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 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 create_eddy_correct_pipeline(name='eddy_correct'):
"""
.. deprecated:: 0.9.3
Use :func:`nipype.workflows.dmri.preprocess.epi.ecc_pipeline` instead.
Creates a pipeline that replaces eddy_correct script in FSL. It takes a
series of diffusion weighted images and linearly co-registers them to one
reference image. No rotation of the B-matrix is performed, so this pipeline
should be executed after the motion correction pipeline.
Example
-------
>>> nipype_eddycorrect = create_eddy_correct_pipeline('nipype_eddycorrect')
>>> nipype_eddycorrect.inputs.inputnode.in_file = 'diffusion.nii'
>>> nipype_eddycorrect.inputs.inputnode.ref_num = 0
>>> nipype_eddycorrect.run() # doctest: +SKIP
Inputs::
inputnode.in_file
inputnode.ref_num
Outputs::
outputnode.eddy_corrected
"""
warnings.warn(
('This workflow is deprecated from v.1.0.0, use '
'nipype.workflows.dmri.preprocess.epi.ecc_pipeline instead'),
DeprecationWarning)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'ref_num']),
name='inputnode')
pipeline = pe.Workflow(name=name)
split = pe.Node(fsl.Split(dimension='t'), name='split')
pick_ref = pe.Node(niu.Select(), name='pick_ref')
coregistration = pe.MapNode(fsl.FLIRT(no_search=True,
padding_size=1,
interp='trilinear'),
name='coregistration',
iterfield=['in_file'])
merge = pe.Node(fsl.Merge(dimension='t'), name='merge')
outputnode = pe.Node(niu.IdentityInterface(fields=['eddy_corrected']),
name='outputnode')
pipeline.connect([(inputnode, split, [('in_file', 'in_file')]),
(split, pick_ref, [('out_files', 'inlist')]),
(inputnode, pick_ref, [('ref_num', 'index')]),
(split, coregistration, [('out_files', 'in_file')]),
(pick_ref, coregistration, [('out', 'reference')]),
(coregistration, merge, [('out_file', 'in_files')]),
(merge, outputnode, [('merged_file', 'eddy_corrected')])
])
return pipeline
def fsl_RegrSliceWise(input_file,txtregr_Path,regr_Path):
# scale Nifti data by factor 10
dataName = os.path.basename(input_file).split('.')[0]
# proof data existence
regrTextFiles = findRegData(txtregr_Path)
if len(regrTextFiles) == 0:
print('No regression with physio data!')
output_file = os.path.join(regr_Path,
os.path.basename(input_file).split('.')[0]) + '_RGR.nii.gz'
shutil.copyfile(input_file, output_file)
return output_file
fslPath = scaleBy10(input_file, inv=False)
# split input_file in slices
mySplit = fsl.Split(in_file=fslPath, dimension='z', out_base_name=dataName)
print(mySplit.cmdline)
mySplit.run()
os.remove(fslPath)
# sparate ref and src volume in slices
sliceFiles = findSlicesData(os.getcwd(), dataName)
if not len(regrTextFiles) == len(sliceFiles):
sys.exit('Error: Not enough txt.Files in %s' % txtregr_Path)
print('Start separate slice Regression ... ')
# start to regression slice by slice
print('For all Sices ...')
for i in range(len(sliceFiles)):
slc = sliceFiles[i]
regr = regrTextFiles[i]
# only take the columns [1,2,7,9,11,12,13] of the reg-.txt Files
output_file = os.path.join(regr_Path, os.path.basename(slc))
myRegr = fsl.FilterRegressor(in_file=slc,design_file=regr,out_file=output_file,filter_columns=[1,2,7,9,11,12,13])
print(myRegr.cmdline)
myRegr.run()
os.remove(slc)
# merge slices to a single volume
mcf_sliceFiles = findSlicesData(regr_Path, dataName)
output_file = os.path.join(regr_Path,
os.path.basename(input_file).split('.')[0]) + '_RGR.nii.gz'
myMerge = fsl.Merge(in_files=mcf_sliceFiles, dimension='z', merged_file=output_file)
print(myMerge.cmdline)
myMerge.run()
for slc in mcf_sliceFiles: os.remove(slc)
# unscale result data by factor 10ˆ(-1)
output_file = scaleBy10(output_file, inv=True)
return output_file
def apply_all_corrections(name='UnwarpArtifacts'):
"""
Combines two lists of linear transforms with the deformation field
map obtained typically after the SDC process.
Additionally, computes the corresponding bspline coefficients and
the map of determinants of the jacobian.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=['in_sdc',
'in_hmc', 'in_ecc', 'in_dwi']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file', 'out_warp',
'out_coeff', 'out_jacobian']), name='outputnode')
warps = pe.MapNode(fsl.ConvertWarp(relwarp=True),
iterfield=['premat', 'postmat'],
name='ConvertWarp')
selref = pe.Node(niu.Select(index=[0]), name='Reference')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
unwarp = pe.MapNode(fsl.ApplyWarp(), iterfield=['in_file', 'field_file'],
name='UnwarpDWIs')
coeffs = pe.MapNode(fsl.WarpUtils(out_format='spline'),
iterfield=['in_file'], name='CoeffComp')
jacobian = pe.MapNode(fsl.WarpUtils(write_jacobian=True),
iterfield=['in_file'], name='JacobianComp')
jacmult = pe.MapNode(fsl.MultiImageMaths(op_string='-mul %s'),
iterfield=['in_file', 'operand_files'],
name='ModulateDWIs')
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='RemoveNegative')
merge = pe.Node(fsl.Merge(dimension='t'), name='MergeDWIs')
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, warps, [('in_sdc', 'warp1'),
('in_hmc', 'premat'),
('in_ecc', 'postmat'),
('in_dwi', 'reference')]),
(inputnode, split, [('in_dwi', 'in_file')]),
(split, selref, [('out_files', 'inlist')]),
(warps, unwarp, [('out_file', 'field_file')]),
(split, unwarp, [('out_files', 'in_file')]),
(selref, unwarp, [('out', 'ref_file')]),
(selref, coeffs, [('out', 'reference')]),
(warps, coeffs, [('out_file', 'in_file')]),
(selref, jacobian, [('out', 'reference')]),
(coeffs, jacobian, [('out_file', 'in_file')]),
(unwarp, jacmult, [('out_file', 'in_file')]),
(jacobian, jacmult, [('out_jacobian', 'operand_files')]),
(jacmult, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(warps, outputnode, [('out_file', 'out_warp')]),
(coeffs, outputnode, [('out_file', 'out_coeff')]),
(jacobian, outputnode, [('out_jacobian', 'out_jacobian')]),
(merge, outputnode, [('merged_file', 'out_file')])
])
return wf
def dr_stage2(i, **kwargs): subj, mask = i regress_moco = True desnorm = True demean = True for i in kwargs.keys(): if i == 'regress_moco': if kwargs[i]: regress_moco = True else: regress_moco = False if i == 'desnorm': if kwargs[i]: desnorm = True else: desnorm = False dr_s1_txt = os.path.join(OUTPUT, 'stage1', 'dr_stage1_idc_' + subj + '.txt') moco_txt = os.path.join(os.path.dirname(INPUT), subj, feat_pfix + subj + feat_sfix, 'mc', 'prefiltered_func_data_mcf.par') dr_s1 = read_txt_file(dr_s1_txt) if regress_moco: moco_pars = read_txt_file(moco_txt) for i in range(0, len(dr_s1)): for j in range(0,6): dr_s1[i].append(moco_pars[i][j]) dr_s1_moco_txt = os.path.join(OUTPUT, 'stage1', 'dr_stage1_moco_idc_' + subj + '.txt') write_txt_file(dr_s1, dr_s1_moco_txt) designFile = dr_s1_moco_txt else: designFile = dr_s1_txt iFile = os.path.join(os.path.dirname(INPUT), subj, feat_pfix + subj + feat_sfix, ff_data_name) oFile = os.path.join(OUTPUT, 'stage2', 'dr_stage2_idc_' + subj + '.nii.gz') ozFile = os.path.join(OUTPUT, 'stage2', 'dr_stage2_Z_idc_' + subj + '.nii.gz') if os.path.exists(oFile) and os.path.exists(ozFile): return mask = os.path.join(OUTPUT, 'stage1', 'mask.nii.gz') fsl_glm = fsl.GLM(in_file=iFile, design=designFile, terminal_output='stream', out_file=oFile, out_z_name=ozFile, mask=mask, des_norm=desnorm, demean=demean, output_type='NIFTI_GZ') cmd_out = os.path.join(OUTPUT, 'stage2', 'stage2_fslglm_idc_' + subj + '.out') write_cmd_out(fsl_glm.cmdline, cmd_out) fsl_glm.run() obname = os.path.join(OUTPUT, 'stage2', 'dr_stage2_idc_' + subj + '_ic') fslsplit = fsl.Split(dimension='t', in_file=oFile, out_base_name=obname, terminal_output='stream', output_type='NIFTI_GZ') fslsplit.run() obname = os.path.join(OUTPUT, 'stage2', 'dr_stage2_idc_Z' + subj + '_ic') fslsplit = fsl.Split(dimension='t', in_file=ozFile, out_base_name=obname, terminal_output='stream', output_type='NIFTI_GZ') fslsplit.run()
def _run_interface(self, runtime):
splitter = fsl.Split(dimension='t',
out_base_name='out_',
in_file=self.inputs.in_file)
split_outputs = splitter.run().outputs.out_files
if len(split_outputs[0]) == 1:
split_outputs = [split_outputs]
self._out_file = self.extract_index(split_outputs, self.inputs.in_id)
return runtime
def create_nonbrain_meansignal(name='nonbrain_meansignal'):
nonbrain_meansignal = Workflow(name=name)
inputspec = Node(utility.IdentityInterface(fields=['func_file']),
name='inputspec')
# Split raw 4D functional image into 3D niftis
split_image = Node(fsl.Split(dimension='t', output_type='NIFTI'),
name='split_image')
# Create a brain mask for each of the 3D images
brain_mask = MapNode(fsl.BET(frac=0.3,
mask=True,
no_output=True,
robust=True),
iterfield=['in_file'],
name='brain_mask')
# Merge the 3D masks into a 4D nifti (producing a separate mask per volume)
merge_mask = Node(fsl.Merge(dimension='t'), name='merge_mask')
# Reverse the 4D brain mask, to produce a 4D non brain mask
reverse_mask = Node(fsl.ImageMaths(op_string='-sub 1 -mul -1'),
name='reverse_mask')
# Apply the mask on the raw functional data
apply_mask = Node(fsl.ImageMaths(), name='apply_mask')
# Highpass filter the non brain image
highpass = create_highpass_filter(name='highpass')
# Extract the mean signal from the non brain image
mean_signal = Node(fsl.ImageMeants(), name='mean_signal')
outputspec = Node(utility.IdentityInterface(fields=['nonbrain_regressor']),
name='outputspec')
nonbrain_meansignal.connect(inputspec, 'func_file', split_image, 'in_file')
nonbrain_meansignal.connect(split_image, 'out_files', brain_mask,
'in_file')
nonbrain_meansignal.connect(brain_mask, 'mask_file', merge_mask,
'in_files')
nonbrain_meansignal.connect(merge_mask, 'merged_file', reverse_mask,
'in_file')
nonbrain_meansignal.connect(reverse_mask, 'out_file', apply_mask,
'mask_file')
nonbrain_meansignal.connect(inputspec, 'func_file', apply_mask, 'in_file')
nonbrain_meansignal.connect(apply_mask, 'out_file', highpass,
'inputspec.in_file')
nonbrain_meansignal.connect(highpass, 'outputspec.filtered_file',
mean_signal, 'in_file')
nonbrain_meansignal.connect(mean_signal, 'out_file', outputspec,
'nonbrain_regressor')
return nonbrain_meansignal
def splitting(launcher, in_file, base_name):
split = flsi.Split()
split.inputs.in_file = in_file
split.inputs.out_base_name = base_name
split.inputs.dimension = 't'
split.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI'}
# 'mcflirt -in functional.nii -cost mutualinfo -out moco.nii'
# >>> res = mcflt.run() # doctest: +SKIP
launcher.run(split.cmdline)
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 create_realign_flow(name='realign'):
"""Realign a time series to the middle volume using spline interpolation
Uses MCFLIRT to realign the time series and ApplyWarp to apply the rigid
body transformations using spline interpolation (unknown order).
