def init_prepare_epi_wf(ants_nthreads, name="prepare_epi_wf"):
"""
This workflow takes in a set of EPI files with with the same phase
encoding direction and returns a single 3D volume ready to be used in
field distortion estimation.
The procedure involves: estimating a robust template using FreeSurfer's
'mri_robust_template', bias field correction using ANTs N4BiasFieldCorrection
and AFNI 3dUnifize, skullstripping using FSL BET and AFNI 3dAutomask,
and rigid coregistration to the reference using ANTs.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_prepare_epi_wf
wf = init_prepare_epi_wf(ants_nthreads=8)
Inputs
fmaps
list of 3D or 4D NIfTI images
ref_brain
coregistration reference (skullstripped and bias field corrected)
Outputs
out_file
single 3D NIfTI file
"""
inputnode = pe.Node(niu.IdentityInterface(fields=['fmaps', 'ref_brain']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
split = pe.MapNode(fsl.Split(dimension='t'),
iterfield='in_file',
name='split')
merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1,
fixed_timepoint=True, # Align to first image
intensity_scaling=True,
# 7-DOF (rigid + intensity)
no_iteration=True,
subsample_threshold=200,
out_file='template.nii.gz'),
name='merge')
enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()
ants_settings = pkgr.resource_filename('fmriprep',
'data/translation_rigid.json')
fmap2ref_reg = pe.Node(ants.Registration(from_file=ants_settings,
output_warped_image=True,
num_threads=ants_nthreads),
name='fmap2ref_reg')
fmap2ref_reg.interface.num_threads = ants_nthreads
workflow = pe.Workflow(name=name)
def _flatten(l):
return [item for sublist in l for item in sublist]
workflow.connect([
(inputnode, split, [('fmaps', 'in_file')]),
(split, merge, [(('out_files', _flatten), 'in_files')]),
(merge, enhance_and_skullstrip_epi_wf, [('out_file',
'inputnode.in_file')]),
(enhance_and_skullstrip_epi_wf, fmap2ref_reg,
[('outputnode.skull_stripped_file', 'moving_image')]),
(inputnode, fmap2ref_reg, [('ref_brain', 'fixed_image')]),
(fmap2ref_reg, outputnode, [('warped_image', 'out_file')]),
])
return workflow
def init_bold_hmc_wf(bold_file_size_gb, omp_nthreads, name='bold_hmc_wf'):
"""
This workflow performs :abbr:`HMC (head motion correction)` over the input
:abbr:`BOLD (blood-oxygen-level dependent)` image.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_hmc_wf
wf = init_bold_hmc_wf(bold_file_size_gb=3, omp_nthreads=1)
**Parameters**
bold_file_size_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_hmc_wf``)
**Inputs**
bold_file
BOLD series NIfTI file
raw_ref_image
Reference image to which BOLD series is motion corrected
**Outputs**
bold_split
Individual 3D volumes, not motion corrected
xforms
ITKTransform file aligning each volume to ``ref_image``
movpar_file
MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['bold_split', 'xforms', 'movpar_file']),
name='outputnode')
# Head motion correction (hmc)
mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
name='mcflirt',
mem_gb=bold_file_size_gb * 3)
fsl2itk = pe.Node(MCFLIRT2ITK(),
name='fsl2itk',
mem_gb=0.05,
n_procs=omp_nthreads)
normalize_motion = pe.Node(NormalizeMotionParams(format='FSL'),
name="normalize_motion",
mem_gb=DEFAULT_MEMORY_MIN_GB)
split = pe.Node(fsl.Split(dimension='t'),
name='split',
mem_gb=bold_file_size_gb * 3)
workflow.connect([
(inputnode, split, [('bold_file', 'in_file')]),
(inputnode, mcflirt, [('raw_ref_image', 'ref_file'),
('bold_file', 'in_file')]),
(inputnode, fsl2itk, [('raw_ref_image', 'in_source'),
('raw_ref_image', 'in_reference')]),
(mcflirt, fsl2itk, [('mat_file', 'in_files')]),
(mcflirt, normalize_motion, [('par_file', 'in_file')]),
(fsl2itk, outputnode, [('out_file', 'xforms')]),
(normalize_motion, outputnode, [('out_file', 'movpar_file')]),
(split, outputnode, [('out_files', 'bold_split')]),
])
return workflow
