def head_motion_correction(name='motion_correction'):
workflow = Workflow(name)
input_node = Node(
niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']),
name='input')
output_node = Node(niu.IdentityInterface(fields=['xforms', 'movpar_file']),
name='outputnode')
mcflirt = Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
name='mcflirt')
fsl2itk = Node(MCFLIRT2ITK(), name='fsl2itk')
normalize_motion = Node(NormalizeMotionParams(format='FSL'),
name="normalize_motion")
workflow.connect([
(input_node, mcflirt, [('raw_ref_image', 'ref_file'),
('bold_file', 'in_file')]),
(input_node, 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, output_node, [('out_file', 'xforms')]),
(normalize_motion, output_node, [('out_file', 'movpar_file')]),
])
return workflow
def init_bold_hmc_wf(mem_gb, omp_nthreads, name='bold_hmc_wf'):
"""
This workflow estimates the motion parameters to perform
: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(
mem_gb=3,
omp_nthreads=1)
**Parameters**
mem_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**
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=['xforms', 'movpar_file']),
name='outputnode')
# Head motion correction (hmc)
mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
name='mcflirt',
mem_gb=mem_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)
workflow.connect([
(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')]),
])
return workflow
def init_bold_hmc_wf(mem_gb, omp_nthreads, name='bold_hmc_wf'):
"""
Build a workflow to estimate head-motion parameters.
This workflow estimates the motion parameters to perform
:abbr:`HMC (head motion correction)` over the input
:abbr:`BOLD (blood-oxygen-level dependent)` image.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_hmc_wf
wf = init_bold_hmc_wf(
mem_gb=3,
omp_nthreads=1)
Parameters
----------
mem_gb : :obj:`float`
Size of BOLD file in GB
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
name : :obj:`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
-------
xforms
ITKTransform file aligning each volume to ``ref_image``
movpar_file
MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces import NormalizeMotionParams
from niworkflows.interfaces.itk import MCFLIRT2ITK
workflow = Workflow(name=name)
workflow.__desc__ = """\
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL {fsl_ver}, @mcflirt].
""".format(fsl_ver=fsl.Info().version() or '<ver>')
inputnode = pe.Node(
niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['xforms', 'movpar_file']),
name='outputnode')
# Head motion correction (hmc)
mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
name='mcflirt',
mem_gb=mem_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)
workflow.connect([
(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')]),
])
return workflow