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkCorrectSliceIntensity01_nlm,
("out_file", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_file")
else:
self.wf.connect(srtkCorrectSliceIntensity01,
("out_file", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkSliceBySliceN4BiasFieldCorrection, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity01,
("out_file", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_file")
self.wf.connect(srtkSliceBySliceN4BiasFieldCorrection,
("out_fld_file", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_field")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkSliceBySliceCorrectBiasField, "in_mask")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkSliceBySliceN4BiasFieldCorrection,
("out_im_file", utils.sort_ascending),
srtkCorrectSliceIntensity02_nlm, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity02_nlm, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity02_nlm,
("out_file", utils.sort_ascending),
srtkIntensityStandardization01_nlm, "input_images")
self.wf.connect(srtkIntensityStandardization01_nlm,
("output_images", utils.sort_ascending),
srtkHistogramNormalization_nlm, "input_images")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkHistogramNormalization_nlm, "input_masks")
self.wf.connect(srtkHistogramNormalization_nlm,
("output_images", utils.sort_ascending),
srtkIntensityStandardization02_nlm, "input_images")
self.wf.connect(srtkSliceBySliceCorrectBiasField,
("out_im_file", utils.sort_ascending),
srtkCorrectSliceIntensity02, "in_file")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkCorrectSliceIntensity02, "in_mask")
self.wf.connect(srtkCorrectSliceIntensity02,
("out_file", utils.sort_ascending),
srtkIntensityStandardization01, "input_images")
self.wf.connect(srtkIntensityStandardization01,
("output_images", utils.sort_ascending),
srtkHistogramNormalization, "input_images")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkHistogramNormalization, "input_masks")
self.wf.connect(srtkHistogramNormalization,
("output_images", utils.sort_ascending),
srtkIntensityStandardization02, "input_images")
if not self.m_skip_nlm_denoising:
self.wf.connect(srtkIntensityStandardization02_nlm,
("output_images", utils.sort_ascending),
srtkMaskImage01, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkMaskImage01, "in_mask")
else:
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending),
srtkMaskImage01, "in_file")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending),
srtkMaskImage01, "in_mask")
self.wf.connect(srtkMaskImage01, "out_im_file",
srtkImageReconstruction, "input_images")
self.wf.connect(masks_filtered, "output_files",
srtkImageReconstruction, "input_masks")
self.wf.connect(stacksOrdering, "stacks_order",
srtkImageReconstruction, "stacks_order")
self.wf.connect(srtkIntensityStandardization02, "output_images",
srtkTVSuperResolution, "input_images")
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending),
srtkTVSuperResolution, "input_transforms")
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
srtkTVSuperResolution, "input_masks")
self.wf.connect(stacksOrdering, "stacks_order", srtkTVSuperResolution,
"stacks_order")
self.wf.connect(srtkImageReconstruction, "output_sdi",
srtkTVSuperResolution, "input_sdi")
if self.m_do_refine_hr_mask:
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending),
srtkHRMask, "input_images")
self.wf.connect(masks_filtered,
("output_files", utils.sort_ascending), srtkHRMask,
"input_masks")
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending),
srtkHRMask, "input_transforms")
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkHRMask,
"input_sr")
else:
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkHRMask,
"input_image")
self.wf.connect(srtkTVSuperResolution, "output_sr", srtkMaskImage02,
"in_file")
self.wf.connect(srtkHRMask, "output_srmask", srtkMaskImage02,
"in_mask")
self.wf.connect(srtkTVSuperResolution, "output_sr",
srtkN4BiasFieldCorrection, "input_image")
self.wf.connect(srtkHRMask, "output_srmask", srtkN4BiasFieldCorrection,
"input_mask")
# Datasinker
finalFilenamesGeneration = Node(
interface=postprocess.FilenamesGeneration(), name='filenames_gen')
finalFilenamesGeneration.inputs.sub_ses = sub_ses
finalFilenamesGeneration.inputs.sr_id = self.sr_id
finalFilenamesGeneration.inputs.use_manual_masks = self.use_manual_masks
self.wf.connect(stacksOrdering, "stacks_order",
finalFilenamesGeneration, "stacks_order")
datasink = Node(interface=DataSink(), name='data_sinker')
datasink.inputs.base_directory = final_res_dir
if not self.m_skip_stacks_ordering:
self.wf.connect(stacksOrdering, "report_image", datasink,
'figures.@stackOrderingQC')
self.wf.connect(stacksOrdering, "motion_tsv", datasink,
'anat.@motionTSV')
self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending),
datasink, 'anat.@LRmasks')
self.wf.connect(srtkIntensityStandardization02,
("output_images", utils.sort_ascending), datasink,
'anat.@LRsPreproc')
self.wf.connect(srtkImageReconstruction,
("output_transforms", utils.sort_ascending), datasink,
'xfm.@transforms')
self.wf.connect(finalFilenamesGeneration, "substitutions", datasink,
"substitutions")
self.wf.connect(srtkMaskImage01, ("out_im_file", utils.sort_ascending),
datasink, 'anat.@LRsDenoised')
self.wf.connect(srtkImageReconstruction, "output_sdi", datasink,
'anat.@SDI')
self.wf.connect(srtkN4BiasFieldCorrection, "output_image", datasink,
'anat.@SR')
self.wf.connect(srtkTVSuperResolution, "output_json_path", datasink,
'anat.@SRjson')
self.wf.connect(srtkTVSuperResolution, "output_sr_png", datasink,
'figures.@SRpng')
self.wf.connect(srtkHRMask, "output_srmask", datasink, 'anat.@SRmask')
def run(self, memory=None):
"""Execute the workflow of the super-resolution reconstruction pipeline.
Nipype execution engine will take care of the management and execution of
all processing steps involved in the super-resolution reconstruction pipeline.
Note that the complete execution graph is saved as a PNG image to support
transparency on the whole processing.
