def test_WarpTimeSeriesImageMultiTransform_outputs():
output_map = dict(output_image=dict(), )
outputs = WarpTimeSeriesImageMultiTransform.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_WarpTimeSeriesImageMultiTransform_outputs():
output_map = dict(output_image=dict(),
)
outputs = WarpTimeSeriesImageMultiTransform.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_WarpTimeSeriesImageMultiTransform_inputs():
input_map = dict(
args=dict(argstr='%s', ),
dimension=dict(
argstr='%d',
position=1,
usedefault=True,
),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
input_image=dict(
argstr='%s',
copyfile=True,
mandatory=True,
),
invert_affine=dict(),
num_threads=dict(
nohash=True,
usedefault=True,
),
out_postfix=dict(
argstr='%s',
usedefault=True,
),
reference_image=dict(
argstr='-R %s',
xor=['tightest_box'],
),
reslice_by_header=dict(argstr='--reslice-by-header', ),
terminal_output=dict(
mandatory=True,
nohash=True,
),
tightest_box=dict(
argstr='--tightest-bounding-box',
xor=['reference_image'],
),
transformation_series=dict(
argstr='%s',
copyfile=False,
mandatory=True,
),
use_bspline=dict(argstr='--use-Bspline', ),
use_nearest=dict(argstr='--use-NN', ),
)
inputs = WarpTimeSeriesImageMultiTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_WarpTimeSeriesImageMultiTransform_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
num_threads=dict(nohash=True,
usedefault=True,
),
invert_affine=dict(),
reslice_by_header=dict(argstr='--reslice-by-header',
),
tightest_box=dict(xor=['reference_image'],
argstr='--tightest-bounding-box',
),
out_postfix=dict(argstr='%s',
usedefault=True,
),
use_nearest=dict(argstr='--use-NN',
),
args=dict(argstr='%s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
environ=dict(nohash=True,
usedefault=True,
),
reference_image=dict(xor=['tightest_box'],
argstr='-R %s',
),
input_image=dict(copyfile=True,
mandatory=True,
argstr='%s',
),
use_bspline=dict(argstr='--use-Bspline',
),
transformation_series=dict(copyfile=False,
mandatory=True,
argstr='%s',
),
dimension=dict(position=1,
usedefault=True,
argstr='%d',
),
)
inputs = WarpTimeSeriesImageMultiTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def create_t1_based_unwarp(name='unwarp'):
"""
Unwarp an fMRI time series based on non-linear registration to T1.
NOTE: AS IT STANDS THIS METHOD DID NOT PRODUCE ACCEPTABLE RESULTS
IF BRAIN COVERAGE IS NOT COMPLETE ON THE EPI IMAGE.
ALSO: NEED TO ADD AUTOMATIC READING OF EPI RESOLUTION TO GET
"""
unwarpflow = pe.Workflow(name=name)
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['epi', 'T1W']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['unwarped_func', 'warp_files']),
name='outputspec')
tmedian = pe.Node(interface=ImageMaths(), name='tmedian')
tmedian.inputs.op_string = '-Tmedian'
epi_brain_ext = pe.Node(interface=util.Function(
function=epi_brain_extract,
input_names=['in_file'],
output_names=['out_vol', 'out_mask']),
name='epi_brain_ext')
fast_debias = pe.Node(interface=FAST(), name='FAST_debias')
fast_debias.inputs.output_biascorrected = True
robex = pe.Node(interface=util.Function(
function=my_robex,
input_names=['in_file'],
output_names=['out_file', 'out_mask']),
name='robex')
downsample_T1 = pe.Node(MRIConvert(), name='downsample_dti')
downsample_T1.inputs.vox_size = (3.438, 3.438, 3.000)
downsample_T1.inputs.out_type = 'niigz'
contrast_invert = pe.Node(interface=util.Function(
function=invert_contrast,
input_names=['in_t1_brain', 'in_b0_brain'],
output_names=['out_fn']),
name='contrast_invert')
ants_syn = pe.Node(interface=util.Function(
function=my_ants_registration_syn,
input_names=['in_T1W', 'in_epi'],
output_names=['out_transforms']),
name='ants_syn')
ants_warp = pe.Node(interface=WarpTimeSeriesImageMultiTransform(),
name='ants_warp')
'''connections'''
# unwarpflow.connect(inputnode, 'T1W', robex, 'in_file')
unwarpflow.connect(inputnode, 'T1W', fast_debias, 'in_files')
# unwarpflow.connect(robex, 'out_file', fast_debias, 'in_files')
unwarpflow.connect(fast_debias, 'restored_image', robex, 'in_file')
# unwarpflow.connect(fast_debias, 'restored_image', downsample_T1, 'in_file')
unwarpflow.connect(robex, 'out_file', downsample_T1, 'in_file')
unwarpflow.connect(downsample_T1, 'out_file', contrast_invert,
'in_t1_brain')
unwarpflow.connect(inputnode, 'epi', tmedian, 'in_file')
unwarpflow.connect(tmedian, 'out_file', epi_brain_ext, 'in_file')
unwarpflow.connect(epi_brain_ext, 'out_vol', contrast_invert,
'in_b0_brain')
unwarpflow.connect(contrast_invert, 'out_fn', ants_syn, 'in_T1W')
unwarpflow.connect(epi_brain_ext, 'out_vol', ants_syn, 'in_epi')
unwarpflow.connect(ants_syn, 'out_transforms', outputnode,
'out_transforms')
unwarpflow.connect(inputnode, 'epi', ants_warp, 'input_image')
unwarpflow.connect(contrast_invert, 'out_fn', ants_warp, 'reference_image')
unwarpflow.connect(ants_syn, 'out_transforms', ants_warp,
'transformation_series')
unwarpflow.connect(ants_syn, 'out_transforms', outputnode, 'warp_files')
unwarpflow.connect(ants_warp, 'output_image', outputnode, 'unwarped_func')
return unwarpflow