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Merged
merged 9 commits into from
Jul 17, 2014
Merged

Fix/fnirtflow #877

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1750e14
fix: test stripped brain image for FLIRT
satra Jul 15, 2014
9c4e9ad
fix: node variable name
satra Jul 15, 2014
0d72d3e
fix: fnirt doesn't require two stages and added config file option
satra Jul 15, 2014
272c74e
fix: warp zstat files to MNI
satra Jul 15, 2014
553a47d
fix: use splits properly
satra Jul 15, 2014
bc8358a
fix: remove warpres
satra Jul 15, 2014
351bb3b
fix: change datasink subs
satra Jul 15, 2014
bfdb615
fix: index of zstats
satra Jul 15, 2014
c2ef807
updated CHANGES
satra Jul 17, 2014
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1 change: 1 addition & 0 deletions CHANGES
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@ Next Release
* ENH: New data grabbing interface that works over SSH connections, SSHDataGrabber
* ENH: New color mode for write_graph
* FIX: MRTrix tracking algorithms were ignoring mask parameters.
* FIX: FNIRT registration pathway and associated OpenFMRI example script

Release 0.9.2 (January 31, 2014)
============
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29 changes: 20 additions & 9 deletions examples/fmri_openfmri.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,33 +305,41 @@ def num_copes(files):
wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'

def merge_files(copes, varcopes):
def merge_files(copes, varcopes, zstats):
out_files = []
splits = []
out_files.extend(copes)
splits.append(len(copes))
out_files.extend(varcopes)
splits.append(len(varcopes))
out_files.extend(zstats)
splits.append(len(zstats))
return out_files, splits

mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes'],
mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
'zstats'],
output_names=['out_files', 'splits'],
function=merge_files),
name='merge_files')
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
[('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
])])
wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')

def split_files(in_files, splits):
copes = in_files[:splits[1]]
varcopes = in_files[splits[1]:]
return copes, varcopes
copes = in_files[:splits[0]]
varcopes = in_files[splits[0]:(splits[0] + splits[1])]
zstats = in_files[(splits[0] + splits[1]):]
return copes, varcopes, zstats

splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
output_names=['copes', 'varcopes'],
output_names=['copes', 'varcopes',
'zstats'],
function=split_files),
name='split_files')
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
Expand All @@ -347,7 +355,7 @@ def get_subs(subject_id, conds, model_id, task_id):
subs = [('_subject_id_%s_' % subject_id, '')]
subs.append(('_model_id_%d' % model_id, 'model%03d' %model_id))
subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp_warp',
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
'mean'))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
'affine'))
Expand All @@ -358,10 +366,12 @@ def get_subs(subject_id, conds, model_id, task_id):
subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_warp_warp.' % i,
subs.append(('_warpall%d/cope1_warp.' % i,
'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_warp_warp.' % (len(conds) + i),
subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
return subs

subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds',
Expand All @@ -388,6 +398,7 @@ def get_subs(subject_id, conds, model_id, task_id):
wf.connect([(splitfunc, datasink,
[('copes', 'copes.mni'),
('varcopes', 'varcopes.mni'),
('zstats', 'zstats.mni'),
])])
wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
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45 changes: 23 additions & 22 deletions nipype/workflows/fmri/fsl/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1104,7 +1104,9 @@ def create_reg_workflow(name='registration'):
inputnode = pe.Node(interface=util.IdentityInterface(fields=['source_files',
'mean_image',
'anatomical_image',
'target_image']),
'target_image',
'target_image_brain',
'config_file']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
'anat2target_transform',
Expand Down Expand Up @@ -1154,15 +1156,20 @@ def create_reg_workflow(name='registration'):
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file', mean2anatbbr, 'in_matrix_file')
register.connect(mean2anat, 'out_matrix_file',
mean2anatbbr, 'in_matrix_file')

"""
Calculate affine transform from anatomical to target
"""

anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
register.connect(inputnode, 'anatomical_image', anat2target_affine, 'in_file')
register.connect(inputnode, 'target_image', anat2target_affine, 'reference')
anat2target_affine.inputs.searchr_x = [-180, 180]
anat2target_affine.inputs.searchr_y = [-180, 180]
anat2target_affine.inputs.searchr_z = [-180, 180]
register.connect(stripper, 'out_file', anat2target_affine, 'in_file')
register.connect(inputnode, 'target_image_brain',
anat2target_affine, 'reference')

"""
Calculate nonlinear transform from anatomical to target
Expand All @@ -1172,22 +1179,20 @@ def create_reg_workflow(name='registration'):
anat2target_nonlinear.inputs.fieldcoeff_file=True
register.connect(anat2target_affine, 'out_matrix_file',
anat2target_nonlinear, 'affine_file')
anat2target_nonlinear.inputs.warp_resolution = (8, 8, 8)
register.connect(inputnode, 'anatomical_image', anat2target_nonlinear, 'in_file')
register.connect(inputnode, 'anatomical_image',
anat2target_nonlinear, 'in_file')
register.connect(inputnode, 'config_file',
anat2target_nonlinear, 'config_file')
register.connect(inputnode, 'target_image',
anat2target_nonlinear, 'ref_file')

"""
Transform the mean image. First to anatomical and then to target
"""

warp2anat = pe.Node(fsl.ApplyWarp(interp='spline'), name='warp2anat')
register.connect(inputnode, 'mean_image', warp2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warp2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warp2anat, 'premat')

warpmean = warp2anat.clone(name='warpmean')
register.connect(warp2anat, 'out_file', warpmean, 'in_file')
warpmean = pe.Node(fsl.ApplyWarp(interp='spline'), name='warpmean')
register.connect(inputnode, 'mean_image', warpmean, 'in_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpmean, 'premat')
register.connect(inputnode, 'target_image', warpmean, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file',
warpmean, 'field_file')
Expand All @@ -1196,15 +1201,11 @@ def create_reg_workflow(name='registration'):
Transform the remaining images. First to anatomical and then to target
"""

warpall2anat = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file'],
name='warpall2anat')
register.connect(inputnode, 'source_files', warpall2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warpall2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpall2anat, 'premat')

warpall = warpall2anat.clone(name='warpall')
register.connect(warpall2anat, 'out_file', warpall, 'in_file')
warpall = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file'],
name='warpall')
register.connect(inputnode, 'source_files', warpall, 'in_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpall, 'premat')
register.connect(inputnode, 'target_image', warpall, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file',
warpall, 'field_file')
Expand Down