def biasfieldcorr(input_file,outputPath):
#output_file = os.path.join(os.path.dirname(input_file),os.path.basename(input_file).split('.')[0] + 'Bias.nii.gz')
output_file = os.path.join(outputPath, os.path.basename(input_file).split('.')[0] + 'Bias.nii.gz')
myAnts = ants.N4BiasFieldCorrection(input_image=input_file,output_image=output_file,shrink_factor=4,dimension=3)
myAnts.run()
print('Bias correction DONE!')
return output_file
def epi_sbref_registration(name='EPI_SBrefRegistration'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['epi_brain', 'sbref_brain']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['epi_registered', 'out_mat']),
name='outputnode')
mean = pe.Node(fsl.MeanImage(dimension='T'), name='EPImean')
inu = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='EPImeanBias')
epi_sbref = pe.Node(fsl.FLIRT(dof=6, out_matrix_file='init.mat'),
name='EPI2SBRefRegistration')
epi_split = pe.Node(fsl.Split(dimension='t'), name='EPIsplit')
epi_xfm = pe.MapNode(fsl.ApplyXfm(),
name='EPIapplyxfm',
iterfield=['in_file'])
epi_merge = pe.Node(fsl.Merge(dimension='t'), name='EPImergeback')
workflow.connect([
(inputnode, epi_split, [('epi_brain', 'in_file')]),
(inputnode, epi_sbref, [('sbref_brain', 'reference')]),
(inputnode, epi_xfm, [('sbref_brain', 'reference')]),
(inputnode, mean, [('epi_brain', 'in_file')]),
(mean, inu, [('out_file', 'input_image')]),
(inu, epi_sbref, [('output_image', 'in_file')]),
(epi_split, epi_xfm, [('out_files', 'in_file')]),
(epi_sbref, epi_xfm, [('out_matrix_file', 'in_matrix_file')]),
(epi_xfm, epi_merge, [('out_file', 'in_files')]),
(epi_sbref, outputnode, [('out_matrix_file', 'out_mat')]),
(epi_merge, outputnode, [('merged_file', 'epi_registered')])
])
return workflow
def bet_T1(self, **name_maps):
pipeline = self.new_pipeline(name='BET_T1',
name_maps=name_maps,
desc=("python implementation of BET"),
references=[fsl_cite])
bias = pipeline.add('n4_bias_correction',
ants.N4BiasFieldCorrection(),
inputs={'input_image': ('t1', nifti_gz_format)},
requirements=[ants_req.v('1.9')],
wall_time=60,
mem_gb=12)
pipeline.add('bet',
fsl.BET(frac=0.15, reduce_bias=True),
connections={'in_file': (bias, 'output_image')},
outputs={
'out_file': ('betted_T1', nifti_gz_format),
'mask_file': ('betted_T1_mask', nifti_gz_format)
},
requirements=[fsl_req.v('5.0.8')],
mem_gb=8,
wall_time=45)
return pipeline
def bet_T1(self, **name_maps):
pipeline = self.new_pipeline(
name='BET_T1',
name_maps=name_maps,
desc=("Brain extraction pipeline using FSL's BET"),
citations=[fsl_cite])
bias = pipeline.add('n4_bias_correction',
ants.N4BiasFieldCorrection(),
inputs={'input_image': ('t1', nifti_gz_format)},
requirements=[ants_req.v('1.9')],
wall_time=60,
mem_gb=12)
pipeline.add('bet',
fsl.BET(frac=0.15,
reduce_bias=True,
output_type='NIFTI_GZ'),
inputs={'in_file': (bias, 'output_image')},
outputs={
'betted_T1': ('out_file', nifti_gz_format),
'betted_T1_mask': ('mask_file', nifti_gz_format)
},
requirements=[fsl_req.v('5.0.8')],
mem_gb=8,
wall_time=45)
return pipeline
def ants_n4_bf_correction(ffVOL,
ffOUT,
run=False,
bspline_fitting_distance=300,
shrink_factor=3,
n_iterations=[50, 50, 30, 20]):
"""
Run N4 bias field correction on given MRI volume.
Parameters
----------
ffVOL: str
ffOUT: str
run: bool
bspline_fitting_distance: int
shrink_factor: int
n_iterations: list
Returns
-------
n4: nipype.interfaces.ants.N4BiasFieldCorrection
Will write output to desire ffout path.
"""
n4 = ants.N4BiasFieldCorrection(
) # FILTER/nipype module, here:wrapping ants
n4.inputs.dimension = 3
n4.inputs.bspline_fitting_distance = bspline_fitting_distance
n4.inputs.shrink_factor = shrink_factor
n4.inputs.n_iterations = n_iterations
n4.inputs.num_threads = 4
# N4 Bias Field correction for all volumes
n4.inputs.input_image = ffVOL
n4.inputs.output_image = ffOUT
if run:
n4.run()
return n4
def remove_bias(name='bias_correct'):
"""
This workflow estimates a single multiplicative bias field from the
averaged *b0* image, as suggested in [Jeurissen2014]_.
.. admonition:: References
.. [Jeurissen2014] Jeurissen B. et al., `Multi-tissue constrained
spherical deconvolution for improved analysis of multi-shell diffusion
MRI data <https://doi.org/10.1016/j.neuroimage.2014.07.061>`_.
NeuroImage (2014). doi: 10.1016/j.neuroimage.2014.07.061
Example
-------
>>> from nipype.workflows.dmri.fsl.artifacts import remove_bias
>>> bias = remove_bias()
>>> bias.inputs.inputnode.in_file = 'epi.nii'
>>> bias.inputs.inputnode.in_bval = 'diffusion.bval'
>>> bias.inputs.inputnode.in_mask = 'mask.nii'
>>> bias.run() # doctest: +SKIP
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=['in_file', 'in_bval', 'in_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
avg_b0 = pe.Node(niu.Function(input_names=['in_dwi', 'in_bval'],
output_names=['out_file'],
function=b0_average),
name='b0_avg')
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
save_bias=True,
bspline_fitting_distance=600),
name='Bias_b0')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
mult = pe.MapNode(fsl.MultiImageMaths(op_string='-div %s'),
iterfield=['in_file'],
name='RemoveBiasOfDWIs')
thres = pe.MapNode(fsl.Threshold(thresh=0.0),
iterfield=['in_file'],
name='RemoveNegative')
merge = pe.Node(fsl.utils.Merge(dimension='t'), name='MergeDWIs')
wf = pe.Workflow(name=name)
wf.connect([(inputnode, avg_b0, [('in_file', 'in_dwi'),
('in_bval', 'in_bval')]),
(avg_b0, n4, [('out_file', 'input_image')]),
(inputnode, n4, [('in_mask', 'mask_image')]),
(inputnode, split, [('in_file', 'in_file')]),
(n4, mult, [('bias_image', 'operand_files')]),
(split, mult, [('out_files', 'in_file')]),
(mult, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(merge, outputnode, [('merged_file', 'out_file')])])
return wf
def doN4BiasFieldCorrection(infile, outfile): # Doing N4 Bias-Field Correction
'''
Parameters
----------
infile : str
path containing the input image.
outfile : str
path to save the image after N4 bias-field correction (N4 is from ANTS).
