def test_bet():
tmp_infile, tp_dir = setup_infile()
better = fsl.BET()
yield assert_equal, better.cmd, 'bet'
# Test raising error with mandatory args absent
yield assert_raises, ValueError, better.run
# Test generated outfile name
better.inputs.in_file = tmp_infile
outfile = fsl_name(better, 'foo_brain')
outpath = os.path.join(os.getcwd(), outfile)
realcmd = 'bet %s %s' % (tmp_infile, outpath)
yield assert_equal, better.cmdline, realcmd
# Test specified outfile name
outfile = fsl_name(better, '/newdata/bar')
better.inputs.out_file = outfile
realcmd = 'bet %s %s' % (tmp_infile, outfile)
yield assert_equal, better.cmdline, realcmd
# infile foo.nii doesn't exist
def func():
better.run(in_file='foo2.nii', out_file='bar.nii')
yield assert_raises, TraitError, func
# Our options and some test values for them
# Should parallel the opt_map structure in the class for clarity
opt_map = {
'outline': ('-o', True),
'mask': ('-m', True),
'skull': ('-s', True),
'no_output': ('-n', True),
'frac': ('-f 0.40', 0.4),
'vertical_gradient': ('-g 0.75', 0.75),
'radius': ('-r 20', 20),
'center': ('-c 54 75 80', [54, 75, 80]),
'threshold': ('-t', True),
'mesh': ('-e', True),
'surfaces': ('-A', True)
# 'verbose': ('-v', True),
# 'flags': ('--i-made-this-up', '--i-made-this-up'),
}
# Currently we don't test -R, -S, -B, -Z, -F, -A or -A2
# test each of our arguments
better = fsl.BET()
outfile = fsl_name(better, 'foo_brain')
outpath = os.path.join(os.getcwd(), outfile)
for name, settings in list(opt_map.items()):
better = fsl.BET(**{name: settings[1]})
# Add mandatory input
better.inputs.in_file = tmp_infile
realcmd = ' '.join([better.cmd, tmp_infile, outpath, settings[0]])
yield assert_equal, better.cmdline, realcmd
teardown_infile(tmp_dir)
transforms=['Rigid'],
transform_parameters=[(0.1, )],
metric=['MI'],
metric_weight=[1.0],
number_of_iterations=[[1000]],
convergence_threshold=[1.e-7],
convergence_window_size=[10],
shrink_factors=[[4]],
smoothing_sigmas=[[2.]],
output_warped_image=True,
output_transform_prefix='T1w_to_FLAIR'),
name='ants_reg')
# BET: skullstrip T1
bet_t1 = ni_engine.Node(ni_fsl_preproc.BET(frac=0.2,
robust=True,
output_type='NIFTI_GZ',
mask=True),
name="bet_t1")
ants_warp_lesion = ni_engine.Node(ni_ants.ApplyTransforms(
dimension=3,
interpolation='NearestNeighbor',
invert_transform_flags=[True],
default_value=0),
name='ants_warp_lesion')
ants_warp_flair = ni_engine.Node(ni_ants.ApplyTransforms(
dimension=3,
interpolation='NearestNeighbor',
invert_transform_flags=[True],
default_value=0),
def create_B0_workflow(name='b0_unwarping', scanner='philips'):
""" Does B0 field unwarping
Example
-------
>>> nipype_epicorrect = create_unwarping_workflow('unwarp',)
>>> unwarp.inputs.input_node.in_file = 'subj1_run1_bold.nii.gz'
>>> unwarp.inputs.input_node.fieldmap_mag = 'subj1_run1_mag.nii.gz'
>>> unwarp.inputs.input_node.fieldmap_pha = 'subj1_run1_phas.nii.gz'
>>> unwarp.inputs.input_node.wfs = 12.223
>>> unwarp.inputs.input_node.epi_factor = 35.0
>>> unwarp.inputs.input_node.acceleration = 3.0
>>> unwarp.inputs.input_node.te_diff = 0.005
>>> unwarp.inputs.input_node.phase_encoding_direction = 'y'
>>> nipype_epicorrect.run()
Inputs::
input_node.in_file - Volume acquired with EPI sequence
input_node.fieldmap_mag - Magnitude of the fieldmap
input_node.fieldmap_pha - Phase difference of the fieldmap
input_node.wfs - Water-fat-shift in pixels
input_node.epi_factor - EPI factor
input_node.acceleration - Acceleration factor used for EPI parallel imaging (SENSE)
input_node.te_diff - Time difference between TE in seconds.