Example
-------
>>> wf = create_realign_flow()
>>> wf.inputs.inputspec.func = 'f3.nii'
>>> wf.run() # doctest: +SKIP
"""
realignflow = pe.Workflow(name=name)
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'func',
]),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['realigned_file', 'rms_files', 'par_file']),
name='outputspec')
start_dropper = pe.Node(util.Function(
input_names=['in_vol_fn', 'n_frames'],
output_names=['out_fn'],
function=remove_first_n_frames),
name='start_dropper')
start_dropper.inputs.n_frames = 5
realigner = pe.Node(fsl.MCFLIRT(save_mats=True,
stats_imgs=True,
save_rms=True,
save_plots=True),
name='realigner')
splitter = pe.Node(fsl.Split(dimension='t'), name='splitter')
warper = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file', 'premat'],
name='warper')
joiner = pe.Node(fsl.Merge(dimension='t'), name='joiner')
realignflow.connect(inputnode, 'func', start_dropper, 'in_vol_fn')
realignflow.connect(start_dropper, 'out_fn', realigner, 'in_file')
realignflow.connect(start_dropper, ('out_fn', select_volume, 'middle'),
realigner, 'ref_vol')
realignflow.connect(realigner, 'out_file', splitter, 'in_file')
realignflow.connect(realigner, 'mat_file', warper, 'premat')
realignflow.connect(realigner, 'variance_img', warper, 'ref_file')
realignflow.connect(splitter, 'out_files', warper, 'in_file')
realignflow.connect(warper, 'out_file', joiner, 'in_files')
realignflow.connect(joiner, 'merged_file', outputnode, 'realigned_file')
realignflow.connect(realigner, 'rms_files', outputnode, 'rms_files')
realignflow.connect(realigner, 'par_file', outputnode, 'par_file')
return realignflow
def modify_func_fm_run(FILEROOT):
# Input is like sub-sub_run-01_epi.nii.gz (already in fmap dir)
NII = FILEROOT + '.nii.gz'
JSONFILE = FILEROOT + '.json'
JSON_DAT = read_json(JSONFILE)
TASKNAME = JSON_DAT['SeriesDescription'].split('_')[1]
# Splits into AP/PA sets
# sub-<label>[_ses-<label>][_acq-<label>][_ce-<label>]_dir-<label>[_run-<index>]_epi.json
LROOT = FILEROOT.split('_')
APNAME = LROOT[0] + '_dir-AP_' + '_'.join(LROOT[1:])
PANAME = LROOT[0] + '_dir-PA_' + '_'.join(LROOT[1:])
# Volumes 1/3 are AP, 2/4 PA
SPLITTER = fsl.Split(in_file=NII, dimension='t', out_base_name='tmp')
SPLITTER.run()
MERGE_AP = fsl.Merge(in_files=['tmp0000.nii.gz', 'tmp0002.nii.gz'],
dimension='t',
merged_file=APNAME + '.nii.gz')
MERGE_AP.run()
MERGE_PA = fsl.Merge(in_files=['tmp0001.nii.gz', 'tmp0003.nii.gz'],
dimension='t',
merged_file=PANAME + '.nii.gz')
MERGE_PA.run()
os.remove(NII)
os.remove(JSONFILE)
for FILE in os.listdir('.'):
if FILE.startswith('tmp0'):
os.remove(FILE)
# TODO: should we flip 1st volume and reorient?
# Find which func data to use it for (right now assumes series description matches, may need to be more open).
# Kludge: if stub of IntendedFor is there, populate with all runs
os.chdir('..')
INTENDEDFOR = []
if 'IntendedFor' in JSON_DAT:
STUB_TASKS = JSON_DAT['IntendedFor']
for STUB in STUB_TASKS:
INTEND_TASK = glob.glob('func/*task-{}_*bold.nii.gz'.format(STUB))
INTENDEDFOR += INTEND_TASK
else:
INTENDEDFOR = glob.glob('func/*task-{}_*bold.nii.gz'.format(TASKNAME))
JSON_DAT['IntendedFor'] = sorted(INTENDEDFOR)
os.chdir('fmap')
write_json(APNAME + '.json', JSON_DAT)
write_json(PANAME + '.json', JSON_DAT)
def create_eddy_correct_pipeline(name="eddy_correct"):
"""Creates a pipeline that replaces eddy_correct script in FSL. It takes a
series of diffusion weighted images and linearly corregisters them to one
reference image.
Example
-------
>>> nipype_eddycorrect = create_eddy_correct_pipeline("nipype_eddycorrect")
>>> nipype_eddycorrect.inputs.inputnode.in_file = 'diffusion.nii'
>>> nipype_eddycorrect.inputs.inputnode.ref_num = 0
>>> nipype_eddycorrect.run() # doctest: +SKIP
Inputs::
inputnode.in_file
inputnode.ref_num
Outputs::
outputnode.eddy_corrected
"""
inputnode = pe.Node(
interface=util.IdentityInterface(fields=["in_file", "ref_num"]),
name="inputnode")
pipeline = pe.Workflow(name=name)
split = pe.Node(fsl.Split(dimension='t'), name="split")
pipeline.connect([(inputnode, split, [("in_file", "in_file")])])
pick_ref = pe.Node(util.Select(), name="pick_ref")
pipeline.connect([(split, pick_ref, [("out_files", "inlist")]),
(inputnode, pick_ref, [("ref_num", "index")])])
coregistration = pe.MapNode(fsl.FLIRT(no_search=True, padding_size=1),
name="coregistration",
iterfield=["in_file"])
pipeline.connect([(split, coregistration, [("out_files", "in_file")]),
(pick_ref, coregistration, [("out", "reference")])])
merge = pe.Node(fsl.Merge(dimension="t"), name="merge")
pipeline.connect([(coregistration, merge, [("out_file", "in_files")])])
outputnode = pe.Node(
interface=util.IdentityInterface(fields=["eddy_corrected"]),
name="outputnode")
pipeline.connect([(merge, outputnode, [("merged_file", "eddy_corrected")])
])
return pipeline
def test_spm(name='test_spm_3d'):
"""
A simple workflow to test SPM's installation. By default will split the 4D volume in
time-steps.
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_data']),
name='inputnode')
dgr = pe.Node(nio.DataGrabber(template="feeds/data/fmri.nii.gz",
outfields=['out_file'],
sort_filelist=False),
name='datasource')
stc = pe.Node(spm.SliceTiming(num_slices=21,
time_repetition=1.0,
time_acquisition=2. - 2. / 32,
slice_order=list(range(21, 0, -1)),
ref_slice=10),
name='stc')
realign_estimate = pe.Node(spm.Realign(jobtype='estimate'),
name='realign_estimate')
realign_write = pe.Node(spm.Realign(jobtype='write'), name='realign_write')
realign_estwrite = pe.Node(spm.Realign(jobtype='estwrite'),
name='realign_estwrite')
smooth = pe.Node(spm.Smooth(fwhm=[6, 6, 6]), name='smooth')
if name == 'test_spm_3d':
split = pe.Node(fsl.Split(dimension="t", output_type="NIFTI"),
name="split")
workflow.connect([(dgr, split, [(('out_file', _get_first), 'in_file')
]),
(split, stc, [("out_files", "in_files")])])
elif name == 'test_spm_4d':
gunzip = pe.Node(Gunzip(), name="gunzip")
workflow.connect([(dgr, gunzip, [(('out_file', _get_first), 'in_file')
]),
(gunzip, stc, [("out_file", "in_files")])])
else:
raise NotImplementedError(
'No implementation of the test workflow \'{}\' was found'.format(
name))
workflow.connect([
(inputnode, dgr, [('in_data', 'base_directory')]),
(stc, realign_estimate, [('timecorrected_files', 'in_files')]),
(realign_estimate, realign_write, [('modified_in_files', 'in_files')]),
(stc, realign_estwrite, [('timecorrected_files', 'in_files')]),
(realign_write, smooth, [('realigned_files', 'in_files')])
])
return workflow
def _run_interface(self, runtime):
from dipy.core import gradients
splitter = fsl.Split(dimension='t',
out_base_name='dwi_',
in_file=self.inputs.in_file)
gtab = gradients.gradient_table(
bvals=self.inputs.in_bval,
bvecs=self.inputs.in_bvec,
b0_threshold=self.inputs.in_bval_threshold)
masklist = list(gtab.b0s_mask)
split_outputs = splitter.run().outputs.out_files
self._b0s, self._dwis, self._all, self._indices = self.extract_sublists(
split_outputs, masklist)
return runtime
def fsl_SeparateSliceMoCo(input_file, par_folder):
# scale Nifti data by factor 10
dataName = os.path.basename(input_file).split('.')[0]
fslPath = scaleBy10(input_file, inv=False)
mySplit = fsl.Split(in_file=fslPath, dimension='z', out_base_name=dataName)
print(mySplit.cmdline)
mySplit.run()
os.remove(fslPath)
# sparate ref and src volume in slices
sliceFiles = findSlicesData(os.getcwd(), dataName)
# refFiles = findSlicesData(os.getcwd(),'ref')
print('For all slices ... ')
# start to correct motions slice by slice
for i in range(len(sliceFiles)):
slc = sliceFiles[i]
# ref = refFiles[i]
# take epi as ref
output_file = os.path.join(par_folder, os.path.basename(slc))
myMCFLIRT = fsl.preprocess.MCFLIRT(in_file=slc,
out_file=output_file,
save_plots=True,
terminal_output='none')
print(myMCFLIRT.cmdline)
myMCFLIRT.run()
os.remove(slc)
# os.remove(ref)
# merge slices to a single volume
mcf_sliceFiles = findSlicesData(par_folder, dataName)
output_file = os.path.join(
os.path.dirname(input_file),
os.path.basename(input_file).split('.')[0]) + '_mcf.nii.gz'
myMerge = fsl.Merge(in_files=mcf_sliceFiles,
dimension='z',
merged_file=output_file)
print(myMerge.cmdline)
myMerge.run()
for slc in mcf_sliceFiles:
os.remove(slc)
# unscale result data by factor 10ˆ(-1)
output_file = scaleBy10(output_file, inv=True)
return output_file
def _run_interface(self, runtime):
split = fsl.Split(dimension='t', in_file=self.inputs.dwi_file)
split_dwi_files = split.run().outputs.out_files
split_bval_files, split_bvec_files = split_bvals_bvecs(
self.inputs.bval_file, self.inputs.bvec_file, runtime)
bvalues = np.loadtxt(self.inputs.bval_file)
b0_indices = np.flatnonzero(bvalues < self.inputs.b0_threshold)
b0_paths = [split_dwi_files[idx] for idx in b0_indices]
self._results['dwi_files'] = split_dwi_files
self._results['bval_files'] = split_bval_files
self._results['bvec_files'] = split_bvec_files
self._results['b0_images'] = b0_paths
self._results['b0_indices'] = b0_indices.tolist()
return runtime
def create_transform_pipeline(name='transfrom_timeseries'):
# set fsl output type
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# initiate workflow
transform_ts = Workflow(name='transform_timeseries')
# inputnode
inputnode=Node(util.IdentityInterface(fields=['orig_ts',
'anat_head',
'mat_moco',
'fullwarp',
'resolution',
'brain_mask'
]),
name='inputnode')
# outputnode
outputnode=Node(util.IdentityInterface(fields=['trans_ts',
'trans_ts_mean',
'trans_ts_masked',
'resamp_brain',
'brain_mask_resamp',
'out_dvars'
]),
name='outputnode')
#resample anatomy
resample = Node(fsl.FLIRT(datatype='float',
out_file='T1_resampled.nii.gz'),
name = 'resample_anat')
transform_ts.connect([(inputnode, resample, [('anat_head', 'in_file'),
('anat_head', 'reference'),
('resolution', 'apply_isoxfm')
]),
(resample, outputnode, [('out_file', 'resamp_brain')])
])
# split timeseries in single volumes
split=Node(fsl.Split(dimension='t',
out_base_name='timeseries'),
name='split')
transform_ts.connect([(inputnode, split, [('orig_ts','in_file')])])
# applymoco premat and fullwarpfield
applywarp = MapNode(fsl.ApplyWarp(interp='spline',
relwarp=True,
out_file='rest2anat.nii.gz',
datatype='float'),
iterfield=['in_file', 'premat'],
name='applywarp')
transform_ts.connect([(split, applywarp, [('out_files', 'in_file')]),
(inputnode, applywarp,
[('mat_moco', 'premat'),
('fullwarp','field_file')]),
(resample, applywarp, [('out_file', 'ref_file')])
])
# re-concatenate volumes
merge=Node(fsl.Merge(dimension='t',
merged_file='rest2anat.nii.gz'),
name='merge')
transform_ts.connect([(applywarp,merge,[('out_file','in_files')]),
(merge, outputnode, [('merged_file', 'trans_ts')])])
# calculate new mean
tmean = Node(fsl.maths.MeanImage(dimension='T',
out_file='rest_mean2anat_lowres.nii.gz'),
name='tmean')
transform_ts.connect([(merge, tmean, [('merged_file', 'in_file')]),
(tmean, outputnode, [('out_file', 'trans_ts_mean')])
])
# resample brain mask
resample_brain = Node(afni.Resample(resample_mode='NN',
outputtype='NIFTI_GZ',
out_file='T1_brain_mask_lowres.nii.gz'),
name = 'resample_brain')
transform_ts.connect([(inputnode, resample_brain, [('brain_mask', 'in_file')]),
(tmean, resample_brain, [('out_file', 'master')]),
(resample_brain, outputnode, [('out_file', 'brain_mask_resamp')])
])
#mask the transformed file
mask = Node(fsl.ApplyMask(), name="mask")
transform_ts.connect([(resample_brain,mask, [('out_file', 'mask_file')]),
(merge, mask, [('merged_file', 'in_file')]),
(mask, outputnode, [('out_file', 'trans_ts_masked')])
])
#calculate DVARS
dvars = Node(confounds.ComputeDVARS(save_all=True, save_plot=True), name="dvars")
transform_ts.connect([(resample_brain, dvars, [('out_file', 'in_mask')]),
(merge, dvars, [('merged_file', 'in_file')]),
(dvars, outputnode, [('out_all', 'out_dvars')])
])
return transform_ts
def build_core_nodes(self):
"""Build and connect the core nodes of the pipeline.