Returns
-------
an image which is the bias-field corrected version of input image
'''
print('doing bias correction using N4 for', infile)
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = infile
n4.inputs.save_bias = False
n4.inputs.output_image = outfile
n4.inputs.bspline_fitting_distance = 100
n4.inputs.rescale_intensities = True
n4.inputs.convergence_threshold = 0
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [50, 50, 50, 50]
n4.inputs.histogram_sharpening = (0.14, 0.01, 200)
n4.run()
print('bias-corection done', outfile, '\n')
def inflated_size_nodes(): s_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(), name="s_biascorrect") s_biascorrect.inputs.dimension = 3 s_biascorrect.inputs.bspline_fitting_distance = 100 s_biascorrect.inputs.shrink_factor = 2 s_biascorrect.inputs.n_iterations = [200,200,200,200] s_biascorrect.inputs.convergence_threshold = 1e-11 f_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(), name="f_biascorrect") f_biascorrect.inputs.dimension = 3 f_biascorrect.inputs.bspline_fitting_distance = 100 f_biascorrect.inputs.shrink_factor = 2 f_biascorrect.inputs.n_iterations = [200,200,200,200] f_biascorrect.inputs.convergence_threshold = 1e-11 return s_biascorrect, f_biascorrect
def main(sourcedata,
derivatives,
subject,
session,
run,
wf_dir):
layout = BIDSLayout(sourcedata)
bolds = layout.get(subject=subject,
session=session,
run=run,
suffix='bold',
return_type='file')
bold = bolds
for bold in bolds:
print('Making reference image of {}'.format(bold))
inputnode = pe.Node(niu.IdentityInterface(fields=['bold']),
name='inputnode')
inputnode.inputs.bold = bolds
wf = pe.Workflow(name='make_ref_{}_{}_{}'.format(subject,
session,
run))
wf.base_dir = wf_dir
mc_wf_bold = create_motion_correction_workflow(name='mc_wf_bold',
method='FSL',
lightweight=True)
wf.connect(inputnode, 'bold', mc_wf_bold, 'inputspec.in_files')
wf.connect(inputnode, ('bold', pickfirst), mc_wf_bold, 'inputspec.which_file_is_EPI_space')
mean_bold = pe.MapNode(fsl.MeanImage(dimension='T'),
iterfield=['in_file'],
name='mean_bold1')
n4_correct = pe.MapNode(ants.N4BiasFieldCorrection(),
iterfield=['input_image'],
name='n4_correct')
wf.connect(mean_bold, 'out_file', n4_correct, 'input_image')
ds = pe.MapNode(DerivativesDataSink(out_path_base='simple_bold_ref',
suffix='reference',
base_directory=derivatives),
iterfield=['in_file', 'source_file'],
name='ds_reg_report')
wf.connect(mc_wf_bold, 'outputspec.motion_corrected_files', mean_bold, 'in_file')
wf.connect(n4_correct, 'output_image', ds, 'in_file')
wf.connect(inputnode, 'bold', ds, 'source_file')
wf.run()
def create_workflow(config: AttrDict, resource_pool: ResourcePool,
context: Context):
for _, rp in resource_pool[['T1w']]:
anat_image = rp[R('T1w')]
anat_deoblique = NipypeJob(interface=afni.Refit(deoblique=True),
reference='anat_deoblique')
anat_deoblique.in_file = anat_image
output_node = anat_deoblique.out_file
if config.non_local_means_filtering:
denoise = NipypeJob(interface=ants.DenoiseImage(),
reference='anat_denoise')
denoise.input_image = output_node
output_node = denoise.output_image
if config.n4_bias_field_correction:
n4 = NipypeJob(interface=ants.N4BiasFieldCorrection(
dimension=3, shrink_factor=2, copy_header=True),
reference='anat_n4')
n4.input_image = output_node
output_node = n4.output_image
anat_reorient = NipypeJob(interface=afni.Resample(
orientation='RPI', outputtype='NIFTI_GZ'),
reference='anat_reorient')
anat_reorient.in_file = output_node
rp[R('T1w', label='reorient')] = anat_reorient.out_file
def real_size_nodes(): s_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(), name="s_biascorrect") s_biascorrect.inputs.dimension = 3 s_biascorrect.inputs.bspline_fitting_distance = 10 s_biascorrect.inputs.bspline_order = 4 s_biascorrect.inputs.shrink_factor = 2 s_biascorrect.inputs.n_iterations = [150,100,50,30] s_biascorrect.inputs.convergence_threshold = 1e-16 f_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(), name="f_biascorrect") f_biascorrect.inputs.dimension = 3 f_biascorrect.inputs.bspline_fitting_distance = 10 f_biascorrect.inputs.bspline_order = 4 f_biascorrect.inputs.shrink_factor = 2 f_biascorrect.inputs.n_iterations = [150,100,50,30] f_biascorrect.inputs.convergence_threshold = 1e-11 return s_biascorrect, f_biascorrect
def n4_correction(in_file):
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension=3
n4.inputs.input_image = in_file
n4.inputs.bspline_fitting_distance = 300
n4.inputs.shrink_factor = 3
n4.inputs.n_iterations = [50, 50, 30, 20]
n4.inputs.output_image = in_file.replace('.nii.gz', '_correcred.nii.gz')
n4.run()
return n4.inputs.output_image
def dwi_flirt(name='DWICoregistration', excl_nodiff=False,
flirt_param={}):
"""
Generates a workflow for linear registration of dwi volumes
"""
inputnode = pe.Node(niu.IdentityInterface(fields=['reference',
'in_file', 'ref_mask', 'in_xfms', 'in_bval']),
name='inputnode')
initmat = pe.Node(niu.Function(input_names=['in_bval', 'in_xfms',
'excl_nodiff'], output_names=['init_xfms'],
function=_checkinitxfm), name='InitXforms')
initmat.inputs.excl_nodiff = excl_nodiff
dilate = pe.Node(fsl.maths.MathsCommand(nan2zeros=True,
args='-kernel sphere 5 -dilM'), name='MskDilate')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
pick_ref = pe.Node(niu.Select(), name='Pick_b0')
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='Bias')
enhb0 = pe.Node(niu.Function(input_names=['in_file', 'in_mask',
'clip_limit'], output_names=['out_file'],
function=enhance), name='B0Equalize')
enhb0.inputs.clip_limit = 0.015
enhdw = pe.MapNode(niu.Function(input_names=['in_file', 'in_mask'],
output_names=['out_file'], function=enhance),
name='DWEqualize', iterfield=['in_file'])
flirt = pe.MapNode(fsl.FLIRT(**flirt_param), name='CoRegistration',
iterfield=['in_file', 'in_matrix_file'])
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='RemoveNegative')
merge = pe.Node(fsl.Merge(dimension='t'), name='MergeDWIs')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file',
'out_xfms']), name='outputnode')
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, split, [('in_file', 'in_file')]),
(inputnode, dilate, [('ref_mask', 'in_file')]),
(inputnode, enhb0, [('ref_mask', 'in_mask')]),
(inputnode, initmat, [('in_xfms', 'in_xfms'),
('in_bval', 'in_bval')]),
(inputnode, n4, [('reference', 'input_image'),
('ref_mask', 'mask_image')]),
(dilate, flirt, [('out_file', 'ref_weight'),
('out_file', 'in_weight')]),
(n4, enhb0, [('output_image', 'in_file')]),
(split, enhdw, [('out_files', 'in_file')]),
(dilate, enhdw, [('out_file', 'in_mask')]),
(enhb0, flirt, [('out_file', 'reference')]),
(enhdw, flirt, [('out_file', 'in_file')]),
(initmat, flirt, [('init_xfms', 'in_matrix_file')]),
(flirt, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(merge, outputnode, [('merged_file', 'out_file')]),
(flirt, outputnode, [('out_matrix_file', 'out_xfms')])
])
return wf
def additional_s_biascorrect(node_name='_s_biascorrect'):
s_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(),
name=node_name)
s_biascorrect.inputs.dimension = 3
s_biascorrect.inputs.bspline_fitting_distance = 10
s_biascorrect.inputs.bspline_order = 4
s_biascorrect.inputs.shrink_factor = 2
s_biascorrect.inputs.n_iterations = [150, 100, 50, 30]
s_biascorrect.inputs.convergence_threshold = 1e-16
return s_biascorrect
def bias_corr(images, brain_mask, output_dir):
'''
Uses N4 bias correction to remove intensity inhomogeneities
Input:
images = List of images (w/ paths) to be corrected
'''
subj = os.path.split(output_dir)[-1]
dir_files = os.listdir(output_dir)
if fnmatch.filter(dir_files, '*bias*'):
print("Bias correction already run. Not re-running for subj {}".format(
subj))
bias_output = glob.glob(os.path.join(output_dir, '*_bias_corr.nii.gz'))
bias_output.sort(
key=len) #Assumes T2 image has been rigidly transformed to t1
else:
bias_output = []
for n, image in enumerate(images):
image_file = os.path.split(image)[1]
image_name = image_file.split('.')[0]
print(image, image_name)
n4 = ants.N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = image
n4.inputs.bspline_fitting_distance = 300
n4.inputs.shrink_factor = 2
n4.inputs.n_iterations = [50, 50, 50, 50]
n4.inputs.save_bias = True
n4.inputs.bias_image = os.path.join(
output_dir, image_name + '_bias_field.nii.gz')
n4.inputs.output_image = os.path.join(
output_dir, image_name + '_bias_corr.nii.gz')
n4.inputs.num_threads = os.cpu_count() - 1
n4.inputs.mask_image = brain_mask
n4_results = n4.run()
output_image = n4_results.outputs.get()['output_image']
bias_output.append(output_image)
return (bias_output)
def ants_getmask(name):
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.interfaces.ants as ants
import nipype.interfaces.freesurfer as fs
wf = pe.Workflow(name=name)
inputspec = pe.Node(
niu.IdentityInterface(fields=['functional', 'structural']),
name='inputspec')
bet = pe.Node(fsl.BET(mask=True, remove_eyes=True), name='bet')
applymask = pe.Node(fs.ApplyMask(), name='applymask')
#flirt = pe.Node(fsl.FLIRT(),name='flirt')
#applyxfm_mask = pe.Node(fsl.ApplyXfm(interp='nearestneighbour',apply_xfm=True),name='applyxfm_mask')
#applyxfm_seg = pe.MapNode(fsl.ApplyXfm(interp='nearestneighbour',apply_xfm=True),name='applyxfm_seg',iterfield=['in_file'])
dilate = pe.Node(fsl.DilateImage(operation='mean'), name='dilate')
atropos = pe.Node(ants.Atropos(initialization='KMeans',
number_of_tissue_classes=3,
dimension=3),
name='atropos')
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='n4corrections')
outputspec = pe.Node(niu.IdentityInterface(
fields=['mask', 'reg_file', 'segments', 'warped_struct']),
name='outputspec')
#create brain mask
wf.connect(inputspec, "structural", bet, "in_file")
#dilate bets mask a bit for atropos
wf.connect(bet, "mask_file", dilate, "in_file")
# apply dilated mask to img
wf.connect(dilate, 'out_file', applymask, 'mask_file')
wf.connect(inputspec, 'structural', applymask, 'in_file')
#N4 bias correction
wf.connect(applymask, "out_file", n4, 'input_image')
# atropos it
wf.connect(n4, "output_image", atropos, 'intensity_images')
wf.connect(dilate, 'out_file', atropos, 'mask_image')
return wf
def init_enhance_and_skullstrip_epi_wf(name='enhance_and_skullstrip_epi_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'mask_file', 'skull_stripped_file', 'bias_corrected_file', 'out_report'
]),
name='outputnode')
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True),
name='n4_correct')
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
unifize = pe.Node(afni.Unifize(t2=True,
outputtype='NIFTI_GZ',
args='-clfrac 0.4',
out_file="uni.nii.gz"),
name='unifize')
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')
workflow.connect([
(inputnode, n4_correct, [('in_file', 'input_image')]),
(n4_correct, skullstrip_first_pass, [('output_image', 'in_file')]),
(skullstrip_first_pass, unifize, [('out_file', 'in_file')]),
(unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file', 'operand_file')
]),
(unifize, apply_mask, [('out_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(n4_correct, mask_reportlet, [('output_image', 'background_file')]),
(combine_masks, mask_reportlet, [('out_file', 'mask_file')]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(mask_reportlet, outputnode, [('out_report', 'out_report')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])
return workflow
def ants_n4(in_file,
write_dir=None,
caching=False,
terminal_output='allatonce',
environ=None,
copy_geometry=True):
if write_dir is None:
write_dir = os.path.dirname(in_file)
if environ is None:
environ = {'AFNI_DECONFLICT': 'OVERWRITE'}
if caching:
memory = Memory(write_dir)
bias_correct = memory.cache(ants.N4BiasFieldCorrection)
copy = memory.cache(afni.Copy)
copy_geom = memory.cache(fsl.CopyGeom)
bias_correct.interface().set_default_terminal_output(terminal_output)
copy.interface().set_default_terminal_output(terminal_output)
else:
bias_correct = ants.N4BiasFieldCorrection(
terminal_output=terminal_output).run
copy = afni.Copy(terminal_output=terminal_output).run
copy_geom = fsl.CopyGeom(terminal_output=terminal_output).run
unbiased_file = fname_presuffix(in_file, suffix='_n4', newpath=write_dir)
if copy_geometry:
output_image = fname_presuffix(in_file,
suffix='_n4_rough_geom',
newpath=write_dir)
else:
output_image = unbiased_file
out_bias_correct = bias_correct(
input_image=in_file,
shrink_factor=_compute_n4_max_shrink(in_file),
output_image=output_image)
if copy_geometry:
out_copy = copy(in_file=out_bias_correct.outputs.output_image,
out_file=unbiased_file,
environ=environ)
out_copy_geom = copy_geom(dest_file=out_copy.outputs.out_file,
in_file=in_file)
return unbiased_file
def _multiple_pe_hmc(in_files, in_movpar, in_ref=None):
"""
This function interprets that we are dealing with a
multiple PE (phase encoding) input if it finds several
files in in_files.