input_node.phase_encoding_direction - Unwarp direction (default should be "y")
Outputs::
outputnode.epi_corrected
"""
# Nodes:
# ------
# Define input and workflow:
input_node = pe.Node(name='inputspec',
interface=IdentityInterface(fields=[
'in_files', 'fieldmap_mag', 'fieldmap_pha', 'wfs',
'epi_factor', 'acceleration', 'echo_spacing',
'te_diff', 'phase_encoding_direction'
]))
# Normalize phase difference of the fieldmap phase to be [-pi, pi)
norm_pha = pe.Node(interface=Prepare_phasediff, name='normalize_phasediff')
# Mask the magnitude of the fieldmap
mask_mag = pe.Node(fsl.BET(mask=True), name='mask_magnitude')
mask_mag_dil = pe.Node(interface=Dilate_mask, name='mask_dilate')
# Unwrap fieldmap phase using FSL PRELUDE
prelude = pe.Node(fsl.PRELUDE(process3d=True), name='phase_unwrap')
# Convert unwrapped fieldmap phase to radials per second:
radials_per_second = pe.Node(interface=Radials_per_second,
name='radials_ps')
# in case of SIEMENS scanner:
prepare_fieldmap = pe.Node(PrepareFieldmap(), name='prepare_fieldmap')
# Register unwrapped fieldmap (rad/s) to epi, using the magnitude of the fieldmap
registration = pe.MapNode(fsl.FLIRT(bins=256,
cost='corratio',
dof=6,
interp='trilinear',
searchr_x=[-10, 10],
searchr_y=[-10, 10],
searchr_z=[-10, 10]),
iterfield=['reference'],
name='registration')
# transform unwrapped fieldmap (rad/s)
applyxfm = pe.MapNode(fsl.ApplyXFM(interp='trilinear'),
iterfield=['reference', 'in_matrix_file'],
name='apply_xfm')
# compute effective echospacing:
echo_spacing_philips = pe.Node(interface=Compute_echo_spacing_philips,
name='echo_spacing_philips')
echo_spacing_siemens = pe.Node(interface=Compute_echo_spacing_siemens,
name='echo_spacing_siemens')
te_diff_in_ms = pe.Node(interface=TE_diff_ms, name='te_diff_in_ms')
# Unwarp with FSL Fugue
fugue = pe.MapNode(interface=fsl.FUGUE(median_2dfilter=True),
iterfield=['in_file', 'unwarped_file', 'fmap_in_file'],
name='fugue')
# Convert unwrapped fieldmap phase to radials per second:
out_file = pe.MapNode(interface=Make_output_filename,
iterfield=['in_file'],
name='out_file')
# Define output node
outputnode = pe.Node(
IdentityInterface(fields=['out_files', 'field_coefs']),
name='outputspec')
# Workflow:
# ---------
unwarp_workflow = pe.Workflow(name=name)
unwarp_workflow.connect(input_node, 'in_files', out_file, 'in_file')
# registration:
unwarp_workflow.connect(input_node, 'fieldmap_mag', mask_mag, 'in_file')
unwarp_workflow.connect(mask_mag, 'mask_file', mask_mag_dil, 'in_file')
unwarp_workflow.connect(mask_mag, 'out_file', registration, 'in_file')
unwarp_workflow.connect(input_node, 'in_files', registration, 'reference')
if scanner == 'philips':
# prepare fieldmap:
unwarp_workflow.connect(input_node, 'fieldmap_pha', norm_pha,
'in_file')
unwarp_workflow.connect(input_node, 'fieldmap_mag', prelude,
'magnitude_file')
unwarp_workflow.connect(norm_pha, 'out_file', prelude, 'phase_file')
unwarp_workflow.connect(mask_mag_dil, 'out_file', prelude, 'mask_file')
unwarp_workflow.connect(prelude, 'unwrapped_phase_file',
radials_per_second, 'in_file')
unwarp_workflow.connect(input_node, 'te_diff', radials_per_second,
'asym')
# transform fieldmap:
unwarp_workflow.connect(radials_per_second, 'out_file', applyxfm,
'in_file')
unwarp_workflow.connect(registration, 'out_matrix_file', applyxfm,
'in_matrix_file')
unwarp_workflow.connect(input_node, 'in_files', applyxfm, 'reference')
# compute echo spacing:
unwarp_workflow.connect(input_node, 'wfs', echo_spacing_philips, 'wfs')
