def test_DTIRecon_outputs():
output_map = dict(ADC=dict(),
B0=dict(),
FA=dict(),
FA_color=dict(),
L1=dict(),
L2=dict(),
L3=dict(),
V1=dict(),
V2=dict(),
V3=dict(),
exp=dict(),
tensor=dict(),
)
outputs = DTIRecon.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_DTIRecon_inputs():
input_map = dict(DWI=dict(argstr='%s',
mandatory=True,
position=1,
),
args=dict(argstr='%s',
),
b0_threshold=dict(argstr='-b0_th',
),
bvals=dict(mandatory=True,
),
bvecs=dict(argstr='-gm %s',
mandatory=True,
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
image_orientation_vectors=dict(argstr='-iop %f',
),
n_averages=dict(argstr='-nex %s',
),
oblique_correction=dict(argstr='-oc',
),
out_prefix=dict(argstr='%s',
position=2,
usedefault=True,
),
output_type=dict(argstr='-ot %s',
usedefault=True,
),
terminal_output=dict(mandatory=True,
nohash=True,
),
)
inputs = DTIRecon.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 _run_interface(self, runtime):
# Loading required packages
from BrainTypes_additional_interfaces import DipyDenoise
from nipype.interfaces.diffusion_toolkit.dti import DTIRecon
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.fsl.maths import ApplyMask
import os
# ==============================================================
# Processing of diffusion-weighted data
# Extract b0 image
fslroi = pe.Node(interface=fsl.ExtractROI(), name='extract_b0')
fslroi.inputs.in_file = self.inputs.dwi
fslroi.inputs.t_min = 0
fslroi.inputs.t_size = 1
# Create a brain mask
bet = pe.Node(interface=fsl.BET(frac=0.3,
robust=False,
mask=True,
no_output=False),
name='bet')
# Eddy-current and motion correction
eddy = pe.Node(interface=fsl.epi.Eddy(args='-v'), name='eddy')
eddy.inputs.in_acqp = self.inputs.acqparams
eddy.inputs.in_bvec = self.inputs.bvecs
eddy.inputs.in_bval = self.inputs.bvals
eddy.inputs.in_file = self.inputs.dwi
eddy.inputs.in_index = self.inputs.index_file
# Denoising
dwi_denoise = pe.Node(interface=DipyDenoise(), name='dwi_denoise')
dwi_denoise.inputs.in_file = self.inputs.dwi
# Fitting the diffusion tensor model
dtifit = pe.Node(interface=DTIRecon(), name='dtifit')
dtifit.inputs.out_prefix = self.inputs.subject_id
dtifit.inputs.bvals = self.inputs.bvals
dtifit.inputs.bvecs = self.inputs.bvecs
# Applying the mask
FA_applymask = pe.Node(interface=ApplyMask(), name='FA_applymask')
# Renaming the outputs
b0_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='b0_rename')
b0_rename.inputs.format_string = self.inputs.subject_id + '_b0'
colourFA_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='colourFA_rename')
colourFA_rename.inputs.format_string = self.inputs.subject_id + '_colourFA'
FA_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='FA_rename')
FA_rename.inputs.format_string = self.inputs.subject_id + '_FA'
dwi_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='dwi_rename')
dwi_rename.inputs.format_string = self.inputs.subject_id + '_dwi'
mask_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='mask_rename')
mask_rename.inputs.format_string = self.inputs.subject_id + '_mask'
# Collect everything
datasink = pe.Node(nio.DataSink(), name='sinker')
datasink.inputs.parameterization = False
datasink.inputs.base_directory = self.inputs.out_directory + '/_subject_id_' + self.inputs.subject_id
# ==============================================================
# Setting up the workflow
dwi_preproc = pe.Workflow(name='dwi_preproc')
# Diffusion data
# Preprocessing
dwi_preproc.connect(fslroi, 'roi_file', bet, 'in_file')
dwi_preproc.connect(bet, 'mask_file', eddy, 'in_mask')
dwi_preproc.connect(eddy, 'out_corrected', dwi_denoise, 'in_file')
# Calculate diffusion measures
dwi_preproc.connect(dwi_denoise, 'out_file', dtifit, 'DWI')
# Applying the mask to the scalar images
dwi_preproc.connect(dtifit, 'FA', FA_applymask, 'in_file')
dwi_preproc.connect(bet, 'mask_file', FA_applymask, 'mask_file')
# Renaming
dwi_preproc.connect(fslroi, 'roi_file', b0_rename, 'in_file')
dwi_preproc.connect(dwi_denoise, 'out_file', dwi_rename, 'in_file')
dwi_preproc.connect(FA_applymask, 'out_file', FA_rename, 'in_file')
dwi_preproc.connect(dtifit, 'FA_color', colourFA_rename, 'in_file')
dwi_preproc.connect(bet, 'mask_file', mask_rename, 'in_file')
# Moving the results to the datasink
dwi_preproc.connect(b0_rename, 'out_file', datasink,
'preprocessed.@b0')
dwi_preproc.connect(colourFA_rename, 'out_file', datasink,
'preprocessed.@colourFA')
dwi_preproc.connect(dwi_rename, 'out_file', datasink,
'preprocessed.@dwi')
dwi_preproc.connect(FA_rename, 'out_file', datasink,
'preprocessed.@FA')
dwi_preproc.connect(mask_rename, 'out_file', datasink,
'preprocessed.@mask')
# ==============================================================
# Running the workflow
dwi_preproc.base_dir = os.path.abspath(self.inputs.out_directory +
'_subject_id_' +
self.inputs.subject_id)
dwi_preproc.write_graph()
dwi_preproc.run()
return runtime
