def _run_interface(self, runtime):
in_files = self.inputs.in_files
if not isinstance(in_files, list):
in_files = [self.inputs.in_files]
# Generate output average name early
self._results['out_avg'] = genfname(self.inputs.in_files[0],
suffix='avg')
if self.inputs.to_ras:
in_files = [reorient(inf) for inf in in_files]
if len(in_files) == 1:
filenii = nb.load(in_files[0])
filedata = filenii.get_data()
# magnitude files can have an extra dimension empty
if filedata.ndim == 5:
sqdata = np.squeeze(filedata)
if sqdata.ndim == 5:
raise RuntimeError('Input image (%s) is 5D' % in_files[0])
else:
in_files = [genfname(in_files[0], suffix='squeezed')]
nb.Nifti1Image(sqdata, filenii.get_affine(),
filenii.get_header()).to_filename(
in_files[0])
if np.squeeze(nb.load(in_files[0]).get_data()).ndim < 4:
self._results['out_file'] = in_files[0]
self._results['out_avg'] = in_files[0]
# TODO: generate identity out_mats and zero-filled out_movpar
return runtime
in_files = in_files[0]
else:
magmrg = fsl.Merge(dimension='t', in_files=self.inputs.in_files)
in_files = magmrg.run().outputs.merged_file
mcflirt = fsl.MCFLIRT(cost='normcorr',
save_mats=True,
save_plots=True,
ref_vol=0,
in_file=in_files)
mcres = mcflirt.run()
self._results['out_mats'] = mcres.outputs.mat_file
self._results['out_movpar'] = mcres.outputs.par_file
self._results['out_file'] = mcres.outputs.out_file
hmcnii = nb.load(mcres.outputs.out_file)
hmcdat = hmcnii.get_data().mean(axis=3)
if self.inputs.zero_based_avg:
hmcdat -= hmcdat.min()
nb.Nifti1Image(hmcdat, hmcnii.get_affine(),
hmcnii.get_header()).to_filename(
self._results['out_avg'])
return runtime
def hmc_mcflirt(settings, name='fMRI_HMC_mcflirt'):
"""
An :abbr:`HMC (head motion correction)` for functional scans
using FSL MCFLIRT
.. workflow::
from mriqc.workflows.functional import hmc_mcflirt
wf = hmc_mcflirt({'biggest_file_size_gb': 1})
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_file', 'fd_radius', 'start_idx', 'stop_idx']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file', 'out_fd']),
name='outputnode')
gen_ref = pe.Node(nwr.EstimateReferenceImage(mc_method="AFNI"),
name="gen_ref")
mcflirt = pe.Node(fsl.MCFLIRT(save_plots=True, interpolation='sinc'),
name='MCFLIRT',
mem_gb=settings['biggest_file_size_gb'] * 2.5)
fdnode = pe.Node(nac.FramewiseDisplacement(normalize=False,
parameter_source="FSL"),
name='ComputeFD')
workflow.connect([
(inputnode, gen_ref, [('in_file', 'in_file')]),
(gen_ref, mcflirt, [('ref_image', 'ref_file')]),
(inputnode, mcflirt, [('in_file', 'in_file')]),
(inputnode, fdnode, [('fd_radius', 'radius')]),
(mcflirt, fdnode, [('par_file', 'in_file')]),
(mcflirt, outputnode, [('out_file', 'out_file')]),
(fdnode, outputnode, [('out_file', 'out_fd')]),
])
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
