def test_JSONFileSink_outputs():
output_map = dict(out_file=dict(), )
outputs = JSONFileSink.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_JSONFileSink_outputs():
output_map = dict(out_file=dict(),
)
outputs = JSONFileSink.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_JSONFileSink_inputs():
input_map = dict(
_outputs=dict(usedefault=True, ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_dict=dict(usedefault=True, ),
out_file=dict(),
)
inputs = JSONFileSink.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_JSONFileSink_inputs():
input_map = dict(_outputs=dict(usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_dict=dict(usedefault=True,
),
out_file=dict(),
)
inputs = JSONFileSink.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def __init__(self, parent, dir_dic, bids):
super().__init__(parent, dir_dic, bids)
# Create interfaces ============================================================================================
# BET
T1w_BET = Node(BET(), name="T1w_BET")
T1w_BET.btn_string = 'T1w Brain Extraction'
self.interfaces.append(T1w_BET)
T1w_gad_BET = Node(BET(), name="T1w_gad_BET")
T1w_gad_BET.btn_string = 'T1w Gadolinium Enhanced Brain Extraction'
self.interfaces.append(T1w_gad_BET)
T2w_dbs_BET = Node(BET(), name="T2w_dbs_BET")
T2w_dbs_BET.btn_string = 'T2w DBS Acquisition Brain Extraction'
self.interfaces.append(T2w_dbs_BET)
dwi_BET = Node(BET(), name="dwi_BET")
dwi_BET.btn_string = 'dwi Brain Extraction'
self.interfaces.append(dwi_BET)
# BFC
T1w_BFC = Node(N4BiasFieldCorrection(), name="T1w_BFC")
T1w_BFC.btn_string = 'T1w Bias Field Correction'
self.interfaces.append(T1w_BFC)
# Split
dwi_ROI_b0 = Node(ExtractROI(), name="dwi_ROI_b0")
dwi_ROI_b0.btn_string = 'dwi Extract b0'
self.interfaces.append(dwi_ROI_b0)
# Eddy current correction
dwi_Eddy = Node(Eddy(), name="dwi_Eddy")
dwi_Eddy.btn_string = 'dwi Eddy Current Correction'
self.interfaces.append(dwi_Eddy)
# Distortion correction
# as this section is script/comment heavy it was put into a function
self.distortion_correction_workflow()
# Data output (i.e. sink) ======================================================================================
self.sink = Node(DataSink(), name="sink")
self.sink.btn_string = 'data sink'
self.sink.inputs.base_directory = self.dir_dic['data_dir']
self.jsink = Node(JSONFileSink(), name="jsink")
self.jsink.btn_string = 'json sink'
self.jsink.inputs.base_directory = self.dir_dic['data_dir']
# Initialize workflow ==========================================================================================
self.wf = Workflow(name='pre_processing')
# T1w BET to ants N4BiasFieldCorrection
self.wf.connect([(self.return_interface("T1w_BET"),
self.return_interface("T1w_BFC"),
[("out_file", "input_image")])])
self.wf.connect([(self.return_interface("T1w_BET"),
self.return_interface("T1w_BFC"), [("mask_file",
"mask_image")])])
# Eddy
self.wf.connect([(self.return_interface("dwi_BET"),
self.return_interface("dwi_Eddy"), [("out_file",
"in_file")])])
self.wf.connect([(self.return_interface("dwi_BET"),
self.return_interface("dwi_Eddy"), [("mask_file",
"in_mask")])])
# ROI b0
self.wf.connect([(self.return_interface("dwi_Eddy"),
self.return_interface("dwi_ROI_b0"),
[("out_corrected", "in_file")])])
# Distortion Correction:
# b0_T1_Reg:
# -i: moving image
# -r: T1
# -x: T1 mask
self.wf.connect([(self.return_interface("dwi_ROI_b0"),
self.return_interface("b0_T1w_Reg"),
[("roi_file", "moving_image")])])
self.wf.connect([(self.return_interface("T1w_BFC"),
self.return_interface("b0_T1w_Reg"),
[("output_image", "fixed_image")])])
# test remove as doesn't seem useful (see self.distortion_correction_workflow()) and causes a crash when added
# self.wf.connect([(self.return_interface("T1w_BET"), self.return_interface("b0_T1w_Reg"),
# [("mask_file", "fixed_image_mask")])])
# dwi_T1_Tran:
# -i: Eddy corrected image
# -r: Eddy corrected b0
# -t: transforms
self.wf.connect([(self.return_interface("dwi_Eddy"),
self.return_interface("dwi_T1w_Tran"),
[("out_corrected", "input_image")])])
self.wf.connect([(self.return_interface("dwi_ROI_b0"),
self.return_interface("dwi_T1w_Tran"),
[("roi_file", "reference_image")])])
self.wf.connect([(self.return_interface("b0_T1w_Reg"),
self.return_interface("dwi_T1w_Tran"),
[("composite_transform", "transforms")])])
# BaseInterface generates a dict mapping button strings to the workflow nodes
# self.map_workflow()
graph_file = self.wf.write_graph("pre_processing", graph2use='flat')
self.graph_file = graph_file.replace("pre_processing.png",
"pre_processing_detailed.png")
self.init_settings()
self.init_ui()
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import clinica.pipelines.statistics_surface.statistics_surface_utils as utils
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.io import JSONFileSink
# Node to fetch the input variables
data_prep = npe.Node(name='inputnode',
interface=nutil.Function(
input_names=[
'input_directory', 'subjects_visits_tsv',
'group_label', 'glm_type'
],
output_names=[
'path_to_matscript', 'surfstat_input_dir',
'output_directory', 'freesurfer_home',
'out_json'
],
function=utils.prepare_data))
data_prep.inputs.input_directory = self.caps_directory
data_prep.inputs.subjects_visits_tsv = self.tsv_file
# Node to wrap the SurfStat matlab script
surfstat = npe.Node(
name='surfstat',
interface=nutil.Function(input_names=[
'input_directory', 'output_directory', 'subjects_visits_tsv',
'design_matrix', 'contrast', 'str_format', 'glm_type',
'group_label', 'freesurfer_home', 'surface_file',
'path_to_matscript', 'full_width_at_half_maximum',
'threshold_uncorrected_pvalue', 'threshold_corrected_pvalue',
'cluster_threshold', 'feature_label'
],
output_names=['out_images'],
function=utils.runmatlab))
surfstat.inputs.subjects_visits_tsv = self.tsv_file
# Node to create the dictionary for JSONFileSink
jsondict = npe.Node(
name='Jsondict',
interface=nutil.Function(input_names=[
'glm_type', 'design_matrix', 'str_format', 'contrast',
'group_label', 'full_width_at_half_maximum',
'threshold_uncorrected_pvalue', 'threshold_corrected_pvalue',
'cluster_threshold'
],
output_names=['json_dict'],
function=utils.json_dict_create))
# Node to write the GLM information into a JSON file
jsonsink = npe.Node(JSONFileSink(input_names=['out_file']),
name='jsonsinker')
# Connection
# ==========
self.connect([
(self.input_node, data_prep, [('group_label', 'group_label')
]), # noqa
(self.input_node, data_prep, [('glm_type', 'glm_type')]), # noqa
(self.input_node, surfstat, [('design_matrix', 'design_matrix')
]), # noqa
(self.input_node, surfstat, [('contrast', 'contrast')]), # noqa
(self.input_node, surfstat, [('str_format', 'str_format')
]), # noqa
(self.input_node, surfstat, [('glm_type', 'glm_type')]), # noqa
(self.input_node, surfstat, [('group_label', 'group_label')
]), # noqa
(self.input_node, surfstat, [
('full_width_at_half_maximum', 'full_width_at_half_maximum')
]), # noqa
(self.input_node, surfstat, [('threshold_uncorrected_pvalue',
'threshold_uncorrected_pvalue')
]), # noqa
(self.input_node, surfstat, [
('threshold_corrected_pvalue', 'threshold_corrected_pvalue')
]), # noqa
(self.input_node, surfstat, [('cluster_threshold',
'cluster_threshold')]), # noqa
(self.input_node, surfstat, [('surface_file', 'surface_file')
]), # noqa
(self.input_node, surfstat, [('feature_label', 'feature_label')]),
(data_prep, surfstat, [('surfstat_input_dir', 'input_directory')
]), # noqa
(data_prep, surfstat, [('path_to_matscript', 'path_to_matscript')
]), # noqa
(data_prep, surfstat, [('output_directory', 'output_directory')
]), # noqa
(data_prep, surfstat, [('freesurfer_home', 'freesurfer_home')
]), # noqa
(self.input_node, jsondict, [('glm_type', 'glm_type')]), # noqa
(self.input_node, jsondict, [('design_matrix', 'design_matrix')
]), # noqa