If we have several images with various PE directions,
it will compute the HMC parameters between them using
an embedded workflow.
It just forwards the two inputs otherwise.
"""
import os
from nipype.interfaces import fsl
from nipype.interfaces import ants
if len(in_files) == 1:
out_file = in_files[0]
out_movpar = in_movpar
else:
if in_ref is None:
in_ref = 0
# Head motion correction
fslmerge = fsl.Merge(dimension='t', in_files=in_files)
hmc = fsl.MCFLIRT(ref_vol=in_ref, save_mats=True, save_plots=True)
hmc.inputs.in_file = fslmerge.run().outputs.merged_file
hmc_res = hmc.run()
out_file = hmc_res.outputs.out_file
out_movpar = hmc_res.outputs.par_file
mean = fsl.MeanImage(
dimension='T', in_file=out_file)
inu = ants.N4BiasFieldCorrection(
dimension=3, input_image=mean.run().outputs.out_file)
inu_res = inu.run()
out_ref = inu_res.outputs.output_image
bet = fsl.BET(
frac=0.6, mask=True, in_file=out_ref)
out_mask = bet.run().outputs.mask_file
return (out_file, out_ref, out_mask, out_movpar)
def afni_wf(name='AFNISkullStripWorkflow'):
"""
Skull-stripping workflow
Derived from the codebase of the QAP:
https://github.com/preprocessed-connectomes-project/\
quality-assessment-protocol/blob/master/qap/anatomical_preproc.py#L105
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['bias_corrected', 'out_file', 'out_mask', 'bias_image']),
name='outputnode')
inu_n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='CorrectINU')
sstrip = pe.Node(afni.SkullStrip(outputtype='NIFTI_GZ'), name='skullstrip')
sstrip_orig_vol = pe.Node(afni.Calc(expr='a*step(b)',
outputtype='NIFTI_GZ'),
name='sstrip_orig_vol')
binarize = pe.Node(fsl.Threshold(args='-bin', thresh=1.e-3),
name='binarize')
workflow.connect([(inputnode, sstrip_orig_vol, [('in_file', 'in_file_a')]),
(inputnode, inu_n4, [('in_file', 'input_image')]),
(inu_n4, sstrip, [('output_image', 'in_file')]),
(sstrip, sstrip_orig_vol, [('out_file', 'in_file_b')]),
(sstrip_orig_vol, binarize, [('out_file', 'in_file')]),
(sstrip_orig_vol, outputnode, [('out_file', 'out_file')
]),
(binarize, outputnode, [('out_file', 'out_mask')]),
(inu_n4, outputnode, [('output_image', 'bias_corrected'),
('bias_image', 'bias_image')])])
return workflow
def n4_bias_correction(wf, cfg, strat_pool, pipe_num, opt=None):
'''
{"name": "n4_bias_correction",
"config": ["anatomical_preproc"],
"switch": ["n4_bias_field_correction"],
"option_key": "None",
"option_val": "None",
"inputs": [["desc-preproc_T1w", "desc-reorient_T1w", "T1w"]],
"outputs": ["desc-preproc_T1w"]}
'''
n4 = pe.Node(interface=ants.N4BiasFieldCorrection(dimension=3,
shrink_factor=2,
copy_header=True),
name=f'anat_n4_{pipe_num}')
node, out = strat_pool.get_data(
['desc-preproc_T1w', 'desc-reorient_T1w', 'T1w'])
wf.connect(node, out, n4, 'input_image')
outputs = {'desc-preproc_T1w': (n4, 'output_image')}
return (wf, outputs)
def bruker(
measurements_base,
template,
DEBUG=False,
exclude={},
functional_match={},
structural_match={},
sessions=[],
subjects=[],
actual_size=True,
functional_blur_xy=False,
functional_registration_method="structural",
highpass_sigma=225,
lowpass_sigma=None,
negative_contrast_agent=False,
n_procs=N_PROCS,
realign="time",
registration_mask=False,
tr=1,
very_nasty_bruker_delay_hack=False,
workflow_name="generic",
keep_work=False,
autorotate=False,
strict=False,
verbose=False,
):
'''
realign: {"space","time","spacetime",""}
Parameter that dictates slictiming correction and realignment of slices. "time" (FSL.SliceTimer) is default, since it works safely. Use others only with caution!
'''
if template:
if template == "mouse":
template = fetch_mouse_DSURQE()['template']
registration_mask = fetch_mouse_DSURQE()['mask']
elif template == "rat":
template = fetch_rat_waxholm()['template']
registration_mask = fetch_rat_waxholm()['mask']
else:
pass
else:
raise ValueError("No species or template specified")
return -1
measurements_base = path.abspath(path.expanduser(measurements_base))
# add subject and session filters if present
if subjects:
structural_scan_types['subject'] = subjects
if sessions:
structural_scan_types['session'] = sessions
# define measurement directories to be processed, and populate the list either with the given include_measurements, or with an intelligent selection
data_selection = pd.DataFrame([])
if structural_match:
s_data_selection = get_data_selection(
measurements_base,
match=structural_match,
exclude=exclude,
)
structural_scan_types = s_data_selection['scan_type'].unique()
data_selection = pd.concat([data_selection, s_data_selection])
if functional_match:
f_data_selection = get_data_selection(
measurements_base,
match=functional_match,
exclude=exclude,
)
functional_scan_types = f_data_selection['scan_type'].unique()
data_selection = pd.concat([data_selection, f_data_selection])
# we currently only support one structural scan type per session
#if functional_registration_method in ("structural", "composite") and structural_scan_types:
# structural_scan_types = [structural_scan_types[0]]
# we start to define nipype workflow elements (nodes, connections, meta)
subjects_sessions = data_selection[["subject", "session"
]].drop_duplicates().values.tolist()
if debug:
print('Data selection:')
print(data_selection)
print('Iterating over:')
print(subjects_sessions)
infosource = pe.Node(interface=util.IdentityInterface(
fields=['subject_session'], mandatory_inputs=False),
name="infosource")
infosource.iterables = [('subject_session', subjects_sessions)]
get_f_scan = pe.Node(name='get_f_scan',
interface=util.Function(
function=get_scan,
input_names=inspect.getargspec(get_scan)[0],
output_names=['scan_path', 'scan_type', 'trial']))
if not strict:
get_f_scan.inputs.ignore_exception = True
get_f_scan.inputs.data_selection = data_selection
get_f_scan.inputs.measurements_base = measurements_base
get_f_scan.iterables = ("scan_type", functional_scan_types)
f_bru2nii = pe.Node(interface=bru2nii.Bru2(), name="f_bru2nii")
f_bru2nii.inputs.actual_size = actual_size
dummy_scans = pe.Node(
name='dummy_scans',
interface=util.Function(
function=force_dummy_scans,
input_names=inspect.getargspec(force_dummy_scans)[0],
output_names=['out_file']))
dummy_scans.inputs.desired_dummy_scans = DUMMY_SCANS
bandpass = pe.Node(interface=fsl.maths.TemporalFilter(), name="bandpass")
bandpass.inputs.highpass_sigma = highpass_sigma
if lowpass_sigma:
bandpass.inputs.lowpass_sigma = lowpass_sigma
else:
bandpass.inputs.lowpass_sigma = tr
#bids_filename = pe.Node(name='bids_filename', interface=util.Function(function=sss_filename,input_names=inspect.getargspec(sss_filename)[0], output_names=['filename']))
bids_filename = pe.Node(name='bids_filename',
interface=util.Function(
function=bids_naming,
input_names=inspect.getargspec(bids_naming)[0],
output_names=['filename']))
bids_filename.inputs.metadata = data_selection
#bids_stim_filename = pe.Node(name='bids_stim_filename', interface=util.Function(function=sss_filename,input_names=inspect.getargspec(sss_filename)[0], output_names=['filename']))
bids_stim_filename = pe.Node(
name='bids_stim_filename',
interface=util.Function(function=bids_naming,
input_names=inspect.getargspec(bids_naming)[0],
