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ENH/WIP: added Apply VDM functionality to FieldMap SPM interface #2879

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ENH/WIP: added Apply VDM functionality to FieldMap SPM interface #2879

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5dcfe15
added apply functionality to fieldmap spm interface. works in my prep…
Feb 11, 2019
b763e51
just following instructions from contributing nipype page
Feb 11, 2019
06fe011
added name to zenodo contributors list
Feb 11, 2019
5dc6e23
test_auto_FieldMap automatically edited
Feb 11, 2019
dc8e1cd
Merge branch 'master' into applyvdm
fabioboh Apr 21, 2021
4fa942c
Merge remote-tracking branch 'upstream/master' into applyvdm
effigies May 1, 2021
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5 changes: 5 additions & 0 deletions .zenodo.json
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Original file line number Diff line number Diff line change
Expand Up @@ -481,6 +481,11 @@
"name": "Geisler, Daniel",
"orcid": "0000-0003-2076-5329"
},
{
"affiliation": "Technische Universit\u00e4t Dresden, Faculty of Medicine",
"name": "Bernardoni, Fabio",
"orcid": "0000-0002-5112-405X"
},
{
"affiliation": "University of illinois urbana champaign",
"name": "Sharp, Paul"
Expand Down
237 changes: 167 additions & 70 deletions nipype/interfaces/spm/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,86 +26,135 @@

class FieldMapInputSpec(SPMCommandInputSpec):
jobtype = traits.Enum('calculatevdm', 'applyvdm', usedefault=True,
desc='one of: calculatevdm, applyvdm')
phase_file = File(mandatory=True, exists=True, copyfile=False,
field='subj.data.presubphasemag.phase',
desc='presubstracted phase file')
magnitude_file = File(mandatory=True, exists=True, copyfile=False,
field='subj.data.presubphasemag.magnitude',
desc='presubstracted magnitude file')
echo_times = traits.Tuple(traits.Float, traits.Float, mandatory=True,
field='subj.defaults.defaultsval.et',
desc='short and long echo times')
desc='one of: calculatevdm, applyvdm')
phase_file = File(exists=True, copyfile=False,
field='subj.data.presubphasemag.phase',
desc='presubstracted phase file')
magnitude_file = File(exists=True, copyfile=False,
field='subj.data.presubphasemag.magnitude',
desc='presubstracted magnitude file')
echo_times = traits.Tuple(traits.Float, traits.Float,
field='subj.defaults.defaultsval.et',
desc='short and long echo times')
maskbrain = traits.Bool(True, usedefault=True,
field='subj.defaults.defaultsval.maskbrain',
desc='masking or no masking of the brain')
blip_direction = traits.Enum(1, -1, mandatory=True,
field='subj.defaults.defaultsval.blipdir',
desc='polarity of the phase-encode blips')
total_readout_time = traits.Float(mandatory=True,
field='subj.defaults.defaultsval.tert',
desc='total EPI readout time')
field='subj.defaults.defaultsval.maskbrain',
desc='masking or no masking of the brain')
blip_direction = traits.Enum(1, -1,
field='subj.defaults.defaultsval.blipdir',
desc='polarity of the phase-encode blips')
total_readout_time = traits.Float(field='subj.defaults.defaultsval.tert',
desc='total EPI readout time')
epifm = traits.Bool(False, usedefault=True,
field='subj.defaults.defaultsval.epifm',
desc='epi-based field map');
field='subj.defaults.defaultsval.epifm',
desc='epi-based field map');
jacobian_modulation = traits.Bool(False, usedefault=True,
field='subj.defaults.defaultsval.ajm',
desc='jacobian modulation');
field='subj.defaults.defaultsval.ajm',
desc='jacobian modulation');
# Unwarping defaults parameters
method = traits.Enum('Mark3D', 'Mark2D', 'Huttonish', usedefault=True,
desc='One of: Mark3D, Mark2D, Huttonish',
field='subj.defaults.defaultsval.uflags.method');
desc='One of: Mark3D, Mark2D, Huttonish',
field='subj.defaults.defaultsval.uflags.method');
unwarp_fwhm = traits.Range(low=0, value=10, usedefault=True,
field='subj.defaults.defaultsval.uflags.fwhm',
desc='gaussian smoothing kernel width');
field='subj.defaults.defaultsval.uflags.fwhm',
desc='gaussian smoothing kernel width');
pad = traits.Range(low=0, value=0, usedefