def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_dir = dicom_dir
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(op.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
return eg, prov_file
def nipype_convert(item_dicoms,
prefix,
with_prov,
bids_options,
tmpdir,
dcmconfig=None):
"""
Converts DICOMs grouped from heuristic using Nipype's Dcm2niix interface.
Parameters
----------
item_dicoms : List
DICOM files to convert
prefix : String
Heuristic output path
with_prov : Bool
Store provenance information
bids_options : List or None
If not None then output BIDS sidecar JSONs
List may contain bids specific options
tmpdir : Directory
Conversion working directory
dcmconfig : File (optional)
JSON file used for additional Dcm2niix configuration
"""
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
fromfile = dcmconfig if dcmconfig else None
if fromfile:
lgr.info("Using custom config file %s", fromfile)
convertnode = Node(Dcm2niix(from_file=fromfile), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = prefix
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids_options is not None
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def create_fs_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs:
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.target_image : registration target
Outputs:
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
Example
-------
See code below
"""
register = Workflow(name=name)
inputnode = Node(
interface=IdentityInterface(fields=[
'source_files', 'mean_image', 'subject_id', 'subjects_dir',
'target_image'
]),
name='inputspec')
outputnode = Node(
interface=IdentityInterface(fields=[
'func2anat_transform', 'out_reg_file', 'anat2target_transform',
'transforms', 'transformed_mean', 'transformed_files',
'min_cost_file', 'anat2target', 'aparc', 'mean2anat_mask'
]),
name='outputspec')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(), name='fssource')
fssource.run_without_submitting = True
register.connect(inputnode, 'subject_id', fssource, 'subject_id')
register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
convert = Node(freesurfer.MRIConvert(out_type='nii'), name="convert")
register.connect(fssource, 'T1', convert, 'in_file')
# Coregister the median to the surface
bbregister = Node(
freesurfer.BBRegister(registered_file=True), name='bbregister')
bbregister.inputs.init = 'fsl'
bbregister.inputs.contrast_type = 't2'
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
register.connect(inputnode, 'mean_image', bbregister, 'source_file')
register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
# Create a mask of the median coregistered to the anatomical image
mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
register.connect(bbregister, 'registered_file', mean2anat_mask, 'in_file')
"""
use aparc+aseg's brain mask
"""
binarize = Node(
fs.Binarize(min=0.5, out_type="nii.gz", dilate=1),
name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize,
"in_file")
stripper = Node(fsl.ApplyMask(), name='stripper')
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, 'out_file', stripper, 'in_file')
"""
Apply inverse transform to aparc file
"""
aparcxfm = Node(
freesurfer.ApplyVolTransform(inverse=True, interp='nearest'),
name='aparc_inverse_transform')
register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
register.connect(fssource, ('aparc_aseg', get_aparc_aseg), aparcxfm,
'target_file')
register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')
"""
Convert the BBRegister transformation to ANTS ITK format
"""
convert2itk = Node(C3dAffineTool(), name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
"""
Compute registration between the subject's structural and MNI template
* All parameters are set using the example from:
#https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
* This is currently set to perform a very quick registration. However,
the registration can be made significantly more accurate for cortical
structures by increasing the number of iterations.