Notes:
- If `FSLOUTPUTTYPE` environment variable is not set, `nipype` takes
NIFTI by default.
Todo:
- [x] Detect space automatically.
- [ ] Allow for custom parcellations (See TODOs in utils).
"""
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as npe
import nipype.interfaces.fsl as fsl
import nipype.interfaces.freesurfer as fs
import nipype.interfaces.mrtrix3 as mrtrix3
from clinica.lib.nipype.interfaces.mrtrix.preprocess import MRTransform
from clinica.lib.nipype.interfaces.mrtrix3.reconst import EstimateFOD
from clinica.lib.nipype.interfaces.mrtrix3.tracking import Tractography
from clinica.utils.exceptions import ClinicaException, ClinicaCAPSError
from clinica.utils.stream import cprint
import clinica.pipelines.dwi_connectome.dwi_connectome_utils as utils
from clinica.utils.mri_registration import convert_flirt_transformation_to_mrtrix_transformation
# cprint('Building the pipeline...')
# Nodes
# =====
# B0 Extraction (only if space=b0)
# -------------
split_node = npe.Node(name="Reg-0-DWI-B0Extraction",
interface=fsl.Split())
split_node.inputs.output_type = "NIFTI_GZ"
split_node.inputs.dimension = 't'
select_node = npe.Node(name="Reg-0-DWI-B0Selection",
interface=niu.Select())
select_node.inputs.index = 0
# B0 Brain Extraction (only if space=b0)
# -------------------
mask_node = npe.Node(name="Reg-0-DWI-BrainMasking",
interface=fsl.ApplyMask())
mask_node.inputs.output_type = "NIFTI_GZ"
# T1-to-B0 Registration (only if space=b0)
# ---------------------
t12b0_reg_node = npe.Node(name="Reg-1-T12B0Registration",
interface=fsl.FLIRT(
dof=6,
interp='spline',
cost='normmi',
cost_func='normmi',
))
t12b0_reg_node.inputs.output_type = "NIFTI_GZ"
# MGZ File Conversion (only if space=b0)
# -------------------
t1_brain_conv_node = npe.Node(name="Reg-0-T1-T1BrainConvertion",
interface=fs.MRIConvert())
wm_mask_conv_node = npe.Node(name="Reg-0-T1-WMMaskConvertion",
interface=fs.MRIConvert())
# WM Transformation (only if space=b0)
# -----------------
wm_transform_node = npe.Node(name="Reg-2-WMTransformation",
interface=fsl.ApplyXFM())
wm_transform_node.inputs.apply_xfm = True
# Nodes Generation
# ----------------
label_convert_node = npe.MapNode(
name="0-LabelsConversion",
iterfield=['in_file', 'in_config', 'in_lut', 'out_file'],
interface=mrtrix3.LabelConvert())
label_convert_node.inputs.in_config = utils.get_conversion_luts()
label_convert_node.inputs.in_lut = utils.get_luts()
# FSL flirt matrix to MRtrix matrix Conversion (only if space=b0)
# --------------------------------------------
fsl2mrtrix_conv_node = npe.Node(
name='Reg-2-FSL2MrtrixConversion',
interface=niu.Function(
input_names=[
'in_source_image', 'in_reference_image', 'in_flirt_matrix',
'name_output_matrix'
],
output_names=['out_mrtrix_matrix'],
function=convert_flirt_transformation_to_mrtrix_transformation)
)
# Parc. Transformation (only if space=b0)
# --------------------
parc_transform_node = npe.MapNode(
name="Reg-2-ParcTransformation",
iterfield=["in_files", "out_filename"],
interface=MRTransform())
# Response Estimation
# -------------------
resp_estim_node = npe.Node(name="1a-ResponseEstimation",
interface=mrtrix3.ResponseSD())
resp_estim_node.inputs.algorithm = 'tournier'
# FOD Estimation
# --------------
fod_estim_node = npe.Node(name="1b-FODEstimation",
interface=EstimateFOD())
fod_estim_node.inputs.algorithm = 'csd'
# Tracts Generation
# -----------------
tck_gen_node = npe.Node(name="2-TractsGeneration",
interface=Tractography())
tck_gen_node.inputs.n_tracks = self.parameters['n_tracks']
tck_gen_node.inputs.algorithm = 'iFOD2'
# BUG: Info package does not exist
# from nipype.interfaces.mrtrix3.base import Info
# from distutils.version import LooseVersion
#
# if Info.looseversion() >= LooseVersion("3.0"):
# tck_gen_node.inputs.select = self.parameters['n_tracks']
# elif Info.looseversion() <= LooseVersion("0.4"):
# tck_gen_node.inputs.n_tracks = self.parameters['n_tracks']
# else:
# from clinica.utils.exceptions import ClinicaException
# raise ClinicaException("Your MRtrix version is not supported.")
# Connectome Generation
# ---------------------
# only the parcellation and output filename should be iterable, the tck
# file stays the same.
conn_gen_node = npe.MapNode(name="3-ConnectomeGeneration",
iterfield=['in_parc', 'out_file'],
interface=mrtrix3.BuildConnectome())
# Print begin message
# -------------------
print_begin_message = npe.MapNode(interface=niu.Function(
input_names=['in_bids_or_caps_file'],
function=utils.print_begin_pipeline),
iterfield='in_bids_or_caps_file',
name='WriteBeginMessage')
# Print end message
# -----------------
print_end_message = npe.MapNode(interface=niu.Function(
input_names=['in_bids_or_caps_file', 'final_file'],
function=utils.print_end_pipeline),
iterfield=['in_bids_or_caps_file'],
name='WriteEndMessage')
# CAPS File names Generation
# --------------------------
caps_filenames_node = npe.Node(
name='CAPSFilenamesGeneration',
interface=niu.Function(input_names='dwi_file',
output_names=self.get_output_fields(),
function=utils.get_caps_filenames))
# Connections
# ===========
# Computation of the diffusion model, tractography & connectome
# -------------------------------------------------------------
self.connect([
(self.input_node, print_begin_message,
[('dwi_file', 'in_bids_or_caps_file')]), # noqa
(self.input_node, caps_filenames_node, [('dwi_file', 'dwi_file')]),
# Response Estimation
(self.input_node, resp_estim_node, [('dwi_file', 'in_file')]
), # Preproc. DWI # noqa
(self.input_node, resp_estim_node,
[('dwi_brainmask_file', 'in_mask')]), # B0 brain mask # noqa
(self.input_node, resp_estim_node, [('grad_fsl', 'grad_fsl')
]), # bvecs and bvals # noqa
(caps_filenames_node, resp_estim_node,
[('response', 'wm_file')]), # output response filename # noqa
# FOD Estimation
(self.input_node, fod_estim_node, [('dwi_file', 'in_file')]
), # Preproc. DWI # noqa
(resp_estim_node, fod_estim_node,
[('wm_file', 'wm_txt')]), # Response (txt file) # noqa
(self.input_node, fod_estim_node,
[('dwi_brainmask_file', 'mask_file')]), # B0 brain mask # noqa
(self.input_node, fod_estim_node,
[('grad_fsl', 'grad_fsl')]), # T1-to-B0 matrix file # noqa
(caps_filenames_node, fod_estim_node,
[('fod', 'wm_odf')]), # output odf filename # noqa
# Tracts Generation
(fod_estim_node, tck_gen_node, [('wm_odf', 'in_file')]
), # ODF file # noqa
(caps_filenames_node, tck_gen_node,
[('tracts', 'out_file')]), # output tck filename # noqa
# Label Conversion
(self.input_node, label_convert_node, [('atlas_files', 'in_file')]
), # atlas image files # noqa
(caps_filenames_node, label_convert_node, [
('nodes', 'out_file')
]), # converted atlas image filenames # noqa
# Connectomes Generation
(tck_gen_node, conn_gen_node, [('out_file', 'in_file')]), # noqa
(caps_filenames_node, conn_gen_node, [('connectomes', 'out_file')
]), # noqa
])
# Registration T1-DWI (only if space=b0)
# -------------------
if self.parameters['dwi_space'] == 'b0':
self.connect([
# MGZ Files Conversion
(self.input_node, t1_brain_conv_node, [('t1_brain_file',
'in_file')]), # noqa
(self.input_node, wm_mask_conv_node, [('wm_mask_file',
'in_file')]), # noqa
# B0 Extraction
(self.input_node, split_node, [('dwi_file', 'in_file')]
), # noqa
(split_node, select_node, [('out_files', 'inlist')]), # noqa
# Masking
(select_node, mask_node, [('out', 'in_file')]), # B0 # noqa
(self.input_node, mask_node,
[('dwi_brainmask_file', 'mask_file')]), # Brain mask # noqa
# T1-to-B0 Registration
(t1_brain_conv_node, t12b0_reg_node, [('out_file', 'in_file')]
), # Brain # noqa
(mask_node, t12b0_reg_node, [('out_file', 'reference')
]), # B0 brain-masked # noqa
# WM Transformation
(wm_mask_conv_node, wm_transform_node,
[('out_file', 'in_file')]), # Brain mask # noqa
(mask_node, wm_transform_node, [('out_file', 'reference')
]), # BO brain-masked # noqa
(t12b0_reg_node, wm_transform_node, [
('out_matrix_file', 'in_matrix_file')
]), # T1-to-B0 matrix file # noqa
# FSL flirt matrix to MRtrix matrix Conversion
(t1_brain_conv_node, fsl2mrtrix_conv_node,
[('out_file', 'in_source_image')]), # noqa
(mask_node, fsl2mrtrix_conv_node,
[('out_file', 'in_reference_image')]), # noqa
(t12b0_reg_node, fsl2mrtrix_conv_node,
[('out_matrix_file', 'in_flirt_matrix')]), # noqa
# Apply registration without resampling on parcellations
(label_convert_node, parc_transform_node,
[('out_file', 'in_files')]), # noqa
(fsl2mrtrix_conv_node, parc_transform_node,
[('out_mrtrix_matrix', 'linear_transform')]), # noqa
(caps_filenames_node, parc_transform_node,
[('nodes', 'out_filename')]), # noqa
])
# Special care for Parcellation & WM mask
# ---------------------------------------
if self.parameters['dwi_space'] == 'b0':
self.connect([
(wm_transform_node, tck_gen_node, [('out_file', 'seed_image')
]), # noqa
(parc_transform_node, conn_gen_node, [('out_file', 'in_parc')
]), # noqa
(parc_transform_node, self.output_node, [('out_file', 'nodes')
]), # noqa
])
elif self.parameters['dwi_space'] == 'T1w':
self.connect([
(self.input_node, tck_gen_node, [('wm_mask_file', 'seed_image')
]), # noqa
(label_convert_node, conn_gen_node, [('out_file', 'in_parc')
]), # noqa
(label_convert_node, self.output_node, [('out_file', 'nodes')
]), # noqa
])
else:
raise ClinicaCAPSError(
'Bad preprocessed DWI space. Please check your CAPS '
'folder.')
# Outputs
# -------
self.connect([
(resp_estim_node, self.output_node, [('wm_file', 'response')]),
(fod_estim_node, self.output_node, [('wm_odf', 'fod')]),
(tck_gen_node, self.output_node, [('out_file', 'tracts')]),
(conn_gen_node, self.output_node, [('out_file', 'connectomes')]),
(self.input_node, print_end_message, [('dwi_file',
'in_bids_or_caps_file')]),
(conn_gen_node, print_end_message, [('out_file', 'final_file')]),
])
def _b1resize(
datapath,
b1name,
regdir,
b1FAmap=None,
outfolder=Path.cwd(),
inrefname=None,
outrefname="Ref_CESTres.nii.gz",
phantom=False,
):
"""B1_RESIZE - Preprocesses B1 Data for Use with FABBER
Takes the raw B1 data and processes it into a B1 map. Also flirts it
into the reference volume space. Does not apply any actual registration,
just resamples it so it is in the same resolution as the Reference data.
Parameters:
-----------
datapath : pathlib Path object
The path to the datafolder
b1name : str
The name of the B1+ scan to register.
regdir : pathlib Path object
The registration directory being used for all
of the registration computations.
b1FAmap : str
The name of the B1+ FA map to register.
If None, will assume this is in the b1name file.
outfolder : pathlib Path object
The output directory of the data being analyzed.
If blank, the datapath directory will be used.
refname : str
The name of the reference volume to register b1 to.
If blank, the file Ref_CESTres.nii.gz will be used.
