#!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Direct B0 mapping sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~
When the fieldmap is directly measured with a prescribed sequence (such as
:abbr:`SE (spiral echo)`), we only need to calculate the corresponding B-Spline
coefficients to adapt the fieldmap to the TOPUP tool.
This procedure is described with more detail `here <https://cni.stanford.edu/\
wiki/GE_Processing#Fieldmaps>`_.
This corresponds to the section 8.9.3 --fieldmap image (and one magnitude image)--
of the BIDS specification.
"""
from __future__ import print_function, division, absolute_import, unicode_literals
from niworkflows.nipype.pipeline import engine as pe
from niworkflows.nipype.interfaces import utility as niu
from niworkflows.nipype.interfaces import fsl, ants
from niworkflows.interfaces.masks import BETRPT
# Note that deman_image imports from nipype
from niworkflows.nipype.workflows.dmri.fsl.utils import demean_image, cleanup_edge_pipeline
from fmriprep.interfaces import IntraModalMerge
from fmriprep.interfaces.bids import DerivativesDataSink
from fmriprep.interfaces.fmap import FieldEnhance
from fmriprep.interfaces.utils import ApplyMask
[docs]def init_fmap_wf(reportlets_dir, omp_nthreads, fmap_bspline, name='fmap_wf'):
"""
Fieldmap workflow - when we have a sequence that directly measures the fieldmap
we just need to mask it (using the corresponding magnitude image) to remove the
noise in the surrounding air region, and ensure that units are Hz.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.fmap import init_fmap_wf
wf = init_fmap_wf(reportlets_dir='.', omp_nthreads=6,
fmap_bspline=False)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['magnitude', 'fieldmap']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# Merge input fieldmap images
fmapmrg = pe.Node(IntraModalMerge(zero_based_avg=False, hmc=False),
name='fmapmrg')
# de-gradient the fields ("bias/illumination artifact")
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
name='n4_correct')
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_fmap_mask = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_mask'), name='ds_fmap_mask')
workflow.connect([
(inputnode, magmrg, [('magnitude', 'in_files')]),
(inputnode, fmapmrg, [('fieldmap', 'in_files')]),
(magmrg, n4_correct, [('out_file', 'input_image')]),
(n4_correct, bet, [('output_image', 'in_file')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(inputnode, ds_fmap_mask, [('fieldmap', 'source_file')]),
(bet, ds_fmap_mask, [('out_report', 'in_file')]),
])
if fmap_bspline:
# despike_threshold=1.0, mask_erode=1),
fmapenh = pe.Node(FieldEnhance(
unwrap=False, despike=False, njobs=omp_nthreads),
name='fmapenh')
fmapenh.interface.num_threads = omp_nthreads
fmapenh.interface.estimated_memory_gb = 4
workflow.connect([
(bet, fmapenh, [('mask_file', 'in_mask'),
('out_file', 'in_magnitude')]),
(fmapmrg, fmapenh, [('out_file', 'in_file')]),
(fmapenh, outputnode, [('out_file', 'fmap')]),
])
else:
torads = pe.Node(niu.Function(output_names=['out_file', 'cutoff_hz'],
function=_torads), name='torads')
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
tohz = pe.Node(niu.Function(function=_tohz), name='tohz')
denoise = pe.Node(fsl.SpatialFilter(operation='median', kernel_shape='sphere',
kernel_size=3), name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name='cleanup_wf')
applymsk = pe.Node(ApplyMask(), name='applymsk')
workflow.connect([
(bet, prelude, [('mask_file', 'mask_file'),
('out_file', 'magnitude_file')]),
(fmapmrg, torads, [('out_file', 'in_file')]),
(torads, tohz, [('cutoff_hz', 'cutoff_hz')]),
(torads, prelude, [('out_file', 'phase_file')]),
(prelude, tohz, [('unwrapped_phase_file', 'in_file')]),
(tohz, denoise, [('out', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, applymsk, [('outputnode.out_file', 'in_file')]),
(bet, applymsk, [('mask_file', 'in_mask')]),
(applymsk, outputnode, [('out_file', 'fmap')]),
])
return workflow
def _torads(in_file, out_file=None):
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='rad')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
cutoff = max(abs(fmapdata.min()), fmapdata.max())
fmapdata = fmapdata * (pi / cutoff)
out_img = nb.Nifti1Image(fmapdata, fmapnii.affine, fmapnii.header)
out_img.set_data_dtype('float32')
out_img.to_filename(out_file)
return out_file, cutoff
def _tohz(in_file, cutoff_hz, out_file=None):
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='hz')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
fmapdata = fmapdata * (cutoff_hz / pi)
out_img = nb.Nifti1Image(fmapdata, fmapnii.affine, fmapnii.header)
out_img.set_data_dtype('float32')
out_img.to_filename(out_file)
return out_file