Parameters
----------
memory : int
Maximal memory used by the workflow
"""
# Use nipype.interface logger to print some information messages
iflogger = nipype_logging.getLogger('nipype.interface')
iflogger.info("**** Workflow graph creation ****")
self.wf.write_graph(dotfilename='graph.dot',
graph2use='colored',
format='png',
simple_form=True)
# Copy and rename the generated "graph.png" image
src = os.path.join(self.wf.base_dir, self.wf.name, 'graph.png')
if self.session is not None:
dst = os.path.join(
self.output_dir, '-'.join(["pymialsrtk", __version__]),
self.subject, self.session, 'figures',
f'{self.subject}_{self.session}_rec-SR_id-{self.sr_id}_desc-processing_graph.png'
)
else:
dst = os.path.join(
self.output_dir, '-'.join(["pymialsrtk", __version__]),
self.subject, 'figures',
f'{self.subject}_rec-SR_id-{self.sr_id}_desc-processing_graph.png'
)
# Create the figures/ and parent directories if they do not exist
figures_dir = os.path.dirname(dst)
os.makedirs(figures_dir, exist_ok=True)
# Make the copy
iflogger.info(f'\t > Copy {src} to {dst}...')
shutil.copy(src=src, dst=dst)
# Create dictionary of arguments passed to plugin_args
args_dict = {
'raise_insufficient': False,
'n_procs': self.nipype_number_of_cores
}
if (memory is not None) and (memory > 0):
args_dict['memory_gb'] = memory
iflogger.info("**** Processing ****")
# datetime object containing current start date and time
start = datetime.now()
self.run_start_time = start.strftime("%B %d, %Y / %H:%M:%S")
print(f" Start date / time : {self.run_start_time}")
# Execute the workflow
if self.nipype_number_of_cores > 1:
res = self.wf.run(plugin='MultiProc', plugin_args=args_dict)
else:
res = self.wf.run()
# Copy and rename the workflow execution log
src = os.path.join(self.wf.base_dir, "pypeline.log")
if self.session is not None:
dst = os.path.join(
self.output_dir, '-'.join(["pymialsrtk", __version__]),
self.subject, self.session, 'logs',
f'{self.subject}_{self.session}_rec-SR_id-{self.sr_id}_log.txt'
)
else:
dst = os.path.join(
self.output_dir, '-'.join(["pymialsrtk",
__version__]), self.subject, 'logs',
f'{self.subject}_rec-SR_id-{self.sr_id}_log.txt')
# Create the logs/ and parent directories if they do not exist
logs_dir = os.path.dirname(dst)
os.makedirs(logs_dir, exist_ok=True)
# Make the copy
iflogger.info(f'\t > Copy {src} to {dst}...')
shutil.copy(src=src, dst=dst)
# datetime object containing current end date and time
end = datetime.now()
self.run_end_time = end.strftime("%B %d, %Y / %H:%M:%S")
print(f" End date / time : {self.run_end_time}")
# Compute elapsed running time in minutes and seconds
duration = end - start
(minutes, seconds) = divmod(duration.total_seconds(), 60)
self.run_elapsed_time = f'{int(minutes)} minutes and {int(seconds)} seconds'
print(f" Elapsed time: {self.run_end_time}")
iflogger.info("**** Write dataset derivatives description ****")
for toolbox in ["pymialsrtk", "nipype"]:
write_bids_derivative_description(bids_dir=self.bids_dir,
deriv_dir=self.output_dir,
pipeline_name=toolbox)
iflogger.info("**** Super-resolution HTML report creation ****")
self.create_subject_report()
return res
def create_subject_report(self):
"""Create the HTML report"""
# Set main subject derivatives directory
if self.session is None:
sub_ses = self.subject
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk",
__version__]), self.subject)
else:
sub_ses = f'{self.subject}_{self.session}'
final_res_dir = os.path.join(self.output_dir,
'-'.join(["pymialsrtk", __version__]),
self.subject, self.session)
# Get the HTML report template
path = pkg_resources.resource_filename(
'pymialsrtk', "data/report/templates/template.html")
jinja_template_dir = os.path.dirname(path)
file_loader = FileSystemLoader(jinja_template_dir)
env = Environment(loader=file_loader)
template = env.get_template('template.html')
# Load main data derivatives necessary for the report
sr_nii_image = os.path.join(
final_res_dir, 'anat',
f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.nii.gz')
img = nib.load(sr_nii_image)
sx, sy, sz = img.header.get_zooms()
sr_json_metadata = os.path.join(
final_res_dir, 'anat',
f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.json')
with open(sr_json_metadata) as f:
sr_json_metadata = json.load(f)
workflow_image = os.path.join(
'..', 'figures',
f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-processing_graph.png')
sr_png_image = os.path.join(
'..', 'figures', f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.png')
motion_report_image = os.path.join(
'..', 'figures',
f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-motion_stats.png')
log_file = os.path.join('..', 'logs',
f'{sub_ses}_rec-SR_id-{self.sr_id}_log.txt')
# Create the text for {{subject}} and {{session}} fields in template
report_subject_text = f'{self.subject.split("-")[-1]}'
if self.session is not None:
report_session_text = f'{self.session.split("-")[-1]}'
else:
report_session_text = None
# Generate the report
report_html_content = template.render(
subject=report_subject_text,
session=report_session_text,
processing_datetime=self.run_start_time,
run_time=self.run_elapsed_time,
log=log_file,
sr_id=self.sr_id,
stacks=self.m_stacks,
svr="on" if not self.m_skip_svr else "off",
nlm_denoising="on" if not self.m_skip_nlm_denoising else "off",
stacks_ordering="on" if not self.m_skip_stacks_ordering else "off",
do_refine_hr_mask="on" if self.m_do_refine_hr_mask else "off",
use_auto_masks="on"
if self.m_masks_derivatives_dir is None else "off",
custom_masks_dir=self.m_masks_derivatives_dir
if self.m_masks_derivatives_dir is not None else None,
sr_resolution=f"{sx} x {sy} x {sz} mm<sup>3</sup>",
sr_json_metadata=sr_json_metadata,
workflow_graph=workflow_image,
sr_png_image=sr_png_image,
motion_report_image=motion_report_image,
version=__version__,
os=f'{platform.system()} {platform.release()}',
python=f'{sys.version}',
openmp_threads=self.openmp_number_of_cores,
nipype_threads=self.nipype_number_of_cores,
jinja_version=__jinja2_version__)
# Create the report directory if it does not exist
report_dir = os.path.join(final_res_dir, 'report')
os.makedirs(report_dir, exist_ok=True)
# Save the HTML report file
out_report_filename = os.path.join(report_dir, f'{sub_ses}.html')
print(f'\t* Save HTML report as {out_report_filename}...')
with open(out_report_filename, "w+") as file:
file.write(report_html_content)
'''
featreg_merge.py
script
Perform Feat preprocessing on given data files and then merge ouputs.