Returns:
--------
None
Author: asmith
Version: 1.0
Changelog:
20181217 - initial creation
"""
b1dir = sorted(datapath.glob("*{0}*.nii.gz".format(b1name)))
if len(b1dir) > 1 and b1FAmap is None:
fslmerge = fsl.Merge()
fslmerge.inputs.in_files = [str(i) for v, i in enumerate(b1dir)]
fslmerge.inputs.dimension = "t"
fslmerge.inputs.merged_file = str(regdir / f"{b1name}_merged.nii.gz")
fslmerge.run()
b1dir = regdir / f"{b1name}_merged.nii.gz"
try:
b1vol = nib.load(str(b1dir[1]))
except TypeError:
b1vol = nib.load(str(b1dir))
except IndexError:
b1vol = nib.load(str(b1dir[0]))
except:
print(f"Unexpected Error: {sys.exc_info()[0]}")
if b1FAmap is None and b1vol.ndim < 4:
if len(b1dir) > 1:
b1FAmap = Path(b1dir[-1].stem).stem
else:
raise NoFAMapError(
"No FA Map specified for B1 Data!\nProvide a FA map to proceed!"
)
if b1FAmap is None and b1vol.shape[3] > 2:
# Split Data
fsplt = fsl.Split()
fsplt.inputs.in_file = str(b1dir)
fsplt.inputs.out_base_name = str(regdir / "DREAM_s")
fsplt.inputs.dimension = "t"
fsplt.run()
# set variables so anatomical and FA map are defined
b1dirs = sorted(regdir.glob("*DREAM_s*.nii.gz"))
b1dir = str(b1dirs[1])
b1FAmapdir = str(b1dirs[-1])
elif b1FAmap is None and b1vol.shape[3] == 2:
# Split Data
fsplt = fsl.Split()
fsplt.inputs.in_file = str(b1dir)
fsplt.inputs.out_base_name = str(regdir / "DREAM_s")
fsplt.inputs.dimension = "t"
fsplt.run()
# Split b1vol into component anatomicals for registration
b1dirs = sorted(regdir.glob("*DREAM_s*.nii.gz"))
b1dir = str(b1dirs[1])
# Define b1FAmap for use in downstream registration
b1FAmapdir = regdir / "B1map.nii.gz"
# Build FA map from anatomical maps
fmaths = fsl.ImageMaths()
fmaths.inputs.in_file = str(b1dirs[0])
fmaths.inputs.op_string = "-mul 2 -div"
fmaths.inputs.in_file2 = str(b1dirs[1])
fmaths.inputs.out_file = str(regdir / "tmp1.nii.gz")
fmaths.run()
fmaths = fsl.ImageMaths()
fmaths.inputs.in_file = str(regdir / "tmp1.nii.gz")
fmaths.inputs.op_string = f"-sqrt -atan -div {radians(60)} -mul 600"
fmaths.inputs.out_file = str(b1FAmapdir)
fmaths.run()
else:
b1FAmapdir = sorted(datapath.glob(f"*{b1FAmap}*.nii.gz"))[0]
try:
b1dir = str(b1dir[1])
except IndexError:
b1dir = str(b1dir[0])
b1splt = fsl.ExtractROI()
b1splt.inputs.in_file = b1dir
b1splt.inputs.roi_file = str(regdir / "B1_1.nii.gz")
b1splt.inputs.t_size = 1
b1splt.inputs.t_min = 0
b1splt.run()
# Run FAST on B1 input data to get better registration
b1FAST = fsl.FAST()
b1FAST.inputs.in_files = str(regdir / "B1_1.nii.gz")
b1FAST.inputs.out_basename = str(regdir / "B1_bc")
b1FAST.inputs.output_biascorrected = True
b1FAST.inputs.no_pve = True
b1FAST.inputs.output_type = "NIFTI_GZ"
b1FAST.run(ignore_exception=True)
if phantom:
# _FOVDiff(str(regdir / "B1_bc_restore.nii.gz"), str(inrefdir), "B1resred.txt", regdir=regdir)
# Flirt B1 Image Data to Original Reference volume
flt = fsl.FLIRT()
flt.inputs.in_file = str(regdir / "B1_bc_restore.nii.gz")
flt.inputs.reference = str(regdir / "Ref_bc_restore.nii.gz")
flt.inputs.out_file = str(regdir / "B1_to_ref.nii.gz")
flt.inputs.output_type = "NIFTI_GZ"
flt.inputs.rigid2D = True
# flt.inputs.in_matrix_file = str(regdir / "B1resred.txt")
# flt.inputs.apply_xfm=True
flt.inputs.out_matrix_file = str(regdir / "B1resred.txt")
flt.run()
else:
# Flirt B1 Image Data to Original Reference volume
flt = fsl.FLIRT()
flt.inputs.in_file = str(regdir / "B1_bc_restore.nii.gz")
flt.inputs.reference = str(regdir / "Ref_bc_restore.nii.gz")
flt.inputs.out_file = str(regdir / "B1_to_ref.nii.gz")
flt.inputs.output_type = "NIFTI_GZ"
flt.inputs.out_matrix_file = str(regdir / "B1resred.txt")
flt.run()
if len(_slicenumber2d) > 0:
# Flirt B1 Map Data to Original Reference volume
flt = fsl.FLIRT()
flt.inputs.in_file = str(b1FAmapdir)
flt.inputs.reference = str(regdir / "Ref_bc_restore.nii.gz")
flt.inputs.out_file = str(regdir / "B1map_to_ref.nii.gz")
flt.inputs.output_type = "NIFTI_GZ"
flt.inputs.in_matrix_file = str(regdir / "B1resred.txt")
flt.inputs.apply_xfm = True
flt.inputs.out_matrix_file = str(regdir / "B1resred.txt")
flt.run()
if _slicenumber2d[1] > 1:
# Run for B1 Map
fsl.ExtractROI(
in_file=str(regdir / "B1map_to_ref.nii.gz"),
roi_file=str(regdir / "B1map_sROI.nii.gz"),
x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=_slicenumber2d[0] - 1,
z_size=_slicenumber2d[1],
).run()
else:
# Run for B1 Map
fsl.ExtractROI(
in_file=str(regdir / "B1map_to_ref.nii.gz"),
roi_file=str(regdir / "B1map_sROI.nii.gz"),
x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=_slicenumber2d[0],
z_size=_slicenumber2d[1],
).run()
# Warp 2D B1map to CEST Space
flt.inputs.in_file = str(regdir / "B1map_sROI.nii.gz")
flt.inputs.out_file = str(regdir / "B1map_resred.nii.gz")
flt.inputs.reference = str(regdir / outrefname)
flt.inputs.in_matrix_file = str(regdir / "Ref_CESTres.txt")
flt.inputs.apply_xfm = True
flt.inputs.rigid2D = True
flt.inputs.out_matrix_file = str(regdir / "B1toCEST.txt")
flt.run()
else:
# Combine B1->Ref matrix with Ref->CEST matrix
xfmcomb = fsl.ConvertXFM()
xfmcomb.inputs.in_file = str(regdir / "B1resred.txt")
xfmcomb.inputs.in_file2 = str(regdir / "Ref_CESTres.txt")
xfmcomb.inputs.concat_xfm = True
xfmcomb.inputs.out_file = str(regdir / "B1toCEST.txt")
xfmcomb.run()
# Use combine Ref->CEST matrix to register B1 FA map to CEST data
flt.inputs.in_file = str(b1FAmapdir)
flt.inputs.out_file = str(regdir / "B1map_resred.nii.gz")
flt.inputs.reference = str(regdir / outrefname)
flt.inputs.in_matrix_file = str(regdir / "B1toCEST.txt")
flt.inputs.apply_xfm = True
flt.inputs.out_matrix_file = str(regdir / "B1toCEST.txt")
flt.run()
# Convert Registered FA Map to Fraction of nominal angle and move to analysis folder
fmaths = fsl.ImageMaths()
fmaths.inputs.in_file = str(regdir / "B1map_resred.nii.gz")
fmaths.inputs.op_string = "-div 600 -mul"
fmaths.inputs.in_file2 = str(regdir / "Ref_Mask.nii.gz")
fmaths.inputs.out_file = str(outfolder / "B1map_resize.nii.gz")
fmaths.run()
return None
def ecc_pipeline(name='eddy_correct'):
"""
ECC stands for Eddy currents correction.
Creates a pipeline that corrects for artifacts induced by Eddy currents in
dMRI sequences.
It takes a series of diffusion weighted images and linearly co-registers
them to one reference image (the average of all b0s in the dataset).
DWIs are also modulated by the determinant of the Jacobian as indicated by
[Jones10]_ and [Rohde04]_.
A list of rigid transformation matrices can be provided, sourcing from a
:func:`.hmc_pipeline` workflow, to initialize registrations in a *motion
free* framework.
A list of affine transformation matrices is available as output, so that
transforms can be chained (discussion
`here <https://github.com/nipy/nipype/pull/530#issuecomment-14505042>`_).
.. admonition:: References
.. [Jones10] Jones DK, `The signal intensity must be modulated by the
determinant of the Jacobian when correcting for eddy currents in
diffusion MRI
<http://cds.ismrm.org/protected/10MProceedings/files/1644_129.pdf>`_,
Proc. ISMRM 18th Annual Meeting, (2010).
.. [Rohde04] Rohde et al., `Comprehensive Approach for Correction of
Motion and Distortion in Diffusion-Weighted MRI
<http://stbb.nichd.nih.gov/pdf/com_app_cor_mri04.pdf>`_, MRM
51:103-114 (2004).
Example
-------
>>> from nipype.workflows.dmri.fsl.artifacts import ecc_pipeline
>>> ecc = ecc_pipeline()
>>> ecc.inputs.inputnode.in_file = 'diffusion.nii'
>>> ecc.inputs.inputnode.in_bval = 'diffusion.bval'
>>> ecc.inputs.inputnode.in_mask = 'mask.nii'
>>> ecc.run() # doctest: +SKIP
Inputs::
inputnode.in_file - input dwi file
inputnode.in_mask - weights mask of reference image (a file with data \
range sin [0.0, 1.0], indicating the weight of each voxel when computing the \
metric.
inputnode.in_bval - b-values table
inputnode.in_xfms - list of matrices to initialize registration (from \
head-motion correction)
Outputs::
outputnode.out_file - corrected dwi file
outputnode.out_xfms - list of transformation matrices
"""
from nipype.workflows.data import get_flirt_schedule
params = dict(dof=12,
no_search=True,
interp='spline',
bgvalue=0,
schedule=get_flirt_schedule('ecc'))
# cost='normmi', cost_func='normmi', bins=64,
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_file', 'in_bval', 'in_mask', 'in_xfms']),
name='inputnode')
avg_b0 = pe.Node(niu.Function(input_names=['in_dwi', 'in_bval'],
output_names=['out_file'],
function=b0_average),
name='b0_avg')
pick_dws = pe.Node(niu.Function(input_names=['in_dwi', 'in_bval', 'b'],
output_names=['out_file'],
function=extract_bval),
name='ExtractDWI')
pick_dws.inputs.b = 'diff'
flirt = dwi_flirt(flirt_param=params, excl_nodiff=True)
mult = pe.MapNode(fsl.BinaryMaths(operation='mul'),
name='ModulateDWIs',
iterfield=['in_file', 'operand_value'])
thres = pe.MapNode(fsl.Threshold(thresh=0.0),
iterfield=['in_file'],
name='RemoveNegative')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
get_mat = pe.Node(niu.Function(input_names=['in_bval', 'in_xfms'],
output_names=['out_files'],
function=recompose_xfm),
name='GatherMatrices')
merge = pe.Node(niu.Function(
input_names=['in_dwi', 'in_bval', 'in_corrected'],
output_names=['out_file'],
function=recompose_dwi),
name='MergeDWIs')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'out_xfms']),
name='outputnode')
wf = pe.Workflow(name=name)
wf.connect([(inputnode, avg_b0, [('in_file', 'in_dwi'),
('in_bval', 'in_bval')]),
(inputnode, pick_dws, [('in_file', 'in_dwi'),
('in_bval', 'in_bval')]),
(inputnode, merge, [('in_file', 'in_dwi'),
('in_bval', 'in_bval')]),
(inputnode, flirt, [('in_mask', 'inputnode.ref_mask'),
('in_xfms', 'inputnode.in_xfms'),
('in_bval', 'inputnode.in_bval')]),
(inputnode, get_mat, [('in_bval', 'in_bval')]),
(avg_b0, flirt, [('out_file', 'inputnode.reference')]),
(pick_dws, flirt, [('out_file', 'inputnode.in_file')]),
(flirt, get_mat, [('outputnode.out_xfms', 'in_xfms')]),
(flirt, mult, [(('outputnode.out_xfms', _xfm_jacobian),
'operand_value')]),
(flirt, split, [('outputnode.out_file', 'in_file')]),
(split, mult, [('out_files', 'in_file')]),
(mult, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_corrected')]),
(get_mat, outputnode, [('out_files', 'out_xfms')]),
(merge, outputnode, [('out_file', 'out_file')])])
return wf
def b0_flirt_pipeline(num_b0s, name="b0_coregistration"):
"""
Rigid registration of the B0 dataset onto the first volume. Rigid
registration is achieved using FLIRT and the normalized
correlation.