unwarp_workflow.connect(input_node, 'epi_factor', echo_spacing_philips,
'epi_factor')
unwarp_workflow.connect(input_node, 'acceleration',
echo_spacing_philips, 'acceleration')
unwarp_workflow.connect(echo_spacing_philips, 'echo_spacing', fugue,
'dwell_time')
elif scanner == 'siemens':
unwarp_workflow.connect(input_node, 'te_diff', te_diff_in_ms,
'te_diff')
# prepare fieldmap:
unwarp_workflow.connect(mask_mag, 'out_file', prepare_fieldmap,
'in_magnitude')
unwarp_workflow.connect(input_node, 'fieldmap_pha', prepare_fieldmap,
'in_phase')
unwarp_workflow.connect(te_diff_in_ms, 'te_diff', prepare_fieldmap,
'delta_TE')
# transform fieldmap:
unwarp_workflow.connect(prepare_fieldmap, 'out_fieldmap', applyxfm,
'in_file')
unwarp_workflow.connect(registration, 'out_matrix_file', applyxfm,
'in_matrix_file')
unwarp_workflow.connect(input_node, 'in_files', applyxfm, 'reference')
# compute echo spacing:
unwarp_workflow.connect(input_node, 'acceleration',
echo_spacing_siemens, 'acceleration')
unwarp_workflow.connect(input_node, 'echo_spacing',
echo_spacing_siemens, 'echo_spacing')
unwarp_workflow.connect(echo_spacing_siemens, 'echo_spacing', fugue,
'dwell_time')
unwarp_workflow.connect(input_node, 'in_files', fugue, 'in_file')
unwarp_workflow.connect(out_file, 'out_file', fugue, 'unwarped_file')
unwarp_workflow.connect(applyxfm, 'out_file', fugue, 'fmap_in_file')
unwarp_workflow.connect(input_node, 'te_diff', fugue, 'asym_se_time')
unwarp_workflow.connect(input_node, 'phase_encoding_direction', fugue,
'unwarp_direction')
unwarp_workflow.connect(fugue, 'unwarped_file', outputnode, 'out_files')
unwarp_workflow.connect(applyxfm, 'out_file', outputnode, 'field_coefs')
# # Connect
# unwarp_workflow.connect(input_node, 'in_files', out_file, 'in_file')
# unwarp_workflow.connect(input_node, 'fieldmap_pha', norm_pha, 'in_file')
# unwarp_workflow.connect(input_node, 'fieldmap_mag', mask_mag, 'in_file')
# unwarp_workflow.connect(mask_mag, 'mask_file', mask_mag_dil, 'in_file')
# unwarp_workflow.connect(input_node, 'fieldmap_mag', prelude, 'magnitude_file')
# unwarp_workflow.connect(norm_pha, 'out_file', prelude, 'phase_file')
# unwarp_workflow.connect(mask_mag_dil, 'out_file', prelude, 'mask_file')
# unwarp_workflow.connect(prelude, 'unwrapped_phase_file', radials_per_second, 'in_file')
# unwarp_workflow.connect(input_node, 'te_diff', radials_per_second, 'asym')
# unwarp_workflow.connect(mask_mag, 'out_file', registration, 'in_file')
# unwarp_workflow.connect(input_node, 'in_files', registration, 'reference')
# unwarp_workflow.connect(radials_per_second, 'out_file', applyxfm, 'in_file')
# unwarp_workflow.connect(registration, 'out_matrix_file', applyxfm, 'in_matrix_file')
# unwarp_workflow.connect(input_node, 'in_files', applyxfm, 'reference')
# if compute_echo_spacing:
# unwarp_workflow.connect(input_node, 'wfs', echo_spacing, 'wfs')
# unwarp_workflow.connect(input_node, 'epi_factor', echo_spacing, 'epi_factor')
# unwarp_workflow.connect(input_node, 'acceleration', echo_spacing, 'acceleration')
# unwarp_workflow.connect(echo_spacing, 'echo_spacing', fugue, 'dwell_time')
# else:
# unwarp_workflow.connect(input_node, 'echo_spacing', fugue, 'dwell_time')
# unwarp_workflow.connect(input_node, 'in_files', fugue, 'in_file')
# unwarp_workflow.connect(out_file, 'out_file', fugue, 'unwarped_file')
# unwarp_workflow.connect(applyxfm, 'out_file', fugue, 'fmap_in_file')
# unwarp_workflow.connect(input_node, 'te_diff', fugue, 'asym_se_time')
# unwarp_workflow.connect(input_node, 'phase_encoding_direction', fugue, 'unwarp_direction')
# unwarp_workflow.connect(fugue, 'unwarped_file', outputnode, 'out_files')
# unwarp_workflow.connect(applyxfm, 'out_file', outputnode, 'field_coefs')
return unwarp_workflow