'''
from nipype.workflows.fmri.fsl import create_featreg_preproc
import nipype.interfaces.fsl as fsl
from nipype.pipeline import Workflow, Node
# get filelist from file
nifti_filelist = open('nifti_filelist.txt').read().splitlines()
featreg_merge = Workflow(name='featreg_merge')
preproc = create_featreg_preproc(highpass=True, whichvol='mean')
preproc.inputs.inputspec.func = nifti_filelist
preproc.inputs.inputspec.fwhm = 0
preproc.inputs.inputspec.highpass = 128./(2*2.5)
# preproc.base_dir = '/tmp/pre/working_dir'
# preproc.run()
merge = Node(
interface=fsl.utils.Merge(
dimension='t',
output_type='NIFTI_GZ',
merged_file='merged.nii.gz'
),
def create_featreg_merge(run, whichvol_glob, name="featregmerge"):
###########################################################################
#
# FEATREG_MERGE WORKFLOW
#
###########################################################################
featregmerge = Workflow(name=name)
inputnode = Node(
interface=util.IdentityInterface(fields=["in_sub", "in_hand", "run", "whichvol_glob"]), name="inputspec"
)
# inputnode.inputs.in_sub = 'GK011RZJA'
###########################################################################
#
# DATA GRABBER NODE
#
###########################################################################
ds = Node(DataGrabber(infields=["subject_id", "hand"], outfields=["func"]), name="datasource")
ds.inputs.base_directory = opap(base_directory)
ds.inputs.template = "%s/%s_Hand/*.nii*"
ds.inputs.sort_filelist = True
# ds.inputs.subject_id = 'GK011RZJA'
# ds.inputs.hand = 'Left'
featregmerge.connect(inputnode, "in_hand", ds, "hand")
featregmerge.connect(inputnode, "in_sub", ds, "subject_id")
"""
To print the list of files being taken uncomment the following lines.
"""
# functional_input = ds.run().outputs
# input_files = functional_input.get()['func']
# print input_files
###########################################################################
#
# CREATE FEAT REGISTRATION WORKFLOW NODE
#
###########################################################################
preproc = create_featreg_preproc(highpass=True, whichvol="first")
preproc.inputs.inputspec.fwhm = 0
preproc.inputs.inputspec.highpass = 128.0 / (2 * 2.5)
# remove_nodes takes list as an argument
preproc.remove_nodes([preproc.get_node("extractref")])
"""
preproc.disconnect(
preproc.get_node('extractref'), 'roi_file',
preproc.get_node('realign'), 'ref_file'
)
"""
featregmerge.connect(ds, "func", preproc, "inputspec.func")
###########################################################################
#
# MERGE NODE
#
###########################################################################
merge = Node(
interface=fsl.utils.Merge(dimension="t", output_type="NIFTI_GZ", merged_file="bold.nii.gz"), name="merge"
)
featregmerge.connect(preproc, "outputspec.highpassed_files", merge, "in_files")
masksnode = Node(
interface=fsl.utils.Merge(dimension="t", output_type="NIFTI_GZ", merged_file="masks_merged.nii.gz"),
name="masksnode",
)
featregmerge.connect(preproc, "outputspec.mask", masksnode, "in_files")
# ### SPLIT MERGED MASKS ##################################################
splitnode = Node(interface=fsl.utils.Split(dimension="t", output_type="NIFTI_GZ"), name="splitnode")
featregmerge.connect(masksnode, "merged_file", splitnode, "in_file")
return featregmerge
def create_fs_compatible_logb_workflow(name="LOGISMOSB", plugin_args=None, config=None):
"""
Create a workflow to run LOGISMOS-B from FreeSurfer Inputs
:param name:
:param plugin_args:
:param config:
:return:
"""
if not config:
config = read_json_config("fs_logb_config.json")
wf = Workflow(name)
inputspec = Node(
IdentityInterface(
[
"t1_file",
"t2_file",
"white",
"aseg",
"hemi",
"recoding_file",
"gm_proba",
"wm_proba",
"lut_file",
"hncma_atlas",
]
),
name="inputspec",
)
# convert the white mesh to a vtk file with scanner coordinates
to_vtk = Node(MRIsConvert(), name="WhiteVTK")
to_vtk.inputs.out_file = "white.vtk"
to_vtk.inputs.to_scanner = True
wf.connect(inputspec, "white", to_vtk, "in_file")
# convert brainslabels to nifti
aseg_to_nifti = Node(MRIConvert(), "ABCtoNIFTI")
aseg_to_nifti.inputs.out_file = "aseg.nii.gz"
aseg_to_nifti.inputs.out_orientation = "LPS"
wf.connect(inputspec, "aseg", aseg_to_nifti, "in_file")
# create brainslabels from aseg
aseg2brains = Node(
Function(["in_file", "recode_file", "out_file"], ["out_file"], recode_labelmap),
name="ConvertAseg2BRAINSLabels",
)
aseg2brains.inputs.out_file = "brainslabels.nii.gz"
wf.connect(
[
(inputspec, aseg2brains, [("recoding_file", "recode_file")]),
(aseg_to_nifti, aseg2brains, [("out_file", "in_file")]),
]
)
t1_to_nifti = Node(MRIConvert(), "T1toNIFTI")
t1_to_nifti.inputs.out_file = "t1.nii.gz"
t1_to_nifti.inputs.out_orientation = "LPS"
wf.connect(inputspec, "t1_file", t1_to_nifti, "in_file")
def t2_convert(in_file=None, reference_file=None, out_file=None):
"""
This function...
:param in_file:
:param reference_file:
:param out_file:
:return:
"""
import os
from nipype.interfaces.freesurfer import MRIConvert
from nipype.interfaces.traits_extension import Undefined
from nipype import Node
if in_file:
t2_to_nifti = Node(MRIConvert(), "T2toNIFTI")
t2_to_nifti.inputs.in_file = in_file
t2_to_nifti.inputs.out_file = os.path.abspath(out_file)
t2_to_nifti.inputs.out_orientation = "LPS"
if reference_file:
t2_to_nifti.inputs.reslice_like = reference_file
result = t2_to_nifti.run()
out_file = os.path.abspath(result.outputs.out_file)
else:
out_file = Undefined
return out_file
t2_node = Node(
Function(["in_file", "reference_file", "out_file"], ["out_file"], t2_convert),
name="T2Convert",
)
t2_node.inputs.out_file = "t2.nii.gz"
wf.connect(inputspec, "t2_file", t2_node, "in_file")
wf.connect(t1_to_nifti, "out_file", t2_node, "reference_file")
# convert raw t1 to lia
t1_to_ras = Node(MRIConvert(), "T1toRAS")
t1_to_ras.inputs.out_orientation = "LIA"
t1_to_ras.inputs.out_file = "t1_lia.mgz"
wf.connect(inputspec, "t1_file", t1_to_ras, "in_file")
# Create ones image for use when masking the white matter
ones = Node(
Function(["in_volume", "out_file"], ["out_file"], create_ones_image),
name="Ones_Image",
)
ones.inputs.out_file = "ones.mgz"
wf.connect(t1_to_ras, "out_file", ones, "in_volume")
# use the ones image to obtain a white matter mask
surfmask = Node(SurfaceMask(), name="WhiteMask")
surfmask.inputs.out_file = "white_ras.mgz"
wf.connect(ones, "out_file", surfmask, "in_volume")
wf.connect(inputspec, "white", surfmask, "in_surface")
surfmask_to_nifti = Node(MRIConvert(), "MasktoNIFTI")
surfmask_to_nifti.inputs.out_file = "white.nii.gz"
surfmask_to_nifti.inputs.out_orientation = "LPS"
wf.connect(surfmask, "out_file", surfmask_to_nifti, "in_file")
# create hemi masks
split = Node(SplitLabels(), name="SplitLabelMask")
split.inputs.out_file = "HemiBrainLabels.nii.gz"
wf.connect(
[
(aseg2brains, split, [("out_file", "in_file")]),
(inputspec, split, [("lut_file", "lookup_table")]),
(aseg_to_nifti, split, [("out_file", "labels_file")]),
(inputspec, split, [("hemi", "hemi")]),
]
)
dilate = Node(MultiLabelDilation(), "DilateLabels")
dilate.inputs.out_file = "DilatedBrainLabels.nii.gz"
dilate.inputs.radius = 1
wf.connect(split, "out_file", dilate, "in_file")
convert_label_map = Node(MRIConvert(), "ConvertLabelMapToMatchT1")
convert_label_map.inputs.resample_type = "nearest"
convert_label_map.inputs.out_file = "BrainLabelsFromAsegInT1Space.nii.gz"
wf.connect(t1_to_nifti, "out_file", convert_label_map, "reslice_like")
wf.connect(dilate, "out_file", convert_label_map, "in_file")
logb = Node(LOGISMOSB(), name="LOGISMOS-B")
logb.inputs.smoothnessConstraint = config["LOGISMOSB"]["smoothnessConstraint"]
logb.inputs.nColumns = config["LOGISMOSB"]["nColumns"]
logb.inputs.columnChoice = config["LOGISMOSB"]["columnChoice"]
logb.inputs.columnHeight = config["LOGISMOSB"]["columnHeight"]
logb.inputs.nodeSpacing = config["LOGISMOSB"]["nodeSpacing"]
logb.inputs.w = config["LOGISMOSB"]["w"]
logb.inputs.a = config["LOGISMOSB"]["a"]
logb.inputs.nPropagate = config["LOGISMOSB"]["nPropagate"]
if plugin_args:
logb.plugin_args = plugin_args
wf.connect(
[
(t1_to_nifti, logb, [("out_file", "t1_file")]),
(t2_node, logb, [("out_file", "t2_file")]),
(
inputspec,
logb,
[
("hemi", "basename"),
("hncma_atlas", "atlas_file"),
("wm_proba", "wm_proba_file"),
("gm_proba", "gm_proba_file"),
],
),
(to_vtk, logb, [("converted", "mesh_file")]),
(surfmask_to_nifti, logb, [("out_file", "wm_file")]),
(convert_label_map, logb, [("out_file", "brainlabels_file")]),
]
)
outputspec = Node(
IdentityInterface(["gmsurface_file", "wmsurface_file"]), name="outputspec"
)
wf.connect(
[
(
logb,
outputspec,
[
("gmsurface_file", "gmsurface_file"),
("wmsurface_file", "wmsurface_file"),
],
)
]
)
return wf
subject_template = 'GK'
###############################################################################
#
# CREATE MAIN WORKFLOW
#
###############################################################################
from nipype.pipeline import Workflow, Node
import nipype.interfaces.utility as util
from pymri.utils.paths_dirs_info import get_subject_names
flirt_apply_all_subs = Workflow(name='flirt_apply_all_subs')
inputsub = Node(
interface=util.IdentityInterface(
fields=['sub']
),
name='inputsub'
)
# inputsub.inputs.sub = ['GK011RZJA', 'GK012OHPA']
# inputsub.iterables = ('sub', ['GK011RZJA', 'GK012OHPA'])
inputsub.iterables = (
'sub', get_subject_names(base_directory, subject_template)
)
inputhand = Node(
interface=util.IdentityInterface(
def create_fs_logb_workflow_for_both_hemispheres(
name="FSLOGB", plugin_args=None, ml=False, config=None
):
"""
Creates a workflow that connects FreeSurfer with LOGISMOS-B
:param name:
:param plugin_args:
:param ml:
:param config:
:return:
"""
fslogb_wf = Workflow(name=name)
inputspec = Node(
IdentityInterface(
[
"recoding_file",
"lut_file",
"aseg_presurf",
"rawavg",
"t2_raw",
"lh_white",
"rh_white",
"hncma_atlas",
]
),
name="inputspec",
)
inputspec.inputs.recoding_file = get_local_file_location("abc_fs_equivelants.json")
inputspec.inputs.lut_file = get_local_file_location("FreeSurferColorLUT.csv")
# create outputspec with gm and wm surfaces
outputs = [
"lh_gm_surf_file",
"lh_wm_surf_file",
"rh_gm_surf_file",
"rh_wm_surf_file",
]
outputspec = Node(IdentityInterface(outputs), name="outputspec")
for hemi in ("lh", "rh"):
hemi_logb_wf = create_fs_compatible_logb_workflow(
"{0}_LOGBWF".format(hemi), plugin_args=plugin_args, config=config
)
hemi_logb_wf.inputs.inputspec.hemi = hemi
fslogb_wf.connect(
[
(
inputspec,
hemi_logb_wf,
[
("aseg_presurf", "inputspec.aseg"),
("rawavg", "inputspec.t1_file"),
("t2_raw", "inputspec.t2_file"),
("hncma_atlas", "inputspec.hncma_atlas"),
("{0}_white".format(hemi), "inputspec.white"),
],
),
(
inputspec,
hemi_logb_wf,
[
("recoding_file", "inputspec.recoding_file"),
("lut_file", "inputspec.lut_file"),
],
),
]
)
# move the outputs from logb to the outputspec
fslogb_wf.connect(
[
(
hemi_logb_wf,
outputspec,
[
("outputspec.gmsurface_file", "{0}_gm_surf_file".format(hemi)),
("outputspec.wmsurface_file", "{0}_wm_surf_file".format(hemi)),
],
)
]
)
return fslogb_wf
# Get mask from FSLDIR location
mask_file = \
os.environ['FSLDIR'] + '/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'
###############################################################################
#
# MASK MULTIPLE WORKFLOW
#
###############################################################################
from nipype.pipeline import Workflow, Node
import nipype.interfaces.io as nio
from os.path import abspath as opap
mask_multiple = Workflow(name='mask_multiple')
grabber = nio.DataGrabber()
grabber.inputs.base_directory = opap(base_directory)
grabber.inputs.template = '*.nii*'
grabber.inputs.sort_filelist = True
grabbed = grabber.run()
rois_filelist = grabbed.outputs.outfiles
###############################################################################
#
# MASK SINGLE WORKFLOW
#
###############################################################################
apply_mask_multiple = Workflow(name='apply_mask_multiple')
def create_logb_workflow(name="LOGISMOSB_WF", master_config=None, plugin_args=None):
"""
This function...
:param name:
:param master_config:
:param plugin_args:
:return:
"""
logb_wf = Workflow(name=name)
config = read_json_config("config.json")
config["atlas_info"] = get_local_file_location(config["atlas_info"])
inputs_node = Node(
IdentityInterface(
fields=[
"t1_file",
"t2_file",
"posterior_files",
"joint_fusion_file",
"brainlabels_file",
"hncma_atlas",
]
),
name="inputspec",
)
inputs_node.run_without_submitting = True
# ensure that t1 and t2 are in the same voxel lattice
input_t2 = Node(BRAINSResample(), "ResampleInputT2Volume")
input_t2.inputs.outputVolume = "t2_resampled.nii.gz"
input_t2.inputs.pixelType = "ushort"
input_t2.inputs.interpolationMode = "Linear"
logb_wf.connect(
[
(
inputs_node,
input_t2,
[("t1_file", "referenceVolume"), ("t2_file", "inputVolume")],
)
]
)
white_matter_masking_node = Node(interface=WMMasking(), name="WMMasking")
white_matter_masking_node.inputs.dilation = config["WMMasking"]["dilation"]
white_matter_masking_node.inputs.csf_threshold = config["WMMasking"][
"csf_threshold"
]
if master_config and master_config["labelmap_colorlookup_table"]:
white_matter_masking_node.inputs.atlas_info = master_config[
"labelmap_colorlookup_table"
]
else:
white_matter_masking_node.inputs.atlas_info = config["atlas_info"]
logb_wf.connect(
[
(
inputs_node,
white_matter_masking_node,
[
("posterior_files", "posterior_files"),
("joint_fusion_file", "atlas_file"),
("brainlabels_file", "brainlabels_file"),
("hncma_atlas", "hncma_file"),
],
)
]
)
gm_labels = Node(interface=CreateGMLabelMap(), name="GM_Labelmap")
gm_labels.inputs.atlas_info = config["atlas_info"]
logb_wf.connect([(inputs_node, gm_labels, [("joint_fusion_file", "atlas_file")])])
logismosb_output_node = create_output_spec(
["wmsurface_file", "gmsurface_file"],
config["hemisphere_names"],
name="outputspec",
)
for hemisphere in config["hemisphere_names"]:
genus_zero_filter = Node(
interface=GenusZeroImageFilter(),
name="{0}_GenusZeroImageFilter".format(hemisphere),
)
genus_zero_filter.inputs.connectivity = config["GenusZeroImageFilter"][
"connectivity"
]
genus_zero_filter.inputs.biggestComponent = config["GenusZeroImageFilter"][
"biggestComponent"
]
genus_zero_filter.inputs.connectedComponent = config["GenusZeroImageFilter"][
"connectedComponent"
]
genus_zero_filter.inputs.out_mask = "{0}_genus_zero_white_matter.nii.gz".format(
hemisphere
)
logb_wf.connect(
[
(
white_matter_masking_node,
genus_zero_filter,
[("{0}_wm".format(hemisphere), "in_file")],
)
]
)
surface_generation = Node(
interface=BRAINSSurfaceGeneration(),
name="{0}_BRAINSSurfaceGeneration".format(hemisphere),
)
surface_generation.inputs.smoothSurface = config["BRAINSSurfaceGeneration"][
"smoothSurface"
]
surface_generation.inputs.numIterations = config["BRAINSSurfaceGeneration"][
"numIterations"
]
surface_generation.inputs.out_file = "{0}_white_matter_surface.vtk".format(
hemisphere
)
logb_wf.connect(
[(genus_zero_filter, surface_generation, [("out_file", "in_file")])]
)
logismosb = Node(interface=LOGISMOSB(), name="{0}_LOGISMOSB".format(hemisphere))
logismosb.inputs.smoothnessConstraint = config["LOGISMOSB"][
"smoothnessConstraint"
]
logismosb.inputs.nColumns = config["LOGISMOSB"]["nColumns"]
logismosb.inputs.columnChoice = config["LOGISMOSB"]["columnChoice"]
logismosb.inputs.columnHeight = config["LOGISMOSB"]["columnHeight"]
logismosb.inputs.nodeSpacing = config["LOGISMOSB"]["nodeSpacing"]
logismosb.inputs.w = config["LOGISMOSB"]["w"]
logismosb.inputs.a = config["LOGISMOSB"]["a"]
logismosb.inputs.nPropagate = config["LOGISMOSB"]["nPropagate"]
logismosb.inputs.basename = hemisphere
if config["LOGISMOSB"]["thickRegions"]:
logismosb.inputs.thick_regions = config["LOGISMOSB"]["thickRegions"]
else:
logismosb.inputs.useHNCMALabels = True
if plugin_args:
logismosb.plugin_args = plugin_args
logb_wf.connect(
[
(
inputs_node,
logismosb,
[("t1_file", "t1_file"), ("hncma_atlas", "atlas_file")],
),
(input_t2, logismosb, [("outputVolume", "t2_file")]),
(genus_zero_filter, logismosb, [("out_file", "wm_file")]),
(surface_generation, logismosb, [("out_file", "mesh_file")]),
(
white_matter_masking_node,
logismosb,
[("{0}_boundary".format(hemisphere), "brainlabels_file")],
),
(
logismosb,
logismosb_output_node,
[
("gmsurface_file", "{0}_gmsurface_file".format(hemisphere)),
("wmsurface_file", "{0}_wmsurface_file".format(hemisphere)),
],
),
]
)
return logb_wf
def petpvc_mask(wf_name="petpvc_mask"):
""" A Workflow that returns a 4D merge of 4 volumes for PETPVC: GM, WM, CSF and background.
Parameters
----------
wf_name: str
The name of the workflow.
Nipype.Inputs
-------------
pvcmask_input.tissues: list of existing files
List of tissue files in anatomical space, the 3 file
paths must be in this order: GM, WM, CSF
Nipype.Outputs
--------------
pvcmask_output.petpvc_mask: existing file
A 4D volume file with these maps in order: GM, WM, CSF, background
pvcmask_output.brain_mask: existing file
A mask that is a binarised sum of the tissues file with fslmaths.
Can be used as brain mask in anatomical space for the PET image.
Returns
-------
wf: nipype Workflow
"""
# define nodes
# specify input and output fields
in_fields = ["tissues"]
out_fields = [
"petpvc_mask",
"brain_mask",
]
# input
pvcmask_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="pvcmask_input")
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"],
mandatory_inputs=True),
name="tissues")
merge_list = setup_node(Merge(4), name="merge_list")
# maths for background
img_bkg = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='background')
img_bkg.inputs.out_file = "tissue_bkg.nii.gz"
img_bkg.inputs.formula = "np.maximum((-((gm + wm + csf) - 1)), 0)"
# maths for brain mask
brain_mask = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='brain_mask')
brain_mask.inputs.out_file = "tissues_brain_mask.nii.gz"
brain_mask.inputs.formula = "np.abs(gm + wm + csf) > 0"
# concat the tissues images and the background for PETPVC
merge_tissues = setup_node(Function(
function=concat_imgs,
input_names=["in_files"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='merge_tissues')
merge_tissues.inputs.out_file = "petpvc_mask.nii.gz"
# output
pvcmask_output = setup_node(IdentityInterface(fields=out_fields),
name="pvcmask_output")
# Create the workflow object
wf = Workflow(name=wf_name)
# Connect the nodes
wf.connect([
# separate [GM, WM, CSF] into [GM] and [WM, CSF]
(pvcmask_input, tissues, [(("tissues", selectindex, 0), "gm"),
(("tissues", selectindex, 1), "wm"),
(("tissues", selectindex, 2), "csf")]),
(tissues, img_bkg, [("gm", "gm"), ("wm", "wm"), ("csf", "csf")]),
(tissues, brain_mask, [("gm", "gm"), ("wm", "wm"), ("csf", "csf")]),
(tissues, merge_list, [("gm", "in1"), ("wm", "in2"), ("csf", "in3")]),
# create a list of [GM, WM, CSF, BKG]
(img_bkg, merge_list, [("out_file", "in4")]),
# merge into 4D: [GM, WM, CSF, BKG]
(merge_list, merge_tissues, [("out", "in_files")]),
# output
(merge_tissues, pvcmask_output, [("out_file", "petpvc_mask")]),
(brain_mask, pvcmask_output, [("out_file", "brain_mask")]),
])
return wf
def get_wf_tissue_priors(name='wf_tissue_priors3'):
'''
This Function gives a workflow that Resamples the tissue priors and then thresholds it at 0.5
'''
# csf_tissue_prior_path, gm_tissue_prior_path, wm_tissue_prior_path,
# threshold = 0.5
wf_tissue_priors = Workflow(name=name)
inputspec = Node(IdentityInterface(fields=['csf_tissue_prior_path', 'wm_tissue_prior_path',
'threshold','std2func_mat_path', 'reference_func_file_path']),
name="inputspec")
'''
# 'gm_tissue_prior_path',
resample_tissue_prior_csf = Node(Resample(voxel_size=(3, 3, 3), resample_mode='Cu', # cubic interpolation
outputtype='NIFTI'),
name="resample_tissue_prior_csf")
# resample_tissue_prior_gm = Node(Resample(voxel_size=(3, 3, 3), resample_mode='Cu', # cubic interpolation
# outputtype='NIFTI'),
# name="resample_tissue_prior_gm")
resample_tissue_prior_wm = Node(Resample(voxel_size=(3, 3, 3), resample_mode='Cu', # cubic interpolation
outputtype='NIFTI'),
name="resample_tissue_prior_wm")
wf_tissue_priors.connect(inputspec, 'csf_tissue_prior_path', resample_tissue_prior_csf, 'in_file' )
# wf_tissue_priors.connect(inputspec, 'gm_tissue_prior_path', resample_tissue_prior_gm, 'in_file' )
wf_tissue_priors.connect(inputspec, 'wm_tissue_prior_path', resample_tissue_prior_wm, 'in_file' )
'''
# # Invert the func2anat matrix to get anat2func
# inv_mat = Node(ConvertXFM(invert_xfm=True), name='inv_mat')
# wf_tissue_priors.connect(inputspec, 'func2anat_mat_path', inv_mat, 'in_file')
# Transform the tissue priors to the functional space using the inverse matrix
std2func_xform_csf_prior = Node(FLIRT(output_type='NIFTI',
apply_xfm=True, interp='sinc'), name='std2func_xform_csf_prior')
wf_tissue_priors.connect(inputspec, 'reference_func_file_path', std2func_xform_csf_prior, 'reference')
wf_tissue_priors.connect(inputspec, 'std2func_mat_path', std2func_xform_csf_prior, 'in_matrix_file')
std2func_xform_wm_prior = Node(FLIRT(output_type='NIFTI',
apply_xfm=True, interp='sinc'), name='std2func_xform_wm_prior')
wf_tissue_priors.connect(inputspec, 'reference_func_file_path', std2func_xform_wm_prior, 'reference')
wf_tissue_priors.connect(inputspec, 'std2func_mat_path', std2func_xform_wm_prior, 'in_matrix_file')
# Transformed the priors
# Get the input in_file(s) of the std2func_xform_csf and std2func_xform_wm from the old workspace
wf_tissue_priors.connect(inputspec, 'csf_tissue_prior_path', std2func_xform_csf_prior, 'in_file')
wf_tissue_priors.connect(inputspec, 'wm_tissue_prior_path', std2func_xform_wm_prior, 'in_file')
# Threshold
def get_opstring(threshold, tissue_type):
if tissue_type == 'csf':
max = 216 # 216 is the highest intensity of the resampled afni output for CSF
elif tissue_type == 'wm':
max = 253 # 253 is the highest intensity of the resampled afni output for WM
threshold = int(threshold * max)