Args:
num_b0s (int): Number of the B0 volumes in the dataset.
name (str): Name of the workflow.
Inputnode:
in_file(str): B0 dataset.
Outputnode
out_b0_reg(str): The set of B0 volumes registered to the first volume.
Returns:
The workflow
"""
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.interfaces import fsl
from clinica.utils.dwi import merge_volumes_tdim
inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]),
name="inputnode")
fslroi_ref = pe.Node(fsl.ExtractROI(args="0 1"), name="b0_reference")
tsize = num_b0s - 1
fslroi_moving = pe.Node(fsl.ExtractROI(args="1 " + str(tsize)),
name="b0_moving")
split_moving = pe.Node(fsl.Split(dimension="t"), name="split_b0_moving")
bet_ref = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
name="bet_ref")
dilate = pe.Node(
fsl.maths.MathsCommand(nan2zeros=True, args="-kernel sphere 5 -dilM"),
name="mask_dilate",
)
flirt = pe.MapNode(
fsl.FLIRT(
interp="spline",
dof=6,
bins=50,
save_log=True,
cost="corratio",
cost_func="corratio",
padding_size=10,
searchr_x=[-4, 4],
searchr_y=[-4, 4],
searchr_z=[-4, 4],
fine_search=1,
coarse_search=10,
),
name="b0_co_registration",
iterfield=["in_file"],
)
merge = pe.Node(fsl.Merge(dimension="t"), name="merge_registered_b0s")
thres = pe.MapNode(fsl.Threshold(thresh=0.0),
iterfield=["in_file"],
name="remove_negative")
insert_ref = pe.Node(
niu.Function(
input_names=["in_file1", "in_file2"],
output_names=["out_file"],
function=merge_volumes_tdim,
),
name="concat_ref_moving",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["out_file", "out_xfms"]),
name="outputnode")
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, fslroi_ref, [("in_file", "in_file")]),
(inputnode, fslroi_moving, [("in_file", "in_file")]),
(fslroi_moving, split_moving, [("roi_file", "in_file")]),
(fslroi_ref, bet_ref, [("roi_file", "in_file")]),
(bet_ref, dilate, [("mask_file", "in_file")]),
(dilate, flirt, [("out_file", "ref_weight"),
("out_file", "in_weight")]),
(fslroi_ref, flirt, [("roi_file", "reference")]),
(split_moving, flirt, [("out_files", "in_file")]),
(flirt, thres, [("out_file", "in_file")]),
(thres, merge, [("out_file", "in_files")]),
(merge, insert_ref, [("merged_file", "in_file2")]),
(fslroi_ref, insert_ref, [("roi_file", "in_file1")]),
(insert_ref, outputnode, [("out_file", "out_file")]),
(flirt, outputnode, [("out_matrix_file", "out_xfms")]),
])
return wf
def _cestreg(
datapath,
cestprefix,
offsetspath,
regdir,
outfolder=Path.cwd(),
CESTRefImage=0,
phantom=False,
):
""" _cestreg7T - Co-registers CEST data, and then registers that data
to the high resolution reference measurement set up in _reg_ref_7T
"""
cestdir = sorted(datapath.glob(f"*{cestprefix}*.nii.gz"))
offsets = np.loadtxt(str(offsetspath))
n_ones = np.where(abs(offsets) < 1)[0]
cestoutname = cestprefix
cestvol = nib.load(str(cestdir[0]))
if cestvol.ndim == 4:
fslsplit = fsl.Split()
fslsplit.inputs.in_file = str(cestdir[0])
fslsplit.inputs.out_base_name = str(regdir / f"{cestoutname}_Presplit")
fslsplit.inputs.output_type = "NIFTI_GZ"
fslsplit.inputs.dimension = "t"
fslsplit.run()
cestdir = sorted((regdir.glob(f"*{cestoutname}_Presplit*.nii.gz")))
concatname1 = [str(i) for v, i in enumerate(cestdir) if v < n_ones[1]]
concatname2 = [str(i) for v, i in enumerate(cestdir) if v > n_ones[-1]]
concatname2.append(str(cestdir[CESTRefImage]))
# Merge first half of files
fslmerge = fsl.Merge()
fslmerge.inputs.in_files = concatname1
fslmerge.inputs.dimension = "t"
fslmerge.inputs.merged_file = str(regdir / f"{cestoutname}_merged1.nii.gz")
fslmerge.run()
# Merge second half of files
fslmerge.inputs.in_files = concatname2
fslmerge.inputs.merged_file = str(regdir / f"{cestoutname}_merged2.nii.gz")
fslmerge.run()
# Motion-Correct Data
mcflirt = fsl.MCFLIRT()
mcflirt.inputs.in_file = str(regdir / f"{cestoutname}_merged1.nii.gz")
mcflirt.inputs.ref_vol = CESTRefImage
mcflirt.inputs.out_file = str(regdir / f"{cestoutname}_merged1_mcf.nii.gz")
if cestvol.ndim < 3:
mcflirt.inputs.args = "-2d"
mcflirt.run()
mcflirt = fsl.MCFLIRT()
mcflirt.inputs.in_file = str(regdir / f"{cestoutname}_merged2.nii.gz")
mcflirt.inputs.ref_vol = len(concatname2) - 1
mcflirt.inputs.out_file = str(regdir / f"{cestoutname}_merged2_mcf.nii.gz")
if cestvol.ndim < 3:
mcflirt.inputs.args = "-2d"
mcflirt.run()
# Extract one datapoint near middle of CEST
fslroi = fsl.ExtractROI()
fslroi.inputs.in_file = str(regdir / f"{cestoutname}_merged1_mcf.nii.gz")
fslroi.inputs.roi_file = str(regdir / f"{cestoutname}_mcfMin.nii.gz")
fslroi.inputs.t_min = len(concatname1) - 1
fslroi.inputs.t_size = 1
fslroi.run()
# Remove extra reference image in merged2
fslroi = fsl.ExtractROI()
fslroi.inputs.in_file = str(regdir / f"{cestoutname}_merged2_mcf.nii.gz")
fslroi.inputs.roi_file = str(regdir / f"{cestoutname}_merged2_mcf.nii.gz")
fslroi.inputs.t_min = 0
fslroi.inputs.t_size = len(concatname2) - 1
fslroi.run()
concatname3 = [str(i) for v, i in enumerate(cestdir) if v in n_ones[1:]]
concatname3.insert(0, str(regdir / f"{cestoutname}_mcfMin.nii.gz"))
fslmerge.inputs.in_files = concatname3
fslmerge.inputs.merged_file = str(regdir / f"{cestoutname}_merged3.nii.gz")
fslmerge.run()
# MCFLIRT middle range (e.g. where abs(offsets) < 1)
mcflirt = fsl.MCFLIRT()
mcflirt.inputs.in_file = str(regdir / f"{cestoutname}_merged3.nii.gz")
mcflirt.inputs.ref_vol = 0
mcflirt.inputs.out_file = str(regdir / f"{cestoutname}_merged3_mcf.nii.gz")
mcflirt.inputs.cost = "normmi"
if cestvol.ndim < 3:
mcflirt.inputs.args = "-2d"
mcflirt.run()
fslroi = fsl.ExtractROI()
fslroi.inputs.in_file = str(regdir / f"{cestoutname}_merged3_mcf.nii.gz")
fslroi.inputs.roi_file = str(regdir / f"{cestoutname}_mcfMid.nii.gz")
fslroi.inputs.t_min = 1
fslroi.inputs.t_size = len(n_ones) - 1
fslroi.run()
# Merge all CEST images together
fslmerge = fsl.Merge()
fslmerge.inputs.in_files = [
str(regdir / f"{cestoutname}_merged1_mcf.nii.gz"),
str(regdir / f"{cestoutname}_mcfMid.nii.gz"),
str(regdir / f"{cestoutname}_merged2_mcf.nii.gz"),
]
fslmerge.inputs.dimension = "t"
fslmerge.inputs.merged_file = str(regdir / f"{cestoutname}_mergedTot_mcf.nii.gz")
fslmerge.run()
# Separate CEST reference image to use as registration template for CEST data
fslroi = fsl.ExtractROI()
fslroi.inputs.in_file = str(regdir / f"{cestoutname}_mergedTot_mcf.nii.gz")
fslroi.inputs.roi_file = str(regdir / f"{cestoutname}_prereg.nii.gz")
fslroi.inputs.t_min = CESTRefImage
fslroi.inputs.t_size = 1
fslroi.run()
# Run FAST on Ref image
cestfast = fsl.FAST()
cestfast.inputs.in_files = str(regdir / f"{cestoutname}_prereg.nii.gz")
cestfast.inputs.out_basename = str(regdir / f"{cestoutname}_bc")
cestfast.inputs.output_biascorrected = True
cestfast.inputs.output_biasfield = True
cestfast.inputs.no_pve = True
cestfast.run(ignore_exception=True)
# Skull Strip CEST Image
if phantom:
fmaths = fsl.ImageMaths()
fmaths.inputs.in_file = str(regdir / f"{cestoutname}_bc_restore.nii.gz")
fmaths.inputs.out_file = str(regdir / f"{cestoutname}_prereg_brain.nii.gz")
fmaths.inputs.op_string = "-thrp 10"
fmaths.run()
fmaths.inputs.in_file = str(regdir / f"{cestoutname}_prereg_brain.nii.gz")
fmaths.inputs.out_file = str(regdir / f"{cestoutname}_prereg_brain_mask.nii.gz")
fmaths.inputs.op_string = "-bin"
fmaths.run()
else:
betcest0 = fsl.BET()
betcest0.inputs.in_file = str(regdir / f"{cestoutname}_bc_restore.nii.gz")
betcest0.inputs.out_file = str(regdir / f"{cestoutname}_prereg_brain.nii.gz")
betcest0.inputs.mask = True
if (
cestvol.ndim <= 2
or cestvol.shape[3] < 5
or cestvol.header.get_zooms()[2] * cestvol.shape[2] < 25
):
betcest0.inputs.padding = True
betcest0.run()
# BET rest of data
fmaths = fsl.ImageMaths()
fmaths.inputs.in_file = str(regdir / f"{cestoutname}_mergedTot_mcf.nii.gz")
fmaths.inputs.op_string = "-mul"
fmaths.inputs.in_file2 = str(regdir / f"{cestoutname}_prereg_brain_mask.nii.gz")
fmaths.inputs.out_file = str(regdir / f"{cestoutname}_mergedTot_bc_brain.nii.gz")
fmaths.run()
# Move CEST data to analysis folder
shutil.copyfile(
str(regdir / f"{cestoutname}_mergedTot_bc_brain.nii.gz"),
str(outfolder / f"{cestoutname}_reg.nii.gz"),
)
# Correct Mask so it is set at extent of CEST data
fslmaths = fsl.ImageMaths()
fslmaths.inputs.in_file = str(regdir / "Ref_Mask.nii.gz")
fslmaths.inputs.op_string = "-thr 0.95 -mul"
fslmaths.inputs.in_file2 = str(outfolder / f"{cestoutname}_reg.nii.gz")
fslmaths.inputs.args = "-bin"
fslmaths.inputs.out_file = str(regdir / "Ref_Mask.nii.gz")
fslmaths.run()
# Copy Corrected mask into Analysis folder
shutil.copyfile(str(regdir / "Ref_Mask.nii.gz"), str(outfolder / "Ref_Mask.nii.gz"))
# Change mask to only a single volume (instead of 4D volume from CEST data)
maskroi = fsl.ExtractROI()
maskroi.inputs.in_file = str(outfolder / "Ref_Mask.nii.gz")
maskroi.inputs.roi_file = str(outfolder / "Ref_Mask.nii.gz")
maskroi.inputs.t_min = 0
maskroi.inputs.t_size = 1
maskroi.run()
return None
def epi_pipeline(name="susceptibility_distortion_correction_using_t1"):
"""
This workflow allows to correct for echo-planareinduced susceptibility artifacts without fieldmap
(e.g. ADNI Database) by elastically register DWIs to their respective baseline T1-weighted
structural scans using an inverse consistent registration algorithm with a mutual information cost
function (SyN algorithm). This workflow allows also a coregistration of DWIs with their respective
baseline T1-weighted structural scans in order to latter combine tracks and cortex parcelation.