def VERBOSE_EXTRACTER():
#--- 1) Import modules
import nipype.interfaces.dcm2nii as dcm2nii
import nipype.pipeline.engine as pe
import matplotlib
import os
os.system('clear')
from glob import glob
from nilearn import plotting
from nilearn import image
from nipype.interfaces.utility import Function
import nipype.interfaces.fsl.preprocess as fsl
import nipype.interfaces.fsl.utils as fslu
import numpy
#--- 2) Record intial working directory
INITDIR=os.getcwd();
#--- 3) Prompt user for directory containing NIFTI FILES
NIFTIFILE=raw_input('Please drag in the \nnifti file you wish to extract\n(ensure there is no blank space at the end)\n')
os.system('clear')
print '---\n'
NIFTIFILE=NIFTIFILE.strip('\'"')
NIFTIDIR=os.path.split(NIFTIFILE)[0]
frac=list(numpy.linspace(start=0,stop=1,num=6))
grad=list(numpy.linspace(start=-1,stop=1,num=6))
#--- 3) Move to directory
os.chdir(NIFTIDIR)
#--- 4) Realign for good practice. Seems like BET should assume a default orientation.
realigner=pe.Node(interface=fslu.Reorient2Std(),name='REORIENTED')
realigner.inputs.in_file=NIFTIFILE
#--- 5) Set up extracter node
#The input parameters depend on the dimensions of the file, so check these using nilearn.
niftifiledim=len(image.load_img(NIFTIFILE).shape)
extracter=pe.Node(interface=fsl.BET(),name='EXTRACTED')
# Input can either be a list or float. Lists are iterables, floats are inputs.
if type(grad) == float and type(frac) == float:
extracter.inputs.frac=float(frac)
extracter.inputs.vertical_gradient=float(grad)
elif type(grad) == list and type(frac) == list:
extracter.iterables=([('frac',frac),('vertical_gradient',grad)])
elif type(grad) == float and type(frac) == list:
extracter.iterables=([('frac',frac)])
extracter.inputs.vertical_gradient=float(grad)
elif type(grad) == list and type(frac) == float:
extracter.iterables=([('vertical_gradient',grad)])
extracter.inputs.frac=float(frac)
if niftifiledim == 3:
extracter.inputs.functional=bool(0)
else:
extracter.inputs.functional=bool(1)
#--- 6) Set up a plotting node
# Overlay extraction on original file.
def bplot(in_file,in_file2):
from nilearn import image
from nilearn import plotting
import matplotlib
niftifiledim=len(image.load_img(in_file).shape)
if niftifiledim == 3:
display=plotting.plot_anat(in_file2)
display.add_contours(in_file,filled=True, alpha=0.5,levels=[0.2], colors='b')
matplotlib.pyplot.show()
else:
firstim=image.index_img(in_file, 0)
firstim2=image.index_img(in_file2, 0)
display=plotting.plot_anat(firstim2)
display.add_contours(firstim,filled=True, alpha=0.5,levels=[0.2], colors='b')
matplotlib.pyplot.show()
return niftifiledim
showextract= pe.Node(Function(input_names=['in_file','in_file2'],output_names=['niftifiledim'],function=bplot),name='SHOWEXTRACT')
#--- 7) Set up workflow
workflow = pe.Workflow(name='EXTRACTER')
workflow.base_dir = NIFTIDIR
#--- 8) Connect nodes.
workflow.connect(realigner,'out_file',extracter,'in_file')
workflow.connect(realigner,'out_file',showextract,'in_file2')
workflow.connect(extracter,'out_file',showextract,'in_file')
workflow.write_graph(graph2use='exec')
#--- 9) Run workflow
result=workflow.run()
#--- 10) Show plot
print "Node completed. Returning to intital directory\n"
os.chdir(INITDIR)