(self.input_node, jsondict, [('str_format', 'str_format')
]), # noqa
(self.input_node, jsondict, [('contrast', 'contrast')]), # noqa
(self.input_node, jsondict, [('group_label', 'group_label')
]), # noqa
(self.input_node, jsondict, [
('full_width_at_half_maximum', 'full_width_at_half_maximum')
]), # noqa
(self.input_node, jsondict, [('threshold_uncorrected_pvalue',
'threshold_uncorrected_pvalue')
]), # noqa
(self.input_node, jsondict, [
('threshold_corrected_pvalue', 'threshold_corrected_pvalue')
]), # noqa
(self.input_node, jsondict, [('cluster_threshold',
'cluster_threshold')]), # noqa
(data_prep, jsonsink, [('out_json', 'out_file')]), # noqa
(jsondict, jsonsink, [('json_dict', 'in_dict')]), # noqa
])
def __init__(self, parent, dir_dic, bids):
super().__init__(parent, dir_dic, bids)
# Create interfaces ============================================================================================
# BET
MNI_BET = Node(BET(), name="MNI_BET")
MNI_BET.btn_string = 'MNI Template Brain Extraction'
self.interfaces.append(MNI_BET)
# Registration
postopCT_T1_Reg = Node(Registration(), name="postopCT_T1_Reg")
postopCT_T1_Reg.btn_string = 'post-op CT to T1w Registration'
self.interfaces.append(postopCT_T1_Reg)
preopCT_T1_Reg = Node(Registration(), name="preopCT_T1_Reg")
preopCT_T1_Reg.btn_string = 'pre-op CT to T1w Registration'
self.interfaces.append(preopCT_T1_Reg)
T1_MNI_Reg = Node(Registration(), name="T1_MNI_Reg")
T1_MNI_Reg.btn_string = 'T1w to MNI template Registration'
self.interfaces.append(T1_MNI_Reg)
# Transformations
postopCT_T1_Tran = Node(ApplyTransforms(), name="postopCT_T1_Tran")
postopCT_T1_Tran.btn_string = 'post-op CT to T1w Transformation'
self.interfaces.append(postopCT_T1_Tran)
preopCT_T1_Tran = Node(ApplyTransforms(), name="preopCT_T1_Tran")
preopCT_T1_Tran.btn_string = 'pre-op CT to T1w Transformation'
self.interfaces.append(preopCT_T1_Tran)
T1_MNI_Tran = Node(ApplyTransforms(), name="T1_MNI_Tran")
T1_MNI_Tran.btn_string = 'T1w to MNI template Transformation'
self.interfaces.append(T1_MNI_Tran)
# Data output (i.e. sink) ======================================================================================
self.sink = Node(DataSink(), name="sink")
self.sink.btn_string = 'data sink'
self.sink.inputs.base_directory = self.dir_dic['data_dir']
self.jsink = Node(JSONFileSink(), name="jsink")
self.jsink.btn_string = 'json sink'
self.jsink.inputs.base_directory = self.dir_dic['data_dir']
# Initialize workflow ==========================================================================================
self.wf = Workflow(name='co_registration')
# Brain extracted MNI template to antsRegistration
# MI[mni_t1_brain.nii.gz,t1_nonGdE_brain_N4bfc_masked.nii.gz,1,32,Regular,0.25]
# MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingStrategy={None,Regular,Random}>,<samplingPercentage=[0,1]>]
self.wf.connect([(self.return_interface("MNI_BET"),
self.return_interface("T1_MNI_Reg"),
[("out_file", "fixed_image")])])
self.wf.connect([(self.return_interface("MNI_BET"),
self.return_interface("T1_MNI_Tran"),
[("out_file", "reference_image")])])
# T1 -> MNI Reg to Tran
self.wf.connect([(self.return_interface("T1_MNI_Reg"),
self.return_interface("T1_MNI_Tran"),
[("composite_transform", "transforms")])])
# postop CT -> T1 Reg to Tran
self.wf.connect([(self.return_interface("postopCT_T1_Reg"),
self.return_interface("postopCT_T1_Tran"),
[("composite_transform", "transforms")])])
# preop CT -> T1 Reg to Tran
self.wf.connect([(self.return_interface("preopCT_T1_Reg"),
self.return_interface("preopCT_T1_Tran"),
[("composite_transform", "transforms")])])
# BaseInterface generates a dict mapping button strings to the workflow nodes
self.wf.base_dir = self.dir_dic['temp_dir']
graph_file = self.wf.write_graph("co_registration", graph2use='flat')
self.graph_file = graph_file.replace("co_registration.png",
"co_registration_detailed.png")
self.init_settings()
self.init_ui()
