def run_qap_mask(anatomical_reorient,
flirt_affine_xfm,
template_skull,
run=True):
# stand-alone runner for anatomical reorient workflow
import os
import sys
import glob
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
output = 'qap_head_mask'
workflow = pe.Workflow(name='%s_workflow' % output)
current_dir = os.getcwd()
workflow_dir = os.path.join(current_dir, output)
workflow.base_dir = workflow_dir
resource_pool = {}
config = {}
num_cores_per_subject = 1
resource_pool['anatomical_reorient'] = anatomical_reorient
resource_pool['flirt_affine_xfm'] = flirt_affine_xfm
config['template_skull_for_anat'] = template_skull
workflow, resource_pool = \
qap_mask_workflow(workflow, resource_pool, config)
ds = pe.Node(nio.DataSink(), name='datasink_%s' % output)
ds.inputs.base_directory = workflow_dir
node, out_file = resource_pool[output]
workflow.connect(node, out_file, ds, output)
if run:
workflow.run(plugin='MultiProc',
plugin_args={'n_procs': num_cores_per_subject})
outpath = glob.glob(os.path.join(workflow_dir, output, '*'))[0]
return outpath
else:
return workflow, workflow.base_dir
def run_qap_mask(anatomical_reorient, flirt_affine_xfm, template_skull, run=True):
# stand-alone runner for anatomical reorient workflow
import os
import sys
import glob
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
output = "qap_head_mask"
workflow = pe.Workflow(name="%s_workflow" % output)
current_dir = os.getcwd()
workflow_dir = os.path.join(current_dir, output)
workflow.base_dir = workflow_dir
resource_pool = {}
config = {}
num_cores_per_subject = 1
resource_pool["anatomical_reorient"] = anatomical_reorient
resource_pool["flirt_affine_xfm"] = flirt_affine_xfm
config["template_skull_for_anat"] = template_skull
workflow, resource_pool = qap_mask_workflow(workflow, resource_pool, config)
ds = pe.Node(nio.DataSink(), name="datasink_%s" % output)
ds.inputs.base_directory = workflow_dir
node, out_file = resource_pool[output]
workflow.connect(node, out_file, ds, output)
if run == True:
workflow.run(plugin="MultiProc", plugin_args={"n_procs": num_cores_per_subject})
outpath = glob.glob(os.path.join(workflow_dir, output, "*"))[0]
return outpath
else:
return workflow, workflow.base_dir
def qap_anatomical_spatial_workflow(workflow, resource_pool, config):
# resource pool should have:
# anatomical_reorient
# qap_head_mask
# anatomical_gm_mask
# anatomical_wm_mask
# anatomical_csf_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from qap_workflows_utils import qap_anatomical_spatial, write_to_csv
if "qap_head_mask" not in resource_pool.keys():
from qap_workflows import qap_mask_workflow
workflow, resource_pool = qap_mask_workflow(workflow, resource_pool, config)
if (
("anatomical_gm_mask" not in resource_pool.keys())
or ("anatomical_wm_mask" not in resource_pool.keys())
or ("anatomical_csf_mask" not in resource_pool.keys())
):
from anatomical_preproc import segmentation_workflow
workflow, resource_pool = segmentation_workflow(workflow, resource_pool, config)
if "anatomical_reorient" not in resource_pool.keys():
from anatomical_preproc import anatomical_reorient_workflow
workflow, resource_pool = anatomical_reorient_workflow(workflow, resource_pool, config)
spatial = pe.Node(
util.Function(
input_names=[
"anatomical_reorient",
"head_mask_path",
"anatomical_gm_mask",
"anatomical_wm_mask",
"anatomical_csf_mask",
"subject_id",
"session_id",
"scan_id",
"site_name",
],
output_names=["qc"],
function=qap_anatomical_spatial,
),
name="qap_anatomical_spatial",
)
spatial_to_csv = pe.Node(
util.Function(input_names=["sub_qap_dict"], output_names=["outfile"], function=write_to_csv),
name="qap_anatomical_spatial_to_csv",
)
if len(resource_pool["anatomical_reorient"]) == 2:
node, out_file = resource_pool["anatomical_reorient"]
workflow.connect(node, out_file, spatial, "anatomical_reorient")
else:
spatial.inputs.anatomical_reorient = resource_pool["anatomical_reorient"]
if len(resource_pool["qap_head_mask"]) == 2:
node, out_file = resource_pool["qap_head_mask"]
workflow.connect(node, out_file, spatial, "head_mask_path")
else:
spatial.inputs.head_mask_path = resource_pool["qap_head_mask"]
if len(resource_pool["anatomical_gm_mask"]) == 2:
node, out_file = resource_pool["anatomical_gm_mask"]
workflow.connect(node, out_file, spatial, "anatomical_gm_mask")
else:
spatial.inputs.anatomical_gm_mask = resource_pool["anatomical_gm_mask"]
if len(resource_pool["anatomical_wm_mask"]) == 2:
node, out_file = resource_pool["anatomical_wm_mask"]
workflow.connect(node, out_file, spatial, "anatomical_wm_mask")
else:
spatial.inputs.anatomical_wm_mask = resource_pool["anatomical_wm_mask"]
if len(resource_pool["anatomical_csf_mask"]) == 2:
node, out_file = resource_pool["anatomical_csf_mask"]
workflow.connect(node, out_file, spatial, "anatomical_csf_mask")
else:
spatial.inputs.anatomical_csf_mask = resource_pool["anatomical_csf_mask"]
# Subject infos
spatial.inputs.subject_id = config["subject_id"]
spatial.inputs.session_id = config["session_id"]
spatial.inputs.scan_id = config["scan_id"]
if "site_name" in config.keys():
spatial.inputs.site_name = config["site_name"]
workflow.connect(spatial, "qc", spatial_to_csv, "sub_qap_dict")
resource_pool["qap_anatomical_spatial"] = (spatial_to_csv, "outfile")
return workflow, resource_pool
def qap_anatomical_spatial_workflow(workflow, resource_pool, config, report=False):
# resource pool should have:
# anatomical_reorient
# qap_head_mask
# anatomical_gm_mask
# anatomical_wm_mask
# anatomical_csf_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.algorithms.misc as nam
from qap_workflows_utils import qap_anatomical_spatial
from qap.viz.interfaces import PlotMosaic
if "qap_head_mask" not in resource_pool.keys():
from qap_workflows import qap_mask_workflow
workflow, resource_pool = qap_mask_workflow(workflow, resource_pool, config)
if (
("anatomical_gm_mask" not in resource_pool.keys())
or ("anatomical_wm_mask" not in resource_pool.keys())
or ("anatomical_csf_mask" not in resource_pool.keys())
):
from anatomical_preproc import segmentation_workflow
workflow, resource_pool = segmentation_workflow(workflow, resource_pool, config)
if "anatomical_reorient" not in resource_pool.keys():
from anatomical_preproc import anatomical_reorient_workflow
workflow, resource_pool = anatomical_reorient_workflow(workflow, resource_pool, config)
spatial = pe.Node(
niu.Function(
input_names=[
"anatomical_reorient",
"head_mask_path",
"anatomical_gm_mask",
"anatomical_wm_mask",
"anatomical_csf_mask",
"subject_id",
"session_id",
"scan_id",
"site_name",
],
output_names=["qc"],
function=qap_anatomical_spatial,
),
name="qap_anatomical_spatial",
)
# Subject infos
spatial.inputs.subject_id = config["subject_id"]
spatial.inputs.session_id = config["session_id"]
spatial.inputs.scan_id = config["scan_id"]
if "site_name" in config.keys():
spatial.inputs.site_name = config["site_name"]
if len(resource_pool["anatomical_reorient"]) == 2:
node, out_file = resource_pool["anatomical_reorient"]
workflow.connect(node, out_file, spatial, "anatomical_reorient")
else:
spatial.inputs.anatomical_reorient = resource_pool["anatomical_reorient"]
if len(resource_pool["qap_head_mask"]) == 2:
node, out_file = resource_pool["qap_head_mask"]
workflow.connect(node, out_file, spatial, "head_mask_path")
else:
spatial.inputs.head_mask_path = resource_pool["qap_head_mask"]
if len(resource_pool["anatomical_gm_mask"]) == 2:
node, out_file = resource_pool["anatomical_gm_mask"]
workflow.connect(node, out_file, spatial, "anatomical_gm_mask")
else:
spatial.inputs.anatomical_gm_mask = resource_pool["anatomical_gm_mask"]
if len(resource_pool["anatomical_wm_mask"]) == 2:
node, out_file = resource_pool["anatomical_wm_mask"]
workflow.connect(node, out_file, spatial, "anatomical_wm_mask")
else:
spatial.inputs.anatomical_wm_mask = resource_pool["anatomical_wm_mask"]
if len(resource_pool["anatomical_csf_mask"]) == 2:
node, out_file = resource_pool["anatomical_csf_mask"]
workflow.connect(node, out_file, spatial, "anatomical_csf_mask")
else:
spatial.inputs.anatomical_csf_mask = resource_pool["anatomical_csf_mask"]
if config.get("write_report", False):
plot = pe.Node(PlotMosaic(), name="plot_mosaic")
plot.inputs.subject = config["subject_id"]
metadata = [config["session_id"], config["scan_id"]]
if "site_name" in config.keys():
metadata.append(config["site_name"])
plot.inputs.metadata = metadata
plot.inputs.title = "Anatomical reoriented"
if len(resource_pool["anatomical_reorient"]) == 2:
node, out_file = resource_pool["anatomical_reorient"]
workflow.connect(node, out_file, plot, "in_file")
else:
plot.inputs.in_file = resource_pool["anatomical_reorient"]
# Enable this if we want masks
# if len(resource_pool['qap_head_mask']) == 2:
# node, out_file = resource_pool['qap_head_mask']
# workflow.connect(node, out_file, plot, 'in_mask')
# else:
# plot.inputs.in_mask = resource_pool['qap_head_mask']
resource_pool["qap_mosaic"] = (plot, "out_file")