output_names=['filename']))
bids_stim_filename.inputs.suffix = "events"
bids_stim_filename.inputs.extension = ".tsv"
bids_stim_filename.inputs.metadata = data_selection
events_file = pe.Node(
name='events_file',
interface=util.Function(
function=write_events_file,
input_names=inspect.getargspec(write_events_file)[0],
output_names=['out_file']))
events_file.inputs.dummy_scans_ms = DUMMY_SCANS * tr * 1000
events_file.inputs.stim_protocol_dictionary = STIM_PROTOCOL_DICTIONARY
events_file.inputs.very_nasty_bruker_delay_hack = very_nasty_bruker_delay_hack
if not (strict or verbose):
events_file.inputs.ignore_exception = True
datasink = pe.Node(nio.DataSink(), name='datasink')
datasink.inputs.base_directory = path.join(measurements_base,
"preprocessing", workflow_name)
datasink.inputs.parameterization = False
if not (strict or verbose):
datasink.inputs.ignore_exception = True
workflow_connections = [
(infosource, get_f_scan, [('subject_session', 'selector')]),
(infosource, bids_stim_filename, [('subject_session',
'subject_session')]),
(get_f_scan, bids_stim_filename, [('scan_type', 'scan_type')]),
(get_f_scan, f_bru2nii, [('scan_path', 'input_dir')]),
(f_bru2nii, dummy_scans, [('nii_file', 'in_file')]),
(get_f_scan, dummy_scans, [('scan_path', 'scan_dir')]),
(get_f_scan, events_file, [('trial', 'trial'),
('scan_path', 'scan_dir')]),
(events_file, datasink, [('out_file', 'func.@events')]),
(bids_stim_filename, events_file, [('filename', 'out_file')]),
(infosource, datasink, [(('subject_session', ss_to_path), 'container')
]),
(infosource, bids_filename, [('subject_session', 'subject_session')]),
(get_f_scan, bids_filename, [('scan_type', 'scan_type')]),
(bids_filename, bandpass, [('filename', 'out_file')]),
(bandpass, datasink, [('out_file', 'func')]),
]
if realign == "space":
realigner = pe.Node(interface=spm.Realign(), name="realigner")
realigner.inputs.register_to_mean = True
workflow_connections.extend([
(dummy_scans, realigner, [('out_file', 'in_file')]),
])
elif realign == "spacetime":
realigner = pe.Node(interface=nipy.SpaceTimeRealigner(),
name="realigner")
realigner.inputs.slice_times = "asc_alt_2"
realigner.inputs.tr = tr
realigner.inputs.slice_info = 3 #3 for coronal slices (2 for horizontal, 1 for sagittal)
workflow_connections.extend([
(dummy_scans, realigner, [('out_file', 'in_file')]),
])
elif realign == "time":
realigner = pe.Node(interface=fsl.SliceTimer(), name="slicetimer")
realigner.inputs.time_repetition = tr
workflow_connections.extend([
(dummy_scans, realigner, [('out_file', 'in_file')]),
])
#ADDING SELECTABLE NODES AND EXTENDING WORKFLOW AS APPROPRIATE:
if actual_size:
s_biascorrect, f_biascorrect = real_size_nodes()
else:
s_biascorrect, f_biascorrect = inflated_size_nodes()
if structural_scan_types.any():
get_s_scan = pe.Node(
name='get_s_scan',
interface=util.Function(
function=get_scan,
input_names=inspect.getargspec(get_scan)[0],
output_names=['scan_path', 'scan_type', 'trial']))
if not strict:
get_s_scan.inputs.ignore_exception = True
get_s_scan.inputs.data_selection = data_selection
get_s_scan.inputs.measurements_base = measurements_base
get_s_scan.iterables = ("scan_type", structural_scan_types)
s_bru2nii = pe.Node(interface=bru2nii.Bru2(), name="s_bru2nii")
s_bru2nii.inputs.force_conversion = True
s_bru2nii.inputs.actual_size = actual_size
#s_bids_filename = pe.Node(name='s_bids_filename', interface=util.Function(function=sss_filename,input_names=inspect.getargspec(sss_filename)[0], output_names=['filename']))
s_bids_filename = pe.Node(
name='s_bids_filename',
interface=util.Function(
function=bids_naming,
input_names=inspect.getargspec(bids_naming)[0],
output_names=['filename']))
s_bids_filename.inputs.metadata = data_selection
if actual_size:
s_register, s_warp, _, _ = DSURQEc_structural_registration(
template, registration_mask)
#TODO: incl. in func registration
if autorotate:
workflow_connections.extend([
(s_biascorrect, s_rotated, [('output_image', 'out_file')]),
(s_rotated, s_register, [('out_file', 'moving_image')]),
])
else:
workflow_connections.extend([
(s_biascorrect, s_register, [('output_image',
'moving_image')]),
(s_register, s_warp, [('composite_transform', 'transforms')
]),
(s_bru2nii, s_warp, [('nii_file', 'input_image')]),
(s_warp, datasink, [('output_image', 'anat')]),
])
else:
s_reg_biascorrect = pe.Node(interface=ants.N4BiasFieldCorrection(),
name="s_reg_biascorrect")
s_reg_biascorrect.inputs.dimension = 3
s_reg_biascorrect.inputs.bspline_fitting_distance = 95
s_reg_biascorrect.inputs.shrink_factor = 2
s_reg_biascorrect.inputs.n_iterations = [500, 500, 500, 500]
s_reg_biascorrect.inputs.convergence_threshold = 1e-14
s_cutoff = pe.Node(interface=fsl.ImageMaths(), name="s_cutoff")
s_cutoff.inputs.op_string = "-thrP 20 -uthrp 98"
s_BET = pe.Node(interface=fsl.BET(), name="s_BET")
s_BET.inputs.mask = True
s_BET.inputs.frac = 0.3
s_BET.inputs.robust = True
s_mask = pe.Node(interface=fsl.ApplyMask(), name="s_mask")
s_register, s_warp, f_warp = structural_registration(template)
workflow_connections.extend([
(s_bru2nii, s_reg_biascorrect, [('nii_file', 'input_image')]),
(s_reg_biascorrect, s_cutoff, [('output_image', 'in_file')]),
(s_cutoff, s_BET, [('out_file', 'in_file')]),
(s_biascorrect, s_mask, [('output_image', 'in_file')]),
(s_BET, s_mask, [('mask_file', 'mask_file')]),
])
#TODO: incl. in func registration
if autorotate:
workflow_connections.extend([
(s_mask, s_rotated, [('out_file', 'out_file')]),
(s_rotated, s_register, [('out_file', 'moving_image')]),
])
else:
workflow_connections.extend([
(s_mask, s_register, [('out_file', 'moving_image')]),
(s_register, s_warp, [('composite_transform', 'transforms')
]),
(s_bru2nii, s_warp, [('nii_file', 'input_image')]),
(s_warp, datasink, [('output_image', 'anat')]),
])
if autorotate:
s_rotated = autorotate(template)
workflow_connections.extend([
(infosource, get_s_scan, [('subject_session', 'selector')]),
(infosource, s_bids_filename, [('subject_session',
'subject_session')]),
(get_s_scan, s_bru2nii, [('scan_path', 'input_dir')]),
(get_s_scan, s_bids_filename, [('scan_type', 'scan_type')]),
(s_bids_filename, s_warp, [('filename', 'output_image')]),
(s_bru2nii, s_biascorrect, [('nii_file', 'input_image')]),
])
if functional_registration_method == "structural":
if not structural_scan_types:
raise ValueError(
'The option `registration="structural"` requires there to be a structural scan type.'
)
workflow_connections.extend([
(s_register, f_warp, [('composite_transform', 'transforms')]),
])
if realign == "space":
workflow_connections.extend([
(realigner, f_warp, [('realigned_files', 'input_image')]),
])
elif realign == "spacetime":
workflow_connections.extend([
(realigner, f_warp, [('out_file', 'input_image')]),
])
elif realign == "time":
workflow_connections.extend([
(realigner, f_warp, [('slice_time_corrected_file',
'input_image')]),
])
else:
workflow_connections.extend([
(dummy_scans, f_warp, [('out_file', 'input_image')]),
])
if functional_registration_method == "composite":
if not structural_scan_types.any():
raise ValueError(
'The option `registration="composite"` requires there to be a structural scan type.'