.. warning:: This workflow rotates the `b`-vectors'
.. References
.. Nir et al. (Neurobiology of Aging 2015)- Connectivity network measures predict volumetric atrophy in mild cognitive impairment
Leow et al. (IEEE Trans Med Imaging 2007)- Statistical Properties of Jacobian Maps and the Realization of Unbiased Large Deformation Nonlinear Image Registration
Example
-------
>>> epi = epi_pipeline()
>>> epi.inputs.inputnode.DWI = 'DWI.nii'
>>> epi.inputs.inputnode.bvec = 'bvec.txt'
>>> epi.inputs.inputnode.T1 = 'T1.nii'
>>> epi.run() # doctest: +SKIP
"""
import nipype.interfaces.c3 as c3
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from clinica.pipelines.dwi_preprocessing_using_t1.dwi_preprocessing_using_t1_utils import (
ants_combin_transform,
ants_registration_syn_quick,
ants_warp_image_multi_transform,
change_itk_transform_type,
create_jacobian_determinant_image,
expend_matrix_list,
rotate_bvecs,
)
inputnode = pe.Node(niu.IdentityInterface(fields=["T1", "DWI", "bvec"]),
name="inputnode")
split = pe.Node(fsl.Split(dimension="t"), name="SplitDWIs")
pick_ref = pe.Node(niu.Select(), name="Pick_b0")
pick_ref.inputs.index = [0]
flirt_b0_2_T1 = pe.Node(interface=fsl.FLIRT(dof=6), name="flirt_B0_2_T1")
flirt_b0_2_T1.inputs.interp = "spline"
flirt_b0_2_T1.inputs.cost = "normmi"
flirt_b0_2_T1.inputs.cost_func = "normmi"
apply_xfm = pe.Node(interface=fsl.preprocess.ApplyXFM(), name="apply_xfm")
apply_xfm.inputs.apply_xfm = True
expend_matrix = pe.Node(
interface=niu.Function(
input_names=["in_matrix", "in_bvec"],
output_names=["out_matrix_list"],
function=expend_matrix_list,
),
name="expend_matrix",
)
rot_bvec = pe.Node(
niu.Function(
input_names=["in_matrix", "in_bvec"],
output_names=["out_file"],
function=rotate_bvecs,
),
name="Rotate_Bvec",
)
antsRegistrationSyNQuick = pe.Node(
interface=niu.Function(
input_names=["fix_image", "moving_image"],
output_names=[
"image_warped",
"affine_matrix",
"warp",
"inverse_warped",
"inverse_warp",
],
function=ants_registration_syn_quick,
),
name="antsRegistrationSyNQuick",
)
c3d_flirt2ants = pe.Node(c3.C3dAffineTool(), name="fsl_reg_2_itk")
c3d_flirt2ants.inputs.itk_transform = True
c3d_flirt2ants.inputs.fsl2ras = True
change_transform = pe.Node(
niu.Function(
input_names=["input_affine_file"],
output_names=["updated_affine_file"],
function=change_itk_transform_type,
),
name="change_transform_type",
)
merge_transform = pe.Node(niu.Merge(3), name="MergeTransforms")
apply_transform = pe.MapNode(
interface=niu.Function(
input_names=["fix_image", "moving_image", "ants_warp_affine"],
output_names=["out_warp_field", "out_warped"],
function=ants_combin_transform,
),
iterfield=["moving_image"],
name="warp_filed",
)
jacobian = pe.MapNode(
interface=niu.Function(
input_names=["imageDimension", "deformationField", "outputImage"],
output_names=["outputImage"],
function=create_jacobian_determinant_image,
),
iterfield=["deformationField"],
name="jacobian",
)
jacobian.inputs.imageDimension = 3
jacobian.inputs.outputImage = "Jacobian_image.nii.gz"
jacmult = pe.MapNode(
fsl.MultiImageMaths(op_string="-mul %s"),
iterfield=["in_file", "operand_files"],
name="ModulateDWIs",
)
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=[
"DWI_2_T1_Coregistration_matrix",
"epi_correction_deformation_field",
"epi_correction_affine_transform",
"epi_correction_image_warped",
"DWIs_epicorrected",
"warp_epi",
"out_bvec",
]),
name="outputnode",
)
wf = pe.Workflow(name="epi_pipeline")
wf.connect([(inputnode, split, [("DWI", "in_file")])])
wf.connect([(split, pick_ref, [("out_files", "inlist")])])
wf.connect([(pick_ref, flirt_b0_2_T1, [("out", "in_file")])])
wf.connect([(inputnode, flirt_b0_2_T1, [("T1", "reference")])])
wf.connect([(inputnode, rot_bvec, [("bvec", "in_bvec")])])
wf.connect([(flirt_b0_2_T1, expend_matrix, [("out_matrix_file",
"in_matrix")])])
wf.connect([(inputnode, expend_matrix, [("bvec", "in_bvec")])])
wf.connect([(expend_matrix, rot_bvec, [("out_matrix_list", "in_matrix")])])
wf.connect([(inputnode, antsRegistrationSyNQuick, [("T1", "fix_image")])])
wf.connect([(flirt_b0_2_T1, antsRegistrationSyNQuick, [("out_file",
"moving_image")])])
wf.connect([(inputnode, c3d_flirt2ants, [("T1", "reference_file")])])
wf.connect([(pick_ref, c3d_flirt2ants, [("out", "source_file")])])
wf.connect([(flirt_b0_2_T1, c3d_flirt2ants, [("out_matrix_file",
"transform_file")])])
wf.connect([(c3d_flirt2ants, change_transform, [("itk_transform",
"input_affine_file")])])
wf.connect([(antsRegistrationSyNQuick, merge_transform, [("warp", "in1")])
])
wf.connect([(antsRegistrationSyNQuick, merge_transform, [("affine_matrix",
"in2")])])
wf.connect([(change_transform, merge_transform, [("updated_affine_file",
"in3")])])
wf.connect([(inputnode, apply_transform, [("T1", "fix_image")])])
wf.connect([(split, apply_transform, [("out_files", "moving_image")])])
wf.connect([(merge_transform, apply_transform, [("out", "ants_warp_affine")
])])
wf.connect([(apply_transform, jacobian, [("out_warp_field",
"deformationField")])])
wf.connect([(apply_transform, jacmult, [("out_warped", "operand_files")])])
wf.connect([(jacobian, jacmult, [("outputImage", "in_file")])])
wf.connect([(jacmult, thres, [("out_file", "in_file")])])
wf.connect([(thres, merge, [("out_file", "in_files")])])
wf.connect([(merge, outputnode, [("merged_file", "DWIs_epicorrected")])])
wf.connect([(
flirt_b0_2_T1,
outputnode,
[("out_matrix_file", "DWI_2_T1_Coregistration_matrix")],
)])
wf.connect([(
antsRegistrationSyNQuick,
outputnode,
[
("warp", "epi_correction_deformation_field"),
("affine_matrix", "epi_correction_affine_transform"),
("image_warped", "epi_correction_image_warped"),
],
)])
wf.connect([(merge_transform, outputnode, [("out", "warp_epi")])])
wf.connect([(rot_bvec, outputnode, [("out_file", "out_bvec")])])
return wf
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 <http://dx.doi.org/10.1016/j.neuroimage.2014.07.061>`_.squeue
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
"""
import nipype.interfaces.ants as ants
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]),
name="inputnode")
outputnode = pe.Node(niu.IdentityInterface(fields=["out_file", "b0_mask"]),
name="outputnode")
get_b0 = pe.Node(fsl.ExtractROI(t_min=0, t_size=1), name="get_b0")
mask_b0 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
name="mask_b0")
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, get_b0, [("in_file", "in_file")]),
(get_b0, n4, [("roi_file", "input_image")]),
(get_b0, mask_b0, [("roi_file", "in_file")]),
(mask_b0, n4, [("mask_file", "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")]),
(mask_b0, outputnode, [("mask_file", "b0_mask")]),
])
return wf
def ecc_pipeline(name="eddy_correct"):
"""
ECC stands for Eddy currents correction.
Creates a pipelines that corrects for artifacts induced by Eddy currents in
dMRI sequences.
It takes a series of diffusion weighted images and linearly co-registers
them to one reference image (the average of all b0s in the dataset).
DWIs are also modulated by the determinant of the Jacobian as indicated by
[Jones10]_ and [Rohde04]_.
A list of rigid transformation matrices can be provided, sourcing from a
:func:`.hmc_pipeline` workflow, to initialize registrations in a *motion
free* framework.
A list of affine transformation matrices is available as output, so that
transforms can be chained (discussion
`here <https://github.com/nipy/nipype/pull/530#issuecomment-14505042>`_).
.. admonition:: References
.. [Jones10] Jones DK, `The signal intensity must be modulated by the
determinant of the Jacobian when correcting for eddy currents in
diffusion MRI
<http://cds.ismrm.org/protected/10MProceedings/files/1644_129.pdf>`_,
Proc. ISMRM 18th Annual Meeting, (2010).
.. [Rohde04] Rohde et al., `Comprehensive Approach for Correction of
Motion and Distortion in Diffusion-Weighted MRI
<http://stbb.nichd.nih.gov/pdf/com_app_cor_mri04.pdf>`_, MRM
51:103-114 (2004).
Example
-------
from nipype.workflows.dmri.fsl.artifacts import ecc_pipeline
ecc = ecc_pipeline()
ecc.inputs.inputnode.in_file = 'diffusion.nii'
ecc.inputs.inputnode.in_bval = 'diffusion.bval'
ecc.inputs.inputnode.in_mask = 'mask.nii'
ecc.run() # doctest: +SKIP
Inputs::
inputnode.in_file - input dwi file
inputnode.in_mask - weights mask of reference image (a file with data \
range sin [0.0, 1.0], indicating the weight of each voxel when computing the \
metric.
inputnode.in_bval - b-values table
inputnode.in_xfms - list of matrices to initialize registration (from \
head-motion correction)
Outputs::
outputnode.out_file - corrected dwi file
outputnode.out_xfms - list of transformation matrices
"""
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.workflows.data import get_flirt_schedule
from nipype.workflows.dmri.fsl.artifacts import _xfm_jacobian
from nipype.workflows.dmri.fsl.utils import (
extract_bval,
recompose_dwi,
recompose_xfm,
)
from clinica.utils.dwi import merge_volumes_tdim
from clinica.workflows.dwi_preprocessing import dwi_flirt
params = dict(
dof=12,
no_search=True,
interp="spline",
bgvalue=0,
schedule=get_flirt_schedule("ecc"),
)
inputnode = pe.Node(
niu.IdentityInterface(
fields=["in_file", "in_bval", "in_mask", "in_xfms"]),
name="inputnode",
)
getb0 = pe.Node(fsl.ExtractROI(t_min=0, t_size=1), name="get_b0")
pick_dws = pe.Node(
niu.Function(
input_names=["in_dwi", "in_bval", "b"],
output_names=["out_file"],
function=extract_bval,
),
name="extract_dwi",
)
pick_dws.inputs.b = "diff"
flirt = dwi_flirt(flirt_param=params, excl_nodiff=True)
mult = pe.MapNode(
fsl.BinaryMaths(operation="mul"),
name="ModulateDWIs",
iterfield=["in_file", "operand_value"],
)
thres = pe.MapNode(fsl.Threshold(thresh=0.0),
iterfield=["in_file"],
name="RemoveNegative")
split = pe.Node(fsl.Split(dimension="t"), name="SplitDWIs")
get_mat = pe.Node(
niu.Function(
input_names=["in_bval", "in_xfms"],
output_names=["out_files"],
function=recompose_xfm,
),
name="GatherMatrices",
)
merge = pe.Node(
niu.Function(
input_names=["in_dwi", "in_bval", "in_corrected"],
output_names=["out_file"],
function=recompose_dwi,
),
name="MergeDWIs",
)
merged_volumes = pe.Node(
niu.Function(
input_names=["in_file1", "in_file2"],
output_names=["out_file"],
function=merge_volumes_tdim,
),
name="merge_enhanced_ref_dwis",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["out_file", "out_xfms"]),
name="outputnode")
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, getb0, [("in_file", "in_file")]),
(inputnode, pick_dws, [("in_file", "in_dwi"), ("in_bval", "in_bval")]),
(
flirt,
merged_volumes,
[
("outputnode.out_ref", "in_file1"),
("outputnode.out_file", "in_file2"),
],
),
(merged_volumes, merge, [("out_file", "in_dwi")]),
(inputnode, merge, [("in_bval", "in_bval")]),
(
inputnode,
flirt,
[
("in_mask", "inputnode.ref_mask"),
("in_xfms", "inputnode.in_xfms"),
("in_bval", "inputnode.in_bval"),
],
),
(inputnode, get_mat, [("in_bval", "in_bval")]),
(getb0, flirt, [("roi_file", "inputnode.reference")]),
(pick_dws, flirt, [("out_file", "inputnode.in_file")]),
(flirt, get_mat, [("outputnode.out_xfms", "in_xfms")]),
(flirt, mult, [(("outputnode.out_xfms", _xfm_jacobian),
"operand_value")]),
(flirt, split, [("outputnode.out_file", "in_file")]),
(split, mult, [("out_files", "in_file")]),
(mult, thres, [("out_file", "in_file")]),
(thres, merge, [("out_file", "in_corrected")]),
(get_mat, outputnode, [("out_files", "out_xfms")]),
(merge, outputnode, [("out_file", "out_file")]),
])
return wf
def create_motion_correct_pipeline(name='motion_correct'):
"""Creates a pipeline that corrects for motion artifact in dMRI sequences.
It takes a series of diffusion weighted images and rigidly co-registers
them to one reference image. Finally, the b-matrix is rotated accordingly
(Leemans et al. 2009 - http://www.ncbi.nlm.nih.gov/pubmed/19319973),
making use of the rotation matrix obtained by FLIRT.