out_csv = op.join(config["output_directory"], "qap_anatomical_spatial.csv")
spatial_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv), name="qap_anatomical_spatial_to_csv")
workflow.connect(spatial, "qc", spatial_to_csv, "_outputs")
resource_pool["qap_anatomical_spatial"] = (spatial_to_csv, "csv_file")
return workflow, resource_pool
def qap_anatomical_spatial_workflow(workflow,
resource_pool,
config,
report=False):
# resource pool should have:
# anatomical_reorient
# qap_head_mask
# anatomical_gm_mask
# anatomical_wm_mask
# anatomical_csf_mask
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.algorithms.misc as nam
from qap_workflows_utils import qap_anatomical_spatial
from qap.viz.interfaces import PlotMosaic
if 'qap_head_mask' not in resource_pool.keys():
from qap_workflows import qap_mask_workflow
workflow, resource_pool = \
qap_mask_workflow(workflow, resource_pool, config)
if ('anatomical_gm_mask' not in resource_pool.keys()) or \
('anatomical_wm_mask' not in resource_pool.keys()) or \
('anatomical_csf_mask' not in resource_pool.keys()):
from anatomical_preproc import segmentation_workflow
workflow, resource_pool = \
segmentation_workflow(workflow, resource_pool, config)
if 'anatomical_reorient' not in resource_pool.keys():
from anatomical_preproc import anatomical_reorient_workflow
workflow, resource_pool = \
anatomical_reorient_workflow(workflow, resource_pool, config)
spatial = pe.Node(niu.Function(input_names=[
'anatomical_reorient', 'head_mask_path', 'anatomical_gm_mask',
'anatomical_wm_mask', 'anatomical_csf_mask', 'subject_id',
'session_id', 'scan_id', 'site_name'
],
output_names=['qc'],
function=qap_anatomical_spatial),
name='qap_anatomical_spatial')
# Subject infos
spatial.inputs.subject_id = config['subject_id']
spatial.inputs.session_id = config['session_id']
spatial.inputs.scan_id = config['scan_id']
if 'site_name' in config.keys():
spatial.inputs.site_name = config['site_name']
if len(resource_pool['anatomical_reorient']) == 2:
node, out_file = resource_pool['anatomical_reorient']
workflow.connect(node, out_file, spatial, 'anatomical_reorient')
else:
spatial.inputs.anatomical_reorient = \
resource_pool['anatomical_reorient']
if len(resource_pool['qap_head_mask']) == 2:
node, out_file = resource_pool['qap_head_mask']
workflow.connect(node, out_file, spatial, 'head_mask_path')
else:
spatial.inputs.head_mask_path = resource_pool['qap_head_mask']
if len(resource_pool['anatomical_gm_mask']) == 2:
node, out_file = resource_pool['anatomical_gm_mask']
workflow.connect(node, out_file, spatial, 'anatomical_gm_mask')
else:
spatial.inputs.anatomical_gm_mask = \
resource_pool['anatomical_gm_mask']
if len(resource_pool['anatomical_wm_mask']) == 2:
node, out_file = resource_pool['anatomical_wm_mask']
workflow.connect(node, out_file, spatial, 'anatomical_wm_mask')
else:
spatial.inputs.anatomical_wm_mask = \
resource_pool['anatomical_wm_mask']
if len(resource_pool['anatomical_csf_mask']) == 2:
node, out_file = resource_pool['anatomical_csf_mask']
workflow.connect(node, out_file, spatial, 'anatomical_csf_mask')
else:
spatial.inputs.anatomical_csf_mask = \
resource_pool['anatomical_csf_mask']
if config.get('write_report', False):
plot = pe.Node(PlotMosaic(), name='plot_mosaic')
plot.inputs.subject = config['subject_id']
metadata = [config['session_id'], config['scan_id']]
if 'site_name' in config.keys():
metadata.append(config['site_name'])
plot.inputs.metadata = metadata
plot.inputs.title = 'Anatomical reoriented'
if len(resource_pool['anatomical_reorient']) == 2:
node, out_file = resource_pool['anatomical_reorient']
workflow.connect(node, out_file, plot, 'in_file')
else:
plot.inputs.in_file = resource_pool['anatomical_reorient']
# Enable this if we want masks
# if len(resource_pool['qap_head_mask']) == 2:
# node, out_file = resource_pool['qap_head_mask']
# workflow.connect(node, out_file, plot, 'in_mask')
# else:
# plot.inputs.in_mask = resource_pool['qap_head_mask']
resource_pool['qap_mosaic'] = (plot, 'out_file')
out_csv = op.join(config['output_directory'], 'qap_anatomical_spatial.csv')
spatial_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv),
name='qap_anatomical_spatial_to_csv')
workflow.connect(spatial, 'qc', spatial_to_csv, '_outputs')
resource_pool['qap_anatomical_spatial'] = (spatial_to_csv, 'csv_file')
return workflow, resource_pool