)
_, _, f_register, f_warp = DSURQEc_structural_registration(
template, registration_mask)
temporal_mean = pe.Node(interface=fsl.MeanImage(),
name="temporal_mean")
merge = pe.Node(util.Merge(2), name='merge')
workflow_connections.extend([
(temporal_mean, f_biascorrect, [('out_file', 'input_image')]),
(f_biascorrect, f_register, [('output_image', 'moving_image')]),
(s_biascorrect, f_register, [('output_image', 'fixed_image')]),
(f_register, merge, [('composite_transform', 'in1')]),
(s_register, merge, [('composite_transform', 'in2')]),
(merge, f_warp, [('out', 'transforms')]),
])
if realign == "space":
workflow_connections.extend([
(realigner, temporal_mean, [('realigned_files', 'in_file')]),
(realigner, f_warp, [('realigned_files', 'input_image')]),
])
elif realign == "spacetime":
workflow_connections.extend([
(realigner, temporal_mean, [('out_file', 'in_file')]),
(realigner, f_warp, [('out_file', 'input_image')]),
])
elif realign == "time":
workflow_connections.extend([
(realigner, temporal_mean, [('slice_time_corrected_file',
'in_file')]),
(realigner, f_warp, [('slice_time_corrected_file',
'input_image')]),
])
else:
workflow_connections.extend([
(dummy_scans, temporal_mean, [('out_file', 'in_file')]),
(dummy_scans, f_warp, [('out_file', 'input_image')]),
])
elif functional_registration_method == "functional":
f_register, f_warp = functional_registration(template)
temporal_mean = pe.Node(interface=fsl.MeanImage(),
name="temporal_mean")
#f_cutoff = pe.Node(interface=fsl.ImageMaths(), name="f_cutoff")
#f_cutoff.inputs.op_string = "-thrP 30"
#f_BET = pe.Node(interface=fsl.BET(), name="f_BET")
#f_BET.inputs.mask = True
#f_BET.inputs.frac = 0.5
workflow_connections.extend([
(temporal_mean, f_biascorrect, [('out_file', 'input_image')]),
#(f_biascorrect, f_cutoff, [('output_image', 'in_file')]),
#(f_cutoff, f_BET, [('out_file', 'in_file')]),
#(f_BET, f_register, [('out_file', 'moving_image')]),
(f_biascorrect, f_register, [('output_image', 'moving_image')]),
(f_register, f_warp, [('composite_transform', 'transforms')]),
])
if realign == "space":
workflow_connections.extend([
(realigner, temporal_mean, [('realigned_files', 'in_file')]),
(realigner, f_warp, [('realigned_files', 'input_image')]),
])
elif realign == "spacetime":
workflow_connections.extend([
(realigner, temporal_mean, [('out_file', 'in_file')]),
(realigner, f_warp, [('out_file', 'input_image')]),
])
elif realign == "time":
workflow_connections.extend([
(realigner, temporal_mean, [('slice_time_corrected_file',
'in_file')]),
(realigner, f_warp, [('slice_time_corrected_file',
'input_image')]),
])
else:
workflow_connections.extend([
(dummy_scans, temporal_mean, [('out_file', 'in_file')]),
(dummy_scans, f_warp, [('out_file', 'input_image')]),
])
invert = pe.Node(interface=fsl.ImageMaths(), name="invert")
if functional_blur_xy and negative_contrast_agent:
blur = pe.Node(interface=afni.preprocess.BlurToFWHM(), name="blur")
blur.inputs.fwhmxy = functional_blur_xy
workflow_connections.extend([
(f_warp, blur, [('output_image', 'in_file')]),
(blur, invert, [(('out_file', fslmaths_invert_values), 'op_string')
]),
(blur, invert, [('out_file', 'in_file')]),
(invert, bandpass, [('out_file', 'in_file')]),
])
elif functional_blur_xy:
blur = pe.Node(interface=afni.preprocess.BlurToFWHM(), name="blur")
blur.inputs.fwhmxy = functional_blur_xy
workflow_connections.extend([
(f_warp, blur, [('output_image', 'in_file')]),
(blur, bandpass, [('out_file', 'in_file')]),
])
elif negative_contrast_agent:
blur = pe.Node(interface=afni.preprocess.BlurToFWHM(), name="blur")
blur.inputs.fwhmxy = functional_blur_xy
workflow_connections.extend([
(f_warp, invert, [(('output_image', fslmaths_invert_values),
'op_string')]),
(f_warp, invert, [('output_image', 'in_file')]),
(invert, bandpass, [('out_file', 'in_file')]),
])
else:
workflow_connections.extend([
(f_warp, bandpass, [('output_image', 'in_file')]),
])
workflow_config = {
'execution': {
'crashdump_dir':
path.join(measurements_base, 'preprocessing/crashdump'),
}
}
if debug:
workflow_config['logging'] = {
'workflow_level': 'DEBUG',
'utils_level': 'DEBUG',
'interface_level': 'DEBUG',
'filemanip_level': 'DEBUG',
'log_to_file': 'true',
}
workdir_name = workflow_name + "_work"
workflow = pe.Workflow(name=workdir_name)
workflow.connect(workflow_connections)
workflow.base_dir = path.join(measurements_base, "preprocessing")
workflow.config = workflow_config
workflow.write_graph(dotfilename=path.join(workflow.base_dir, workdir_name,
"graph.dot"),
graph2use="hierarchical",
format="png")
workflow.run(plugin="MultiProc", plugin_args={'n_procs': n_procs})
if not keep_work:
shutil.rmtree(path.join(workflow.base_dir, workdir_name))
def anat_qc_workflow(dataset, settings, name='anatMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
workflow = pe.Workflow(name=name)
WFLOGGER.info(
'Building anatomical MRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
# Define workflow, inputs and outputs
# 0. Get data
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
inputnode.iterables = [('in_file', dataset)]
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1a. Reorient anatomical image
to_ras = pe.Node(ConformImage(), name='conform')
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = pe.Node(RobustMNINormalization(
num_threads=settings.get('ants_nthreads', 6),
template='mni_icbm152_nlin_asym_09c',
testing=settings.get('testing', False),
generate_report=True),
name='SpatialNormalization')
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(img_type=1,
segments=True,
out_basename='segment'),
name='segmentation')
# 7. Compute IQMs
iqmswf = compute_iqms(settings)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, meta, [('in_file', 'in_file')]),
(to_ras, n4itk, [('out_file', 'input_image')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('run_id', 'inputnode.run_id')]),
(n4itk, asw, [('output_image', 'inputnode.in_file')]),
(asw, segment, [('outputnode.out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, norm, [('output_image', 'moving_image')]),
(asw, norm, [('outputnode.out_mask', 'moving_mask')]),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(norm, amw, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags', 'inputnode.reverse_invert_flags')
]),
(norm, iqmswf, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags',
'inputnode.reverse_invert_flags')]),
(norm, repwf, ([('out_report', 'inputnode.mni_report')])),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.orig')]),
(n4itk, iqmswf, [('output_image', 'inputnode.inu_corrected'),
('bias_image', 'inputnode.in_inu')]),
(asw, iqmswf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(meta, iqmswf, [('out_dict', 'inputnode.metadata')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.orig')]),
(n4itk, repwf, [('output_image', 'inputnode.inu_corrected')]),
(asw, repwf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.out_noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
return workflow
def create_extract_noT1_pipe(params_template, params={},
name="extract_noT1_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Inputs:
inputnode:
restore_T1: preprocessed (debiased/denoised) T1 file name
restore_T1: preprocessed (debiased/denoised)T2 file name
arguments:
params_template: dictionary of info about template
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['restore_T1',
"indiv_params"]),
name='inputnode')
# N4 intensity normalization with parameters from json
norm_intensity = NodeParams(ants.N4BiasFieldCorrection(),
params=parse_key(params, "norm_intensity"),
name='norm_intensity')
extract_pipe.connect(inputnode, 'restore_T1',
norm_intensity, "input_image")
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(norm_intensity, "output_image",
atlas_brex, 't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(
inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
return extract_pipe
def __init__(self, settings):
# call base constructor
super().__init__(settings)
# define input/output node
self.set_input(['T1', 'orig', 'brainmask'])
self.set_output(['T1_skullstrip', 'allineate_freesurfer2anat'])
# define datasink substitutions
self.set_subs([('_maskop40', ''), ('_calc_calc_calc_calc_calc', '')])
# 3dAllineate (FSorig)
self.allineate_orig = MapNode(afni.Allineate(
out_matrix='FSorig2MPR.aff12.1D',
overwrite=True,
outputtype='NIFTI_GZ'),
iterfield=['in_file', 'reference'],
name='3dallineate_orig')
# 3dAllineate (FSbrainmask)
self.allineate_bm = MapNode(
afni.Allineate(overwrite=True, no_pad=True, outputtype='NIFTI_GZ'),
iterfield=['in_file', 'reference', 'in_matrix'],
name='3dallineate_brainmask')
# skullstrip mprage (afni)
self.afni_skullstrip = MapNode(afni.SkullStrip(args="-orig_vol",
outputtype="NIFTI_GZ"),
iterfield=['in_file'],
name='afni_skullstrip')
# 3dcalc operations for achieving final mask
self.maskop1 = MapNode(afni.Calc(expr='step(a)',
overwrite=True,
outputtype='NIFTI_GZ'),
iterfield=['in_file_a'],
name='maskop1')
self.maskop2 = []
for n in range(3):
self.maskop2.append(
MapNode(afni.Calc(
args='-b a+i -c a-i -d a+j -e a-j -f a+k -g a-k',
expr='ispositive(a+b+c+d+e+f+g)',
overwrite=True,
outputtype='NIFTI_GZ'),
iterfield=['in_file_a'],
name='maskop2_{}'.format(n)))
# Inline function for setting up to copy IJK_TO_DICOM_REAL file attribute
self.refit_setup = MapNode(Function(input_names=['noskull_T1'],
output_names=['refit_input'],
function=lambda noskull_T1:
(noskull_T1, 'IJK_TO_DICOM_REAL')),
iterfield=['noskull_T1'],
name='refitsetup')
# 3dRefit
self.refit = MapNode(afni.Refit(),
iterfield=['in_file', 'atrcopy'],
name='3drefit')
# 3dcalc for uniform intensity
self.uniform = MapNode(afni.Calc(expr='a*and(b,b)',
overwrite=True,
outputtype='NIFTI_GZ'),
iterfield=['in_file_a', 'in_file_b'],
name='uniformintensity')
# skullstrip mprage (fsl)
self.fsl_skullstrip = MapNode(fsl.BET(),
iterfield=['in_file'],
name='fsl_skullstrip')
self.maskop3 = MapNode(
afni.Calc(expr='or(a,b,c)', overwrite=True, outputtype='NIFTI_GZ'),
iterfield=['in_file_a', 'in_file_b', 'in_file_c'],
name='maskop3')
self.maskop4 = MapNode(
afni.Calc(expr='c*and(a,b)', overwrite=True,
outputtype='NIFTI_GZ'),
iterfield=['in_file_a', 'in_file_b', 'in_file_c'],
name='maskop4')
# Convert from list to string input
self.select0T1 = Node(Function(input_names=['T1_list'],
output_names=['T1_0'],
function=lambda T1_list: T1_list[0]),
name='select0T1')
# apply bias field correction
self.biasfieldcorrect = Node(ants.N4BiasFieldCorrection(
num_threads=settings['num_threads'], copy_header=True),
name='biasfieldcorrect')
def init_phdiff_wf(omp_nthreads, phasetype='phasediff', name='phdiff_wf'):
"""
Estimates the fieldmap using a phase-difference image and one or more
magnitude images corresponding to two or more :abbr:`GRE (Gradient Echo sequence)`
acquisitions. The `original code was taken from nipype
<https://github.com/nipy/nipype/blob/master/nipype/workflows/dmri/fsl/artifacts.py#L514>`_.
.. workflow ::
:graph2use: orig
:simple_form: yes
from qsiprep.workflows.fieldmap.phdiff import init_phdiff_wf
wf = init_phdiff_wf(omp_nthreads=1)
Outputs::
outputnode.fmap_ref - The average magnitude image, skull-stripped
outputnode.fmap_mask - The brain mask applied to the fieldmap
outputnode.fmap - The estimated fieldmap in Hz
"""
workflow = Workflow(name=name)
workflow.__desc__ = """\
A deformation field to correct for susceptibility distortions was estimated
based on a field map that was co-registered to the BOLD reference,
using a custom workflow of *fMRIPrep* derived from D. Greve's `epidewarp.fsl`
[script](http://www.nmr.mgh.harvard.edu/~greve/fbirn/b0/epidewarp.fsl) and
further improvements of HCP Pipelines [@hcppipelines].
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=['magnitude', 'phasediff']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# de-gradient the fields ("bias/illumination artifact")
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
name='n4',
n_procs=omp_nthreads)
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_report_fmap_mask = pe.Node(DerivativesDataSink(desc='brain',
suffix='mask'),
name='ds_report_fmap_mask',
mem_gb=0.01,
run_without_submitting=True)
# uses mask from bet; outputs a mask
# dilate = pe.Node(fsl.maths.MathsCommand(
# nan2zeros=True, args='-kernel sphere 5 -dilM'), name='MskDilate')
# FSL PRELUDE will perform phase-unwrapping
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
denoise = pe.Node(fsl.SpatialFilter(operation='median',
kernel_shape='sphere',
kernel_size=5),
name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name="cleanup_wf")
compfmap = pe.Node(Phasediff2Fieldmap(), name='compfmap')
# The phdiff2fmap interface is equivalent to:
# rad2rsec (using rads2radsec from nipype.workflows.dmri.fsl.utils)
# pre_fugue = pe.Node(fsl.FUGUE(save_fmap=True), name='ComputeFieldmapFUGUE')
# rsec2hz (divide by 2pi)
if phasetype == "phasediff":
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(), name='meta', mem_gb=0.01)
# phase diff -> radians
pha2rads = pe.Node(niu.Function(function=siemens2rads),
name='pha2rads')
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True)
workflow.connect([
(meta, compfmap, [('out_dict', 'metadata')]),
(inputnode, pha2rads, [('phasediff', 'in_file')]),
(pha2rads, prelude, [('out', 'phase_file')]),
(inputnode, ds_report_fmap_mask, [('phasediff', 'source_file')]),
])
elif phasetype == "phase":
workflow.__desc__ += """\
The phase difference used for unwarping was calculated using two separate phase measurements
[@pncprocessing].
"""
# Special case for phase1, phase2 images
meta = pe.MapNode(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True,
iterfield=['in_file'])
phases2fmap = pe.Node(Phases2Fieldmap(), name='phases2fmap')
workflow.connect([
(meta, phases2fmap, [('out_dict', 'metadatas')]),
(inputnode, phases2fmap, [('phasediff', 'phase_files')]),
(phases2fmap, prelude, [('out_file', 'phase_file')]),
(phases2fmap, compfmap, [('phasediff_metadata', 'metadata')]),
(phases2fmap, ds_report_fmap_mask, [('out_file', 'source_file')])
])
workflow.connect([
(inputnode, meta, [('phasediff', 'in_file')]),
(inputnode, magmrg, [('magnitude', 'in_files')]),
(magmrg, n4, [('out_avg', 'input_image')]),
(n4, prelude, [('output_image', 'magnitude_file')]),
(n4, bet, [('output_image', 'in_file')]),
(bet, prelude, [('mask_file', 'mask_file')]),
(prelude, denoise, [('unwrapped_phase_file', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, compfmap, [('outputnode.out_file', 'in_file')]),
(compfmap, outputnode, [('out_file', 'fmap')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(bet, ds_report_fmap_mask, [('out_report', 'in_file')]),
])
return workflow
def init_fmap_wf(omp_nthreads, fmap_bspline, name='fmap_wf'):
"""
Fieldmap workflow - when we have a sequence that directly measures the fieldmap
we just need to mask it (using the corresponding magnitude image) to remove the
noise in the surrounding air region, and ensure that units are Hz.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.fmap import init_fmap_wf
wf = init_fmap_wf(omp_nthreads=6, fmap_bspline=False)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['magnitude', 'fieldmap']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# Merge input fieldmap images
fmapmrg = pe.Node(IntraModalMerge(zero_based_avg=False, hmc=False),
name='fmapmrg')
# de-gradient the fields ("bias/illumination artifact")
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True),
name='n4_correct',
n_procs=omp_nthreads)
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_fmap_mask = pe.Node(DerivativesDataSink(suffix='fmap_mask'),
name='ds_report_fmap_mask',
run_without_submitting=True)
workflow.connect([
(inputnode, magmrg, [('magnitude', 'in_files')]),
(inputnode, fmapmrg, [('fieldmap', 'in_files')]),
(magmrg, n4_correct, [('out_file', 'input_image')]),
(n4_correct, bet, [('output_image', 'in_file')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(inputnode, ds_fmap_mask, [('fieldmap', 'source_file')]),
(bet, ds_fmap_mask, [('out_report', 'in_file')]),
])
if fmap_bspline:
# despike_threshold=1.0, mask_erode=1),
fmapenh = pe.Node(FieldEnhance(unwrap=False, despike=False),
name='fmapenh',
mem_gb=4,
n_procs=omp_nthreads)
workflow.connect([
(bet, fmapenh, [('mask_file', 'in_mask'),
('out_file', 'in_magnitude')]),
(fmapmrg, fmapenh, [('out_file', 'in_file')]),
(fmapenh, outputnode, [('out_file', 'fmap')]),
])
else:
torads = pe.Node(FieldToRadS(), name='torads')
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
tohz = pe.Node(FieldToHz(), name='tohz')
denoise = pe.Node(fsl.SpatialFilter(operation='median',
kernel_shape='sphere',
kernel_size=3),
name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name='cleanup_wf')
applymsk = pe.Node(fsl.ApplyMask(), name='applymsk')
workflow.connect([
(bet, prelude, [('mask_file', 'mask_file'),
('out_file', 'magnitude_file')]),
(fmapmrg, torads, [('out_file', 'in_file')]),
(torads, tohz, [('fmap_range', 'range_hz')]),
(torads, prelude, [('out_file', 'phase_file')]),
(prelude, tohz, [('unwrapped_phase_file', 'in_file')]),
(tohz, denoise, [('out_file', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, applymsk, [('outputnode.out_file', 'in_file')]),
(bet, applymsk, [('mask_file', 'mask_file')]),
(applymsk, outputnode, [('out_file', 'fmap')]),
])
return workflow
datasink_anat.inputs.substitutions = substitutions
# ========================================================================================================
# In[6]:
template_brain = '/media/amr/Amr_4TB/Work/October_Acquistion/anat_temp_enhanced_3.nii.gz'
template_mask = '/media/amr/Amr_4TB/Work/October_Acquistion/anat_template_enhanced_mask_2.nii.gz'
TR = 2.0
# =======================================================================================================
# In[7]:
# Remove skull using antsBrainExtraction.sh, i am using the study-based template that I build and remove
# the skull manually using ITKsnap
biasfield_correction_anat = Node(ants.N4BiasFieldCorrection(),
name='biasfield_correction_anat')
biasfield_correction_anat.inputs.dimension = 3
biasfield_correction_anat.inputs.save_bias = True
# biasfield_correction_anat.inputs.output_image = 'anat_bet_biasfield_corrected.nii.gz' #better not to,
# it confuses the Registration
# ======================================================================================================
# In[8]:
# Extract one fmri image to use fro brain extraction, the same one you will use for mcflirt as reference
roi = Node(fsl.ExtractROI(), name='extract_one_fMRI_volume')
roi.inputs.t_min = 75
roi.inputs.t_size = 1
# ======================================================================================================
def init_enhance_and_skullstrip_bold_wf(name='enhance_and_skullstrip_bold_wf',
pre_mask=False,
omp_nthreads=1):
"""
This workflow takes in a :abbr:`BOLD (blood-oxygen level-dependant)`
:abbr:`fMRI (functional MRI)` average/summary (e.g., a reference image
averaging non-steady-state timepoints), and sharpens the histogram
with the application of the N4 algorithm for removing the
:abbr:`INU (intensity non-uniformity)` bias field and calculates a signal
mask.