.. deprecated:: 0.9.3
Use :func:`nipype.workflows.dmri.preprocess.epi.hmc_pipeline` instead.
.. warning:: This workflow rotates the b-vectors, so please be adviced
that not all the dicom converters ensure the consistency between the resulting
nifti orientation and the b matrix table (e.g. dcm2nii checks it).
Example
-------
>>> nipype_motioncorrect = create_motion_correct_pipeline('nipype_motioncorrect')
>>> nipype_motioncorrect.inputs.inputnode.in_file = 'diffusion.nii'
>>> nipype_motioncorrect.inputs.inputnode.in_bvec = 'diffusion.bvec'
>>> nipype_motioncorrect.inputs.inputnode.ref_num = 0
>>> nipype_motioncorrect.run() # doctest: +SKIP
Inputs::
inputnode.in_file
inputnode.ref_num
inputnode.in_bvec
Outputs::
outputnode.motion_corrected
outputnode.out_bvec
"""
warnings.warn(
('This workflow is deprecated from v.1.0.0, use '
'nipype.workflows.dmri.preprocess.epi.hmc_pipeline instead'),
DeprecationWarning)
inputnode = pe.Node(
niu.IdentityInterface(fields=['in_file', 'ref_num', 'in_bvec']),
name='inputnode')
pipeline = pe.Workflow(name=name)
split = pe.Node(fsl.Split(dimension='t'), name='split')
pick_ref = pe.Node(niu.Select(), name='pick_ref')
coregistration = pe.MapNode(fsl.FLIRT(no_search=True,
interp='spline',
padding_size=1,
dof=6),
name='coregistration',
iterfield=['in_file'])
rotate_bvecs = pe.Node(niu.Function(input_names=['in_bvec', 'in_matrix'],
output_names=['out_file'],
function=_rotate_bvecs),
name='rotate_b_matrix')
merge = pe.Node(fsl.Merge(dimension='t'), name='merge')
outputnode = pe.Node(
niu.IdentityInterface(fields=['motion_corrected', 'out_bvec']),
name='outputnode')
pipeline.connect([
(inputnode, split, [('in_file', 'in_file')]),
(split, pick_ref, [('out_files', 'inlist')]),
(inputnode, pick_ref, [('ref_num', 'index')]),
(split, coregistration, [('out_files', 'in_file')]),
(inputnode, rotate_bvecs, [('in_bvec', 'in_bvec')]),
(coregistration, rotate_bvecs, [('out_matrix_file', 'in_matrix')]),
(pick_ref, coregistration, [('out', 'reference')]),
(coregistration, merge, [('out_file', 'in_files')]),
(merge, outputnode, [('merged_file', 'motion_corrected')]),
(rotate_bvecs, outputnode, [('out_file', 'out_bvec')])
])
return pipeline
def create_indnet_workflow(hp_cutoff=100,
smoothing=5,
smm_threshold=0.5,
binarise_threshold=0.5,
melodic_seed=None,
aggr_aroma=False,
name="indnet"):
indnet = Workflow(name=name)
# Input node
inputspec = Node(utility.IdentityInterface(
fields=['anat_file', 'func_file', 'templates', 'networks']),
name='inputspec')
# T1 skullstrip
anat_bet = Node(fsl.BET(), name="anat_bet")
# EPI preprocessing
func_realignsmooth = create_featreg_preproc(highpass=False,
whichvol='first',
name='func_realignsmooth')
func_realignsmooth.inputs.inputspec.fwhm = smoothing
# Transform EPI to MNI space
func_2mni = create_reg_workflow(name='func_2mni')
func_2mni.inputs.inputspec.target_image = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
func_2mni.inputs.inputspec.target_image_brain = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
func_2mni.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
# Segmentation of T1
anat_segmentation = Node(fsl.FAST(output_biascorrected=True),
name='anat_segmentation')
# Transfrom segments to EPI space
segments_2func = create_segments_2func_workflow(
threshold=binarise_threshold, name='segments_2func')
# Transform templates to EPI space
templates_2func = create_templates_2func_workflow(
threshold=binarise_threshold, name='templates_2func')
# Mask network templates with GM
gm_mask_templates = MapNode(fsl.ImageMaths(op_string='-mul'),
iterfield=['in_file2'],
name='gm_mask_templates')
# Mask for ICA-AROMA and statistics
func_brainmask = Node(fsl.BET(frac=0.3,
mask=True,
no_output=True,
robust=True),
name='func_brainmask')
# Melodic ICA
if melodic_seed != None:
func_melodic = Node(fsl.MELODIC(args='--seed={}'.format(melodic_seed),
out_stats=True),
name='func_melodic')
# ICA-AROMA
func_aroma = Node(fsl.ICA_AROMA(), name='func_aroma')
if aggr_aroma:
func_aroma.inputs.denoise_type = 'aggr'
else:
func_aroma.inputs.denoise_type = 'nonaggr'
# Highpass filter ICA results
func_highpass = create_highpass_filter(cutoff=hp_cutoff,
name='func_highpass')
# Calculate mean CSF sgnal
csf_meansignal = Node(fsl.ImageMeants(), name='csf_meansignal')
# Calculate mean WM signal
wm_meansignal = Node(fsl.ImageMeants(), name='wm_meansignal')
# Calculate mean non-brain signal
nonbrain_meansignal = create_nonbrain_meansignal(
name='nonbrain_meansignal')
# Calculate first Eigenvariates
firsteigenvariates = MapNode(fsl.ImageMeants(show_all=True, eig=True),
iterfield=['mask'],
name='firsteigenvariates')
# Combine first eigenvariates and wm/csf/non-brain signals
regressors = Node(utility.Merge(4), name='regressors')
# z-transform regressors
ztransform = MapNode(Ztransform(),
iterfield=['in_file'],
name='ztransform')
# Create design matrix
designmatrix = Node(DesignMatrix(), name='designmatrix')
# Create contrasts
contrasts = Node(Contrasts(), name='contrasts')
# GLM
glm = Node(fsl.GLM(), name='glm')
glm.inputs.out_z_name = 'z_stats.nii.gz'
glm.inputs.demean = True
# Split z-maps
zmaps = Node(fsl.Split(), name='zmaps')
zmaps.inputs.dimension = 't'
# Spatial Mixture Modelling
smm = MapNode(fsl.SMM(), iterfield=['spatial_data_file'], name='smm')
# Transform probability maps to native (anat) space
actmaps_2anat = MapNode(fsl.ApplyXFM(),
iterfield=['in_file'],
name='actmaps_2anat')
# Transform probability maps to MNI space
actmaps_2mni = MapNode(fsl.ApplyWarp(),
iterfield=['in_file'],
name='actmaps_2mni')
actmaps_2mni.inputs.ref_file = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
# Create network masks in native (func) space
network_masks_func = create_network_masks_workflow(
name='network_masks_func', smm_threshold=smm_threshold)
# Create network masks in native (anat) space
network_masks_anat = create_network_masks_workflow(
name='network_masks_anat', smm_threshold=smm_threshold)
# Create network masks in MNI space
network_masks_mni = create_network_masks_workflow(
name='network_masks_mni', smm_threshold=smm_threshold)
# Output node
outputspec = Node(utility.IdentityInterface(fields=[
'network_masks_func_main', 'network_masks_func_exclusive',
'network_masks_anat_main', 'network_masks_anat_exclusive',
'network_masks_mni_main', 'network_masks_mni_exclusive',
'preprocessed_func_file', 'preprocessed_anat_file',
'motion_parameters', 'func2anat_transform', 'anat2mni_transform'
]),
name='outputspec')
# Helper functions
def get_first_item(x):
try:
return x[0]
except:
return x
def get_second_item(x):
return x[1]
def get_third_item(x):
return x[2]
def get_components(x):
return [y['components'] for y in x]
# Connect the nodes
# anat_bet
indnet.connect(inputspec, 'anat_file', anat_bet, 'in_file')
# func_realignsmooth
indnet.connect(inputspec, 'func_file', func_realignsmooth,
'inputspec.func')
# func_2mni
indnet.connect(func_realignsmooth,
('outputspec.smoothed_files', get_first_item), func_2mni,
'inputspec.source_files')
indnet.connect(inputspec, 'anat_file', func_2mni,
'inputspec.anatomical_image')
indnet.connect(func_realignsmooth, 'outputspec.reference', func_2mni,
'inputspec.mean_image')
# anat_segmentation
indnet.connect(anat_bet, 'out_file', anat_segmentation, 'in_files')
# segments_2func
indnet.connect(anat_segmentation, 'partial_volume_files', segments_2func,
'inputspec.segments')
indnet.connect(func_2mni, 'outputspec.func2anat_transform', segments_2func,
'inputspec.premat')
indnet.connect(func_realignsmooth, 'outputspec.mean', segments_2func,
'inputspec.func_file')
# templates_2func
indnet.connect(func_realignsmooth, 'outputspec.mean', templates_2func,
'inputspec.func_file')
indnet.connect(func_2mni, 'outputspec.func2anat_transform',
templates_2func, 'inputspec.premat')
indnet.connect(func_2mni, 'outputspec.anat2target_transform',
templates_2func, 'inputspec.warp')
indnet.connect(inputspec, 'templates', templates_2func,
'inputspec.templates')
# gm_mask_templates
indnet.connect(segments_2func,
('outputspec.segments_2func_files', get_second_item),
gm_mask_templates, 'in_file')
indnet.connect(templates_2func, 'outputspec.templates_2func_files',
gm_mask_templates, 'in_file2')
# func_brainmask
indnet.connect(func_realignsmooth, 'outputspec.mean', func_brainmask,
'in_file')
# func_melodic
if melodic_seed != None:
indnet.connect(func_realignsmooth,
('outputspec.smoothed_files', get_first_item),
func_melodic, 'in_files')
indnet.connect(func_brainmask, 'mask_file', func_melodic, 'mask')
# func_aroma
indnet.connect(func_realignsmooth,
('outputspec.smoothed_files', get_first_item), func_aroma,
'in_file')
indnet.connect(func_2mni, 'outputspec.func2anat_transform', func_aroma,
'mat_file')
indnet.connect(func_2mni, 'outputspec.anat2target_transform', func_aroma,
'fnirt_warp_file')
indnet.connect(func_realignsmooth,
('outputspec.motion_parameters', get_first_item),
func_aroma, 'motion_parameters')
indnet.connect(func_brainmask, 'mask_file', func_aroma, 'mask')
if melodic_seed != None:
indnet.connect(func_melodic, 'out_dir', func_aroma, 'melodic_dir')
# func_highpass
if aggr_aroma:
indnet.connect(func_aroma, 'aggr_denoised_file', func_highpass,
'inputspec.in_file')
else:
indnet.connect(func_aroma, 'nonaggr_denoised_file', func_highpass,
'inputspec.in_file')
# csf_meansignal
indnet.connect(segments_2func,
('outputspec.segments_2func_files', get_first_item),
csf_meansignal, 'mask')
indnet.connect(func_highpass, 'outputspec.filtered_file', csf_meansignal,
'in_file')
# wm_meansignal
indnet.connect(segments_2func,
('outputspec.segments_2func_files', get_third_item),
wm_meansignal, 'mask')
indnet.connect(func_highpass, 'outputspec.filtered_file', wm_meansignal,
'in_file')
# nonbrain_meansignal
indnet.connect(inputspec, 'func_file', nonbrain_meansignal,
'inputspec.func_file')
# firsteigenvariates
indnet.connect(gm_mask_templates, 'out_file', firsteigenvariates, 'mask')
indnet.connect(func_highpass, 'outputspec.filtered_file',
firsteigenvariates, 'in_file')
# regressors
indnet.connect(firsteigenvariates, 'out_file', regressors, 'in1')
indnet.connect(wm_meansignal, 'out_file', regressors, 'in2')
indnet.connect(csf_meansignal, 'out_file', regressors, 'in3')
indnet.connect(nonbrain_meansignal, 'outputspec.nonbrain_regressor',
regressors, 'in4')
# ztransform
indnet.connect(regressors, 'out', ztransform, 'in_file')
# designmatrix
indnet.connect(ztransform, 'out_file', designmatrix, 'in_files')
# contrasts
indnet.connect(inputspec, ('networks', get_components), contrasts,
'in_list')
indnet.connect(designmatrix, 'out_file', contrasts, 'design')
# glm
indnet.connect(designmatrix, 'out_file', glm, 'design')
indnet.connect(contrasts, 'out_file', glm, 'contrasts')
indnet.connect(func_brainmask, 'mask_file', glm, 'mask')
indnet.connect(func_highpass, 'outputspec.filtered_file', glm, 'in_file')
# zmaps
indnet.connect(glm, 'out_z', zmaps, 'in_file')
# smm
indnet.connect(zmaps, 'out_files', smm, 'spatial_data_file')
indnet.connect(func_brainmask, 'mask_file', smm, 'mask')
# actmaps_2anat
indnet.connect(smm, 'activation_p_map', actmaps_2anat, 'in_file')
indnet.connect(func_2mni, 'outputspec.func2anat_transform', actmaps_2anat,
'in_matrix_file')
indnet.connect(anat_bet, 'out_file', actmaps_2anat, 'reference')
# actmaps_2mni
indnet.connect(smm, 'activation_p_map', actmaps_2mni, 'in_file')
indnet.connect(templates_2func, 'outputspec.func_2mni_warp', actmaps_2mni,
'field_file')
# network_masks_func
indnet.connect(smm, 'activation_p_map', network_masks_func,
'inputspec.actmaps')
indnet.connect(inputspec, 'networks', network_masks_func,
'inputspec.networks')
# network_masks_anat
indnet.connect(actmaps_2anat, 'out_file', network_masks_anat,
'inputspec.actmaps')
indnet.connect(inputspec, 'networks', network_masks_anat,
'inputspec.networks')
# network_masks_mni
indnet.connect(actmaps_2mni, 'out_file', network_masks_mni,
'inputspec.actmaps')
indnet.connect(inputspec, 'networks', network_masks_mni,
'inputspec.networks')
# output node
indnet.connect(network_masks_func, 'outputspec.main_masks', outputspec,
'network_masks_func_main')
indnet.connect(network_masks_func, 'outputspec.exclusive_masks',
outputspec, 'network_masks_func_exclusive')
indnet.connect(network_masks_anat, 'outputspec.main_masks', outputspec,
'network_masks_anat_main')
indnet.connect(network_masks_anat, 'outputspec.exclusive_masks',
outputspec, 'network_masks_anat_exclusive')
indnet.connect(network_masks_mni, 'outputspec.main_masks', outputspec,
'network_masks_mni_main')
indnet.connect(network_masks_mni, 'outputspec.exclusive_masks', outputspec,
'network_masks_mni_exclusive')
indnet.connect(func_highpass, 'outputspec.filtered_file', outputspec,
'preprocessed_func_file')
indnet.connect(anat_segmentation, 'restored_image', outputspec,
'preprocessed_anat_file')
indnet.connect(func_realignsmooth,
('outputspec.motion_parameters', get_first_item),
outputspec, 'motion_parameters')
indnet.connect(func_2mni, 'outputspec.func2anat_transform', outputspec,
'func2anat_transform')
indnet.connect(func_2mni, 'outputspec.anat2target_transform', outputspec,
'anat2mni_transform')
return indnet
def dwi_flirt(name="DWICoregistration", excl_nodiff=False, flirt_param={}):
"""
Generates a workflow for linear registration of dwi volumes using flirt.