op = '-thr '+str(threshold)+' -bin'
return op
# ----- CSF ------
threshold_csf = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_csf')
wf_tissue_priors.connect(inputspec, ('threshold', get_opstring, 'csf'), threshold_csf, 'op_string' )
wf_tissue_priors.connect(std2func_xform_csf_prior, 'out_file', threshold_csf, 'in_file')
# ------- GM --------
# threshold_gm = Node(interface=ImageMaths(suffix='_thresh'),
# name='threshold_gm')
# wf_tissue_priors.connect(inputspec, ('threshold', get_opstring), threshold_gm, 'op_string' )
# wf_tissue_priors.connect(resample_tissue_prior_gm, 'out_file', threshold_gm, 'in_file')
# -------- WM --------
threshold_wm = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_wm')
wf_tissue_priors.connect(inputspec, ('threshold', get_opstring, 'wm'), threshold_wm, 'op_string' )
wf_tissue_priors.connect(std2func_xform_wm_prior, 'out_file', threshold_wm, 'in_file')
# -------------------
outputspec = Node(IdentityInterface(fields=['csf_tissue_prior_path', 'wm_tissue_prior_path', 'threshold']),
name="outputspec")
# , 'gm_tissue_prior_path'
wf_tissue_priors.connect(threshold_csf, 'out_file', outputspec, 'csf_tissue_prior_path')
# wf_tissue_priors.connect(threshold_gm, 'out_file', outputspec, 'gm_tissue_prior_path')
wf_tissue_priors.connect(threshold_wm, 'out_file', outputspec, 'wm_tissue_prior_path')
return wf_tissue_priors
subjects_list = [sub for sub in subjects_list if subject_template in sub]
return subjects_list
###############################################################################
#
# CREATE MAIN WORKFLOW
#
###############################################################################
from nipype.pipeline import Workflow, Node
import nipype.interfaces.utility as util
mvpa_preproc = Workflow(name=workflow_name)
inputsub = Node(
interface=util.IdentityInterface(
fields=['sub']
),
name='inputsub'
)
# inputsub.inputs.sub = ['GK011RZJA', 'GK012OHPA']
# inputsub.iterables = ('sub', ['GK011RZJA', 'GK012OHPA'])
inputsub.iterables = (
'sub', get_subject_names(base_directory, subject_template)
)
inputhand = Node(
interface=util.IdentityInterface(
def create_fs_compatible_logb_workflow(name="LOGISMOSB",
plugin_args=None,
config=None):
"""Create a workflow to run LOGISMOS-B from FreeSurfer Inputs"""
if not config:
config = read_json_config("fs_logb_config.json")
wf = Workflow(name)
inputspec = Node(IdentityInterface([
't1_file', 't2_file', 'white', 'aseg', 'hemi', 'recoding_file',
'gm_proba', 'wm_proba', 'lut_file', 'hncma_atlas'
]),
name="inputspec")
# convert the white mesh to a vtk file with scanner coordinates
to_vtk = Node(MRIsConvert(), name="WhiteVTK")
to_vtk.inputs.out_file = "white.vtk"
to_vtk.inputs.to_scanner = True
wf.connect(inputspec, 'white', to_vtk, 'in_file')
# convert brainslabels to nifti
aseg_to_nifti = Node(MRIConvert(), "ABCtoNIFTI")
aseg_to_nifti.inputs.out_file = "aseg.nii.gz"
aseg_to_nifti.inputs.out_orientation = "LPS"
wf.connect(inputspec, 'aseg', aseg_to_nifti, 'in_file')
# create brainslabels from aseg
aseg2brains = Node(Function(['in_file', 'recode_file', 'out_file'],
['out_file'], recode_labelmap),
name="ConvertAseg2BRAINSLabels")
aseg2brains.inputs.out_file = "brainslabels.nii.gz"
wf.connect([(inputspec, aseg2brains, [('recoding_file', 'recode_file')]),
(aseg_to_nifti, aseg2brains, [('out_file', 'in_file')])])
t1_to_nifti = Node(MRIConvert(), "T1toNIFTI")
t1_to_nifti.inputs.out_file = "t1.nii.gz"
t1_to_nifti.inputs.out_orientation = "LPS"
wf.connect(inputspec, 't1_file', t1_to_nifti, 'in_file')
def t2_convert(in_file=None, reference_file=None, out_file=None):
import os
from nipype.interfaces.freesurfer import MRIConvert
from nipype.interfaces.traits_extension import Undefined
from nipype import Node
if in_file:
t2_to_nifti = Node(MRIConvert(), "T2toNIFTI")
t2_to_nifti.inputs.in_file = in_file
t2_to_nifti.inputs.out_file = os.path.abspath(out_file)
t2_to_nifti.inputs.out_orientation = "LPS"
if reference_file:
t2_to_nifti.inputs.reslice_like = reference_file
result = t2_to_nifti.run()
out_file = os.path.abspath(result.outputs.out_file)
else:
out_file = Undefined
return out_file
t2_node = Node(Function(['in_file', 'reference_file', 'out_file'],
['out_file'], t2_convert),
name="T2Convert")
t2_node.inputs.out_file = "t2.nii.gz"
wf.connect(inputspec, 't2_file', t2_node, 'in_file')
wf.connect(t1_to_nifti, 'out_file', t2_node, 'reference_file')
# convert raw t1 to lia
t1_to_ras = Node(MRIConvert(), "T1toRAS")
t1_to_ras.inputs.out_orientation = "LIA"
t1_to_ras.inputs.out_file = "t1_lia.mgz"
wf.connect(inputspec, 't1_file', t1_to_ras, 'in_file')
# Create ones image for use when masking the white matter
ones = Node(Function(['in_volume', 'out_file'], ['out_file'],
create_ones_image),
name="Ones_Image")
ones.inputs.out_file = "ones.mgz"
wf.connect(t1_to_ras, 'out_file', ones, 'in_volume')
# use the ones image to obtain a white matter mask
surfmask = Node(SurfaceMask(), name="WhiteMask")
surfmask.inputs.out_file = "white_ras.mgz"
wf.connect(ones, 'out_file', surfmask, 'in_volume')
wf.connect(inputspec, 'white', surfmask, 'in_surface')
surfmask_to_nifti = Node(MRIConvert(), "MasktoNIFTI")
surfmask_to_nifti.inputs.out_file = "white.nii.gz"
surfmask_to_nifti.inputs.out_orientation = "LPS"
wf.connect(surfmask, 'out_file', surfmask_to_nifti, 'in_file')
# create hemi masks
split = Node(SplitLabels(), name="SplitLabelMask")