Steps of this workflow are:
1. Calculate a tentative mask by registering (9-parameters) to *fMRIPrep*'s
:abbr:`EPI (echo-planar imaging)` -*boldref* template, which
is in MNI space.
The tentative mask is obtained by resampling the MNI template's
brainmask into *boldref*-space.
2. Binary dilation of the tentative mask with a sphere of 3mm diameter.
3. Run ANTs' ``N4BiasFieldCorrection`` on the input
:abbr:`BOLD (blood-oxygen level-dependant)` average, using the
mask generated in 1) instead of the internal Otsu thresholding.
4. Calculate a loose mask using FSL's ``bet``, with one mathematical morphology
dilation of one iteration and a sphere of 6mm as structuring element.
5. Mask the :abbr:`INU (intensity non-uniformity)`-corrected image
with the latest mask calculated in 3), then use AFNI's ``3dUnifize``
to *standardize* the T2* contrast distribution.
6. Calculate a mask using AFNI's ``3dAutomask`` after the contrast
enhancement of 4).
7. Calculate a final mask as the intersection of 4) and 6).
8. Apply final mask on the enhanced reference.
Step 1 can be skipped if the ``pre_mask`` argument is set to ``True`` and
a tentative mask is passed in to the workflow throught the ``pre_mask``
Nipype input.
.. workflow ::
:graph2use: orig
:simple_form: yes
from niworkflows.func.util import init_enhance_and_skullstrip_bold_wf
wf = init_enhance_and_skullstrip_bold_wf(omp_nthreads=1)
**Parameters**
name : str
Name of workflow (default: ``enhance_and_skullstrip_bold_wf``)
pre_mask : bool
Indicates whether the ``pre_mask`` input will be set (and thus, step 1
should be skipped).
omp_nthreads : int
number of threads available to parallel nodes
**Inputs**
in_file
BOLD image (single volume)
pre_mask : bool
A tentative brain mask to initialize the workflow (requires ``pre_mask``
parameter set ``True``).
**Outputs**
bias_corrected_file
the ``in_file`` after `N4BiasFieldCorrection`_
skull_stripped_file
the ``bias_corrected_file`` after skull-stripping
mask_file
mask of the skull-stripped input file
out_report
reportlet for the skull-stripping
.. _N4BiasFieldCorrection: https://hdl.handle.net/10380/3053
"""
workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'pre_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['mask_file', 'skull_stripped_file', 'bias_corrected_file']),
name='outputnode')
# Dilate pre_mask
pre_dilate = pe.Node(fsl.DilateImage(operation='max',
kernel_shape='sphere',
kernel_size=3.0,
internal_datatype='char'),
name='pre_mask_dilate')
# Ensure mask's header matches reference's
check_hdr = pe.Node(MatchHeader(),
name='check_hdr',
run_without_submitting=True)
# Run N4 normally, force num_threads=1 for stability (images are small, no need for >1)
n4_correct = pe.Node(ants.N4BiasFieldCorrection(
dimension=3, copy_header=True, bspline_fitting_distance=200),
name='n4_correct',
n_procs=1)
# Create a generous BET mask out of the bias-corrected EPI
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
bet_dilate = pe.Node(fsl.DilateImage(operation='max',
kernel_shape='sphere',
kernel_size=6.0,
internal_datatype='char'),
name='skullstrip_first_dilate')
bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')
# Use AFNI's unifize for T2 constrast & fix header
unifize = pe.Node(
afni.Unifize(
t2=True,
outputtype='NIFTI_GZ',
# Default -clfrac is 0.1, 0.4 was too conservative
# -rbt because I'm a Jedi AFNI Master (see 3dUnifize's documentation)
args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
out_file="uni.nii.gz"),
name='unifize')
fixhdr_unifize = pe.Node(CopyXForm(), name='fixhdr_unifize', mem_gb=0.1)
# Run ANFI's 3dAutomask to extract a refined brain mask
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
fixhdr_skullstrip2 = pe.Node(CopyXForm(),
name='fixhdr_skullstrip2',
mem_gb=0.1)
# Take intersection of both masks
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
# Compute masked brain
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
if not pre_mask:
bold_template = get_template('MNI152NLin2009cAsym',
resolution=2,
desc='fMRIPrep',
suffix='boldref')
brain_mask = get_template('MNI152NLin2009cAsym',
resolution=2,
desc='brain',
suffix='mask')
# Initialize transforms with antsAI
init_aff = pe.Node(AI(fixed_image=str(bold_template),
fixed_image_mask=str(brain_mask),
metric=('Mattes', 32, 'Regular', 0.2),
transform=('Affine', 0.1),
search_factor=(20, 0.12),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True),
name='init_aff',
n_procs=omp_nthreads)
# Registration().version may be None
if parseversion(Registration().version or '0.0.0') > Version('2.2.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))
# Set up spatial normalization
norm = pe.Node(Registration(from_file=pkgr_fn(
'fmriprep.data', 'epi_atlasbased_brainmask.json')),
name='norm',
n_procs=omp_nthreads)
norm.inputs.fixed_image = str(bold_template)
map_brainmask = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True,
input_image=str(brain_mask)),
name='map_brainmask')
workflow.connect([
(inputnode, init_aff, [('in_file', 'moving_image')]),
(inputnode, map_brainmask, [('in_file', 'reference_image')]),
(inputnode, norm, [('in_file', 'moving_image')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')
]),
(norm, map_brainmask, [('reverse_invert_flags',
'invert_transform_flags'),
('reverse_transforms', 'transforms')]),
(map_brainmask, pre_dilate, [('output_image', 'in_file')]),
])
else:
workflow.connect([
(inputnode, pre_dilate, [('pre_mask', 'in_file')]),
])
workflow.connect([
(inputnode, check_hdr, [('in_file', 'reference')]),
(pre_dilate, check_hdr, [('out_file', 'in_file')]),
(check_hdr, n4_correct, [('out_file', 'mask_image')]),
(inputnode, n4_correct, [('in_file', 'input_image')]),
(inputnode, fixhdr_unifize, [('in_file', 'hdr_file')]),
(inputnode, fixhdr_skullstrip2, [('in_file', 'hdr_file')]),
(n4_correct, skullstrip_first_pass, [('output_image', 'in_file')]),
(skullstrip_first_pass, bet_dilate, [('mask_file', 'in_file')]),
(bet_dilate, bet_mask, [('out_file', 'mask_file')]),
(skullstrip_first_pass, bet_mask, [('out_file', 'in_file')]),
(bet_mask, unifize, [('out_file', 'in_file')]),
(unifize, fixhdr_unifize, [('out_file', 'in_file')]),
(fixhdr_unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, fixhdr_skullstrip2, [('out_file', 'in_file')
]),
(fixhdr_skullstrip2, combine_masks, [('out_file', 'operand_file')]),
(fixhdr_unifize, apply_mask, [('out_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])
return workflow
def create_anat_preproc(template_path=None,
mask_path=None,
regmask_path=None,
method='afni',
already_skullstripped=False,
non_local_means_filtering=True,
n4_correction=True,
wf_name='anat_preproc'):
"""
The main purpose of this workflow is to process T1 scans. Raw mprage file is deobliqued, reoriented
into RPI and skullstripped. Also, a whole brain only mask is generated from the skull stripped image
for later use in registration.
Returns
-------
anat_preproc : workflow
Anatomical Preprocessing Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/anat_preproc/anat_preproc.py>`_
Workflow Inputs::
inputspec.anat : string
User input anatomical (T1) Image, in any of the 8 orientations
Workflow Outputs::
outputspec.refit : string
Path to deobliqued anatomical image
outputspec.reorient : string
Path to RPI oriented anatomical image
outputspec.skullstrip : string
Path to skull stripped RPI oriented mprage file with normalized intensities.
outputspec.brain : string
Path to skull stripped RPI brain image with original intensity values and not normalized or scaled.