Inputnode
---------
reference : FILE
Mandatory input. Reference data set.
in_file : FILE
Mandatory input. Moving data set.
ref_mask : FILE
Mandatory input. Binary mask of the reference volume.
in_xfms : FILE
Mandatory input. Intialisation matrices for flirt.
in_bval : FILE
Mandatory input. B values file.
"""
import nipype.interfaces.ants as ants
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.workflows.dmri.fsl.utils import _checkinitxfm, enhance
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")
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name="Bias")
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", "out_ref"]),
name="outputnode",
)
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"],
)
# enhb0.inputs.clip_limit = clip_limit
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, split, [("in_file", "in_file")]),
(inputnode, dilate, [("ref_mask", "in_file")]),
(inputnode, n4, [("reference", "input_image"),
("ref_mask", "mask_image")]),
# (inputnode, flirt, [('ref_mask', 'reference')]),
(n4, enhb0, [("output_image", "in_file")]),
(enhb0, flirt, [("out_file", "reference")]),
(inputnode, initmat, [("in_xfms", "in_xfms"), ("in_bval", "in_bval")]),
(split, enhdw, [("out_files", "in_file")]),
(dilate, enhdw, [("out_file", "in_mask")]),
(dilate, flirt, [("out_file", "ref_weight"),
("out_file", "in_weight")]),
(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")]),
(enhb0, outputnode, [("out_file", "out_ref")]),
(flirt, outputnode, [("out_matrix_file", "out_xfms")]),
])
return wf
def sdc_fmb(name='fmb_correction',
interp='Linear',
fugue_params=dict(smooth3d=2.0)):
"""
SDC stands for susceptibility distortion correction. FMB stands for
fieldmap-based.
The fieldmap based (FMB) method implements SDC by using a mapping of the
B0 field as proposed by [Jezzard95]_. This workflow uses the implementation
of FSL (`FUGUE <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE>`_). Phase
unwrapping is performed using `PRELUDE
<http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/fugue/prelude.html>`_
[Jenkinson03]_. Preparation of the fieldmap is performed reproducing the
script in FSL `fsl_prepare_fieldmap
<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide#SIEMENS_data>`_.
Example
-------
>>> from nipype.workflows.dmri.fsl.artifacts import sdc_fmb
>>> fmb = sdc_fmb()
>>> fmb.inputs.inputnode.in_file = 'diffusion.nii'
>>> fmb.inputs.inputnode.in_ref = list(range(0, 30, 6))
>>> fmb.inputs.inputnode.in_mask = 'mask.nii'
>>> fmb.inputs.inputnode.bmap_mag = 'magnitude.nii'
>>> fmb.inputs.inputnode.bmap_pha = 'phase.nii'
>>> fmb.inputs.inputnode.settings = 'epi_param.txt'
>>> fmb.run() # doctest: +SKIP
.. warning:: Only SIEMENS format fieldmaps are supported.
.. admonition:: References
.. [Jezzard95] Jezzard P, and Balaban RS, `Correction for geometric
distortion in echo planar images from B0 field variations
<https://doi.org/10.1002/mrm.1910340111>`_,
MRM 34(1):65-73. (1995). doi: 10.1002/mrm.1910340111.
.. [Jenkinson03] Jenkinson M., `Fast, automated, N-dimensional
phase-unwrapping algorithm <https://doi.org/10.1002/mrm.10354>`_,
MRM 49(1):193-197, 2003, doi: 10.1002/mrm.10354.
"""
epi_defaults = {
'delta_te': 2.46e-3,
'echospacing': 0.77e-3,
'acc_factor': 2,
'enc_dir': u'AP'
}
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ref', 'in_mask', 'bmap_pha', 'bmap_mag', 'settings'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'out_vsm', 'out_warp']),
name='outputnode')
r_params = pe.Node(JSONFileGrabber(defaults=epi_defaults),
name='SettingsGrabber')
eff_echo = pe.Node(niu.Function(function=_eff_t_echo,
input_names=['echospacing', 'acc_factor'],
output_names=['eff_echo']),
name='EffEcho')
firstmag = pe.Node(fsl.ExtractROI(t_min=0, t_size=1), name='GetFirst')
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='Bias')
bet = pe.Node(fsl.BET(frac=0.4, mask=True), name='BrainExtraction')
dilate = pe.Node(fsl.maths.MathsCommand(nan2zeros=True,
args='-kernel sphere 5 -dilM'),
name='MskDilate')
pha2rads = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=siemens2rads),
name='PreparePhase')
prelude = pe.Node(fsl.PRELUDE(process3d=True), name='PhaseUnwrap')
rad2rsec = pe.Node(niu.Function(input_names=['in_file', 'delta_te'],
output_names=['out_file'],
function=rads2radsec),
name='ToRadSec')
baseline = pe.Node(niu.Function(input_names=['in_file', 'index'],
output_names=['out_file'],
function=time_avg),
name='Baseline')
fmm2b0 = pe.Node(ants.Registration(output_warped_image=True),
name="FMm_to_B0")
fmm2b0.inputs.transforms = ['Rigid'] * 2
fmm2b0.inputs.transform_parameters = [(1.0, )] * 2
fmm2b0.inputs.number_of_iterations = [[50], [20]]
fmm2b0.inputs.dimension = 3
fmm2b0.inputs.metric = ['Mattes', 'Mattes']
fmm2b0.inputs.metric_weight = [1.0] * 2
fmm2b0.inputs.radius_or_number_of_bins = [64, 64]
fmm2b0.inputs.sampling_strategy = ['Regular', 'Random']
fmm2b0.inputs.sampling_percentage = [None, 0.2]
fmm2b0.inputs.convergence_threshold = [1.e-5, 1.e-8]
fmm2b0.inputs.convergence_window_size = [20, 10]
fmm2b0.inputs.smoothing_sigmas = [[6.0], [2.0]]
fmm2b0.inputs.sigma_units = ['vox'] * 2
fmm2b0.inputs.shrink_factors = [[6], [1]] # ,[1] ]
fmm2b0.inputs.use_estimate_learning_rate_once = [True] * 2
fmm2b0.inputs.use_histogram_matching = [True] * 2
fmm2b0.inputs.initial_moving_transform_com = 0
fmm2b0.inputs.collapse_output_transforms = True
fmm2b0.inputs.winsorize_upper_quantile = 0.995
applyxfm = pe.Node(ants.ApplyTransforms(dimension=3, interpolation=interp),
name='FMp_to_B0')
pre_fugue = pe.Node(fsl.FUGUE(save_fmap=True), name='PreliminaryFugue')
demean = pe.Node(niu.Function(input_names=['in_file', 'in_mask'],
output_names=['out_file'],
function=demean_image),
name='DemeanFmap')
cleanup = cleanup_edge_pipeline()
addvol = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=add_empty_vol),
name='AddEmptyVol')
vsm = pe.Node(fsl.FUGUE(save_shift=True, **fugue_params),
name="ComputeVSM")
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
merge = pe.Node(fsl.Merge(dimension='t'), name='MergeDWIs')
unwarp = pe.MapNode(fsl.FUGUE(icorr=True, forward_warping=False),
iterfield=['in_file'],
name='UnwarpDWIs')
thres = pe.MapNode(fsl.Threshold(thresh=0.0),
iterfield=['in_file'],
name='RemoveNegative')
vsm2dfm = vsm2warp()
vsm2dfm.inputs.inputnode.scaling = 1.0
wf = pe.Workflow(name=name)
wf.connect([(inputnode, r_params, [('settings', 'in_file')]),
(r_params, eff_echo, [('echospacing', 'echospacing'),
('acc_factor', 'acc_factor')]),
(inputnode, pha2rads, [('bmap_pha', 'in_file')]),
(inputnode, firstmag, [('bmap_mag', 'in_file')]),
(inputnode, baseline, [('in_file', 'in_file'),
('in_ref', 'index')]),
(firstmag, n4, [('roi_file', 'input_image')]),
(n4, bet, [('output_image', 'in_file')]),
(bet, dilate, [('mask_file', 'in_file')]),
(pha2rads, prelude, [('out_file', 'phase_file')]),
(n4, prelude, [('output_image', 'magnitude_file')]),
(dilate, prelude, [('out_file', 'mask_file')]),
(r_params, rad2rsec, [('delta_te', 'delta_te')]),
(prelude, rad2rsec, [('unwrapped_phase_file', 'in_file')]),
(baseline, fmm2b0, [('out_file', 'fixed_image')]),
(n4, fmm2b0, [('output_image', 'moving_image')]),
(inputnode, fmm2b0, [('in_mask', 'fixed_image_mask')]),
(dilate, fmm2b0, [('out_file', 'moving_image_mask')]),
(baseline, applyxfm, [('out_file', 'reference_image')]),
(rad2rsec, applyxfm, [('out_file', 'input_image')]),
(fmm2b0, applyxfm, [('forward_transforms', 'transforms'),
('forward_invert_flags',
'invert_transform_flags')]),
(applyxfm, pre_fugue, [('output_image', 'fmap_in_file')]),
(inputnode, pre_fugue, [('in_mask', 'mask_file')]),
(pre_fugue, demean, [('fmap_out_file', 'in_file')]),
(inputnode, demean, [('in_mask', 'in_mask')]),
(demean, cleanup, [('out_file', 'inputnode.in_file')]),
(inputnode, cleanup, [('in_mask', 'inputnode.in_mask')]),
(cleanup, addvol, [('outputnode.out_file', 'in_file')]),
(inputnode, vsm, [('in_mask', 'mask_file')]),
(addvol, vsm, [('out_file', 'fmap_in_file')]),
(r_params, vsm, [('delta_te', 'asym_se_time')]),
(eff_echo, vsm, [('eff_echo', 'dwell_time')]),
(inputnode, split, [('in_file', 'in_file')]),
(split, unwarp, [('out_files', 'in_file')]),
(vsm, unwarp, [('shift_out_file', 'shift_in_file')]),
(r_params, unwarp, [(('enc_dir', _fix_enc_dir),
'unwarp_direction')]),
(unwarp, thres, [('unwarped_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(r_params, vsm2dfm, [(('enc_dir',
_fix_enc_dir), 'inputnode.enc_dir')]),
(merge, vsm2dfm, [('merged_file', 'inputnode.in_ref')]),
(vsm, vsm2dfm, [('shift_out_file', 'inputnode.in_vsm')]),
(merge, outputnode, [('merged_file', 'out_file')]),
(vsm, outputnode, [('shift_out_file', 'out_vsm')]),
(vsm2dfm, outputnode, [('outputnode.out_warp', 'out_warp')])])
return wf