split.inputs.out_file = "HemiBrainLabels.nii.gz"
wf.connect([(aseg2brains, split, [('out_file', 'in_file')]),
(inputspec, split, [('lut_file', 'lookup_table')]),
(aseg_to_nifti, split, [('out_file', 'labels_file')]),
(inputspec, split, [('hemi', 'hemi')])])
dilate = Node(MultiLabelDilation(), "DilateLabels")
dilate.inputs.out_file = "DilatedBrainLabels.nii.gz"
dilate.inputs.radius = 1
wf.connect(split, 'out_file', dilate, 'in_file')
convert_label_map = Node(MRIConvert(), "ConvertLabelMapToMatchT1")
convert_label_map.inputs.resample_type = "nearest"
convert_label_map.inputs.out_file = "BrainLabelsFromAsegInT1Space.nii.gz"
wf.connect(t1_to_nifti, 'out_file', convert_label_map, 'reslice_like')
wf.connect(dilate, 'out_file', convert_label_map, 'in_file')
logb = Node(LOGISMOSB(), name="LOGISMOS-B")
logb.inputs.smoothnessConstraint = config['LOGISMOSB'][
'smoothnessConstraint']
logb.inputs.nColumns = config['LOGISMOSB']['nColumns']
logb.inputs.columnChoice = config['LOGISMOSB']['columnChoice']
logb.inputs.columnHeight = config['LOGISMOSB']['columnHeight']
logb.inputs.nodeSpacing = config['LOGISMOSB']['nodeSpacing']
logb.inputs.w = config['LOGISMOSB']['w']
logb.inputs.a = config['LOGISMOSB']['a']
logb.inputs.nPropagate = config['LOGISMOSB']['nPropagate']
if plugin_args:
logb.plugin_args = plugin_args
wf.connect([(t1_to_nifti, logb, [('out_file', 't1_file')]),
(t2_node, logb, [('out_file', 't2_file')]),
(inputspec, logb, [('hemi', 'basename'),
('hncma_atlas', 'atlas_file'),
('wm_proba', 'wm_proba_file'),
('gm_proba', 'gm_proba_file')]),
(to_vtk, logb, [('converted', 'mesh_file')]),
(surfmask_to_nifti, logb, [('out_file', 'wm_file')]),
(convert_label_map, logb, [('out_file', 'brainlabels_file')])])
outputspec = Node(IdentityInterface(['gmsurface_file', 'wmsurface_file']),
name="outputspec")
wf.connect([(logb, outputspec, [('gmsurface_file', 'gmsurface_file'),
('wmsurface_file', 'wmsurface_file')])])
return wf
# composite transform
dictWarps = {'transforms': [], 'outpath': []}
for strWarpDir in lsWarpDirs:
strAffinePath = glob.glob(os.path.join(strWarpDir, 'out_matrix',
'*.mat'))[0]
# Remove rigid body components (translation and rotation) which don't
# contribute meaningful variation
strAffinePath = remove_rigidbody(strAffinePath)
# We use the inverse warp field, which contains the nonlinear transformation from MNI->subject
strNonlinearPath = glob.glob(
os.path.join(strWarpDir, 'inverse_warp_field', '*.nii.gz'))[0]
dictWarps['transforms'].append([strNonlinearPath, strAffinePath])
dictWarps['outpath'].append(
os.path.join(strWarpDir, 'composite_to_mni.nii.gz'))
# Use ANTs ApplyTransforms to compose the transforms
antstool = MapNode(ants.ApplyTransforms(input_image=TEMPLATE,
reference_image=TEMPLATE,
interpolation='BSpline',
invert_transform_flags=[False, True],
print_out_composite_warp_file=True),
name='applytransforms',
iterfield=['output_image', 'transforms'])
antstool.inputs.output_image = dictWarps['outpath']
antstool.inputs.transforms = dictWarps['transforms']
# Create and run nipype workflow
wf = Workflow('composite_transforms')
wf.add_nodes([antstool])
wf.run(plugin='MultiProc', plugin_args={'n_procs': PIPELINE_JOBS})
def _skullstrip_register_func(self, input_img, output_path):
output_path = os.path.abspath(output_path)
workflow = Workflow('register_func', base_dir=output_path)
# Compute mean image
meanimage = Node(fsl.MeanImage(dimension='T', output_type='NIFTI_GZ'),
name='mean')
meanimage.inputs.in_file = input_img
# Skull strip
funcstrip = skullstrip.make_func_mask_workflow(base_dir=output_path)
workflow.connect(meanimage, 'out_file', funcstrip,
'inputnode.mean_file')
# Register with EPI template.
register = Node(ants.Registration(
fixed_image=strEPITemplatePath,
transforms=['Translation', 'Rigid', 'Affine', 'SyN'],
transform_parameters=[(0.1, ), (0.1, ), (0.1, ), (0.2, 3.0, 0.0)],
number_of_iterations=([[1000, 1000, 1000]] * 3 + [[100, 50, 30]]),
dimension=3,
write_composite_transform=True,
collapse_output_transforms=False,
metric=['Mattes'] * 3 + [['Mattes', 'CC']],
metric_weight=[1, 1, 1, [0.5, 0.5]],
radius_or_number_of_bins=[32] * 3 + [[32, 4]],
sampling_strategy=['Regular'] * 3 + [[None, None]],
sampling_percentage=[0.3] * 3 + [[None, None]],
convergence_threshold=[1e-8] * 3 + [-0.01],
convergence_window_size=[20, 20, 20, 5],
smoothing_sigmas=[[4, 2, 1]] * 3 + [[1, 0.5, 0]],
sigma_units=['vox'] * 4,
shrink_factors=[[6, 4, 2], [3, 2, 1], [3, 2, 1], [4, 2, 1]],
use_estimate_learning_rate_once=[True] * 4,
use_histogram_matching=[False] * 3 + [True],
initial_moving_transform_com=1,
terminal_output='file',
num_threads=self.n_ants_jobs),
name='register',
mem_gb=16,
n_procs=self.n_ants_jobs)
workflow.connect(funcstrip, 'outputnode.masked_file', register,
'moving_image')
# Apply transformation to the entire image timeseries
transform = Node(ants.ApplyTransforms(
reference_image=strEPITemplatePath,
input_image=input_img,
float=True,
output_image=os.path.join(output_path,
'registered_func_Warped.nii.gz'),
interpolation='BSpline',
interpolation_parameters=(5, ),
input_image_type=3,
num_threads=self.n_ants_jobs),
name='apply_transforms')
transform.interface.num_threads = self.n_ants_jobs
workflow.connect(register, 'composite_transform', transform,
'transforms')
workflow.run()
return os.path.join(output_path, 'registered_func_Warped.nii.gz')