Order of commands:
- Deobliqing the scans. ::
3drefit -deoblique mprage.nii.gz
- Re-orienting the Image into Right-to-Left Posterior-to-Anterior Inferior-to-Superior (RPI) orientation ::
3dresample -orient RPI
-prefix mprage_RPI.nii.gz
-inset mprage.nii.gz
- Skull-Stripping the image ::
Using AFNI ::
3dSkullStrip -input mprage_RPI.nii.gz
-o_ply mprage_RPI_3dT.nii.gz
or using BET ::
bet mprage_RPI.nii.gz
- The skull-stripping step modifies the intensity values. To get back the original intensity values, we do an element wise product of RPI data with step function of skull-stripped data ::
3dcalc -a mprage_RPI.nii.gz
-b mprage_RPI_3dT.nii.gz
-expr 'a*step(b)'
-prefix mprage_RPI_3dc.nii.gz
High Level Workflow Graph:
.. image:: ../images/anatpreproc_graph.dot.png
:width: 500
Detailed Workflow Graph:
.. image:: ../images/anatpreproc_graph_detailed.dot.png
:width: 500
Examples
--------
>>> from CPAC.anat_preproc import create_anat_preproc
>>> preproc = create_anat_preproc()
>>> preproc.inputs.inputspec.anat = 'sub1/anat/mprage.nii.gz'
>>> preproc.run() #doctest: +SKIP
"""
preproc = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=['anat', 'brain_mask']),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(
fields=['refit', 'reorient', 'skullstrip', 'brain', 'brain_mask']),
name='outputspec')
anat_deoblique = pe.Node(interface=afni.Refit(), name='anat_deoblique')
anat_deoblique.inputs.deoblique = True
preproc.connect(inputnode, 'anat', anat_deoblique, 'in_file')
preproc.connect(anat_deoblique, 'out_file', outputnode, 'refit')
# Disable non_local_means_filtering and n4_correction when run niworkflows-ants
if method == 'niworkflows-ants':
non_local_means_filtering = False
n4_correction = False
if non_local_means_filtering and n4_correction:
denoise = pe.Node(interface=ants.DenoiseImage(), name='anat_denoise')
preproc.connect(anat_deoblique, 'out_file', denoise, 'input_image')
n4 = pe.Node(interface=ants.N4BiasFieldCorrection(dimension=3,
shrink_factor=2,
copy_header=True),
name='anat_n4')
preproc.connect(denoise, 'output_image', n4, 'input_image')
elif non_local_means_filtering and not n4_correction:
denoise = pe.Node(interface=ants.DenoiseImage(), name='anat_denoise')
preproc.connect(anat_deoblique, 'out_file', denoise, 'input_image')
elif not non_local_means_filtering and n4_correction:
n4 = pe.Node(interface=ants.N4BiasFieldCorrection(dimension=3,
shrink_factor=2,
copy_header=True),
name='anat_n4')
preproc.connect(anat_deoblique, 'out_file', n4, 'input_image')
# Anatomical reorientation
anat_reorient = pe.Node(interface=afni.Resample(), name='anat_reorient')
anat_reorient.inputs.orientation = 'RPI'
anat_reorient.inputs.outputtype = 'NIFTI_GZ'
if n4_correction:
preproc.connect(n4, 'output_image', anat_reorient, 'in_file')
elif non_local_means_filtering and not n4_correction:
preproc.connect(denoise, 'output_image', anat_reorient, 'in_file')
else:
preproc.connect(anat_deoblique, 'out_file', anat_reorient, 'in_file')
preproc.connect(anat_reorient, 'out_file', outputnode, 'reorient')
if already_skullstripped:
anat_skullstrip = pe.Node(
interface=util.IdentityInterface(fields=['out_file']),
name='anat_skullstrip')
preproc.connect(anat_reorient, 'out_file', anat_skullstrip, 'out_file')
preproc.connect(anat_skullstrip, 'out_file', outputnode, 'skullstrip')
preproc.connect(anat_skullstrip, 'out_file', outputnode, 'brain')
else:
if method == 'afni':
# Skull-stripping using AFNI 3dSkullStrip
inputnode_afni = pe.Node(util.IdentityInterface(fields=[
'shrink_factor', 'var_shrink_fac', 'shrink_fac_bot_lim',
'avoid_vent', 'niter', 'pushout', 'touchup', 'fill_hole',
'avoid_eyes', 'use_edge', 'exp_frac', 'smooth_final',
'push_to_edge', 'use_skull', 'perc_int', 'max_inter_iter',
'blur_fwhm', 'fac', 'monkey'
]),
name='AFNI_options')
skullstrip_args = pe.Node(util.Function(
input_names=[
'spat_norm', 'spat_norm_dxyz', 'shrink_fac',
'var_shrink_fac', 'shrink_fac_bot_lim', 'avoid_vent',
'niter', 'pushout', 'touchup', 'fill_hole', 'avoid_eyes',
'use_edge', 'exp_frac', 'smooth_final', 'push_to_edge',
'use_skull', 'perc_int', 'max_inter_iter', 'blur_fwhm',
'fac', 'monkey'
],
output_names=['expr'],
function=create_3dskullstrip_arg_string),
name='anat_skullstrip_args')
preproc.connect([(inputnode_afni, skullstrip_args,
[('shrink_factor', 'shrink_fac'),
('var_shrink_fac', 'var_shrink_fac'),
('shrink_fac_bot_lim', 'shrink_fac_bot_lim'),
('avoid_vent', 'avoid_vent'),
('niter', 'niter'), ('pushout', 'pushout'),
('touchup', 'touchup'),
('fill_hole', 'fill_hole'),
('avoid_eyes', 'avoid_eyes'),
('use_edge', 'use_edge'),
('exp_frac', 'exp_frac'),
('smooth_final', 'smooth_final'),
('push_to_edge', 'push_to_edge'),
('use_skull', 'use_skull'),
('perc_int', 'perc_int'),
('max_inter_iter', 'max_inter_iter'),
('blur_fwhm', 'blur_fwhm'), ('fac', 'fac'),
('monkey', 'monkey')])])
anat_skullstrip = pe.Node(interface=afni.SkullStrip(),
name='anat_skullstrip')
anat_skullstrip.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(anat_reorient, 'out_file', anat_skullstrip,
'in_file')
preproc.connect(skullstrip_args, 'expr', anat_skullstrip, 'args')
preproc.connect(anat_skullstrip, 'out_file', outputnode,
'skullstrip')
# Apply skull-stripping step mask to original volume
anat_skullstrip_orig_vol = pe.Node(interface=afni.Calc(),
name='anat_skullstrip_orig_vol')
anat_skullstrip_orig_vol.inputs.expr = 'a*step(b)'
anat_skullstrip_orig_vol.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(anat_reorient, 'out_file',
anat_skullstrip_orig_vol, 'in_file_a')
anat_brain_mask = pe.Node(interface=afni.Calc(),
name='anat_brain_mask')
anat_brain_mask.inputs.expr = 'step(a)'
anat_brain_mask.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(anat_skullstrip, 'out_file', anat_brain_mask,
'in_file_a')
preproc.connect(anat_skullstrip, 'out_file',
anat_skullstrip_orig_vol, 'in_file_b')
preproc.connect(anat_brain_mask, 'out_file', outputnode,
'brain_mask')
preproc.connect(anat_skullstrip_orig_vol, 'out_file', outputnode,
'brain')
elif method == 'fsl':
# Skull-stripping using FSL BET
inputnode_bet = pe.Node(util.IdentityInterface(fields=[
'frac', 'mask_boolean', 'mesh_boolean', 'outline', 'padding',
'radius', 'reduce_bias', 'remove_eyes', 'robust', 'skull',
'surfaces', 'threshold', 'vertical_gradient'
]),
name='BET_options')
anat_skullstrip = pe.Node(interface=fsl.BET(),
name='anat_skullstrip')
preproc.connect(anat_reorient, 'out_file', anat_skullstrip,
'in_file')
preproc.connect([(inputnode_bet, anat_skullstrip, [
('frac', 'frac'),
('mask_boolean', 'mask'),
('mesh_boolean', 'mesh'),
('outline', 'outline'),
('padding', 'padding'),
('radius', 'radius'),
('reduce_bias', 'reduce_bias'),
('remove_eyes', 'remove_eyes'),
('robust', 'robust'),
('skull', 'skull'),
('surfaces', 'surfaces'),
('threshold', 'threshold'),
('vertical_gradient', 'vertical_gradient'),
])])
preproc.connect(anat_skullstrip, 'out_file', outputnode,
'skullstrip')
# Apply skull-stripping step mask to original volume
anat_skullstrip_orig_vol = pe.Node(interface=afni.Calc(),
name='anat_skullstrip_orig_vol')
anat_skullstrip_orig_vol.inputs.expr = 'a*step(b)'
anat_skullstrip_orig_vol.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(anat_reorient, 'out_file',
anat_skullstrip_orig_vol, 'in_file_a')
preproc.connect(anat_skullstrip, 'out_file',
anat_skullstrip_orig_vol, 'in_file_b')
preproc.connect(anat_skullstrip, 'mask_file', outputnode,
'brain_mask')
preproc.connect(anat_skullstrip_orig_vol, 'out_file', outputnode,
'brain')
elif method == 'niworkflows-ants':
# Skull-stripping using niworkflows-ants
anat_skullstrip_ants = init_brain_extraction_wf(
tpl_target_path=template_path,
tpl_mask_path=mask_path,
tpl_regmask_path=regmask_path,
name='anat_skullstrip_ants')
preproc.connect(anat_reorient, 'out_file', anat_skullstrip_ants,
'inputnode.in_files')
preproc.connect(anat_skullstrip_ants, 'copy_xform.out_file',
outputnode, 'skullstrip')
preproc.connect(anat_skullstrip_ants, 'copy_xform.out_file',
outputnode, 'brain')
preproc.connect(anat_skullstrip_ants,
'atropos_wf.copy_xform.out_mask', outputnode,
'brain_mask')
elif method == 'mask':
anat_skullstrip_orig_vol = pe.Node(interface=afni.Calc(),
name='anat_skullstrip_orig_vol')
anat_skullstrip_orig_vol.inputs.expr = 'a*step(b)'
anat_skullstrip_orig_vol.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(anat_reorient, 'out_file',
anat_skullstrip_orig_vol, 'in_file_a')
preproc.connect(inputnode, 'brain_mask', anat_skullstrip_orig_vol,
'in_file_b')
preproc.connect(inputnode, 'brain_mask', outputnode, 'brain_mask')
preproc.connect(anat_skullstrip_orig_vol, 'out_file', outputnode,
'brain')
return preproc