"""
reg = Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[
100, 30, 20
]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.float = True
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {
'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'
}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image', reg, 'fixed_image')
"""
Concatenate the affine and ants transforms into a list
"""
merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
"""
Transform the mean image. First to anatomical and then to target
"""
warpmean = Node(ants.ApplyTransforms(), name='warpmean')
warpmean.inputs.input_image_type = 0
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.terminal_output = 'file'
warpmean.inputs.args = '--float'
# warpmean.inputs.num_threads = 4
# warpmean.plugin_args = {'sbatch_args': '--mem=4G -c 4'}
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = pe.MapNode(
ants.ApplyTransforms(), iterfield=['input_image'], name='warpall')
warpall.inputs.input_image_type = 0
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.terminal_output = 'file'
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 2
warpall.plugin_args = {'sbatch_args': '--mem=6G -c 2'}
"""
Assign all the output files
"""
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(warpall, 'output_image', outputnode, 'transformed_files')
register.connect(inputnode, 'target_image', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
register.connect(inputnode, 'target_image', warpall, 'reference_image')
register.connect(inputnode, 'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')
"""
Assign all the output files
"""
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(aparcxfm, 'transformed_file', outputnode, 'aparc')
register.connect(bbregister, 'out_fsl_file', outputnode,
'func2anat_transform')
register.connect(bbregister, 'out_reg_file', outputnode, 'out_reg_file')
register.connect(bbregister, 'min_cost_file', outputnode, 'min_cost_file')
register.connect(mean2anat_mask, 'mask_file', outputnode, 'mean2anat_mask')
register.connect(reg, 'composite_transform', outputnode,
'anat2target_transform')
register.connect(merge, 'out', outputnode, 'transforms')
return register
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = Workflow(name=name)
inputnode = Node(interface=IdentityInterface(fields=['source_files',
'mean_image',
'subject_id',
'subjects_dir',
'target_image']),
name='inputspec')
outputnode = Node(interface=IdentityInterface(fields=['func2anat_transform',
'out_reg_file',
'anat2target_transform',
'transforms',
'transformed_mean',
'segmentation_files',
'anat2target',
'aparc',
'min_cost_file'
]),
name='outputspec')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(),
name='fssource')
fssource.run_without_submitting = True
register.connect(inputnode, 'subject_id', fssource, 'subject_id')
register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
convert = Node(freesurfer.MRIConvert(out_type='nii'),
name="convert")
register.connect(fssource, 'T1', convert, 'in_file')
# Coregister the median to the surface
bbregister = Node(freesurfer.BBRegister(),
name='bbregister')
bbregister.inputs.init = 'fsl'
bbregister.inputs.contrast_type = 't2'
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
register.connect(inputnode, 'mean_image', bbregister, 'source_file')
register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
"""
Estimate the tissue classes from the anatomical image. But use aparc+aseg's brain mask
"""
binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
stripper = Node(fsl.ApplyMask(), name='stripper')
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, 'out_file', stripper, 'in_file')
fast = Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = MapNode(fsl.ImageMaths(op_string='-nan -thr 0.9 -ero -bin'),
iterfield=['in_file'],
name='binarize')
register.connect(fast, 'partial_volume_files', binarize, 'in_file')
"""
Apply inverse transform to take segmentations to functional space
"""
applyxfm = MapNode(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
iterfield=['target_file'],
name='inverse_transform')
register.connect(inputnode, 'subjects_dir', applyxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', applyxfm, 'reg_file')
register.connect(binarize, 'out_file', applyxfm, 'target_file')
register.connect(inputnode, 'mean_image', applyxfm, 'source_file')
"""
Apply inverse transform to aparc file
"""
aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
name='aparc_inverse_transform')
register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
register.connect(fssource, ('aparc_aseg', get_aparc_aseg),
aparcxfm, 'target_file')
register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')
"""
Convert the BBRegister transformation to ANTS ITK format
"""
convert2itk = Node(C3dAffineTool(), name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
"""
Compute registration between the subject's structural and MNI template
This is currently set to perform a very quick registration. However, the
registration can be made significantly more accurate for cortical
structures by increasing the number of iterations
All parameters are set using the example from:
#https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
"""
reg = Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.float = True
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {'sbatch_args': '-c%d' % 4}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image', reg, 'fixed_image')
"""
Concatenate the affine and ants transforms into a list
"""
merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
"""
Transform the mean image. First to anatomical and then to target
"""
warpmean = Node(ants.ApplyTransforms(), name='warpmean')
warpmean.inputs.input_image_type = 3
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.terminal_output = 'file'
warpmean.inputs.args = '--float'
warpmean.inputs.num_threads = 4
warpmean.plugin_args = {'sbatch_args': '-c%d' % 4}
register.connect(inputnode, 'target_image', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
"""
Assign all the output files
"""
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(applyxfm, 'transformed_file',
outputnode, 'segmentation_files')
register.connect(aparcxfm, 'transformed_file',
outputnode, 'aparc')
register.connect(bbregister, 'out_fsl_file',
outputnode, 'func2anat_transform')
register.connect(bbregister, 'out_reg_file',
outputnode, 'out_reg_file')
register.connect(reg, 'composite_transform',
outputnode, 'anat2target_transform')
register.connect(merge, 'out', outputnode, 'transforms')
register.connect(bbregister, 'min_cost_file',
outputnode, 'min_cost_file')
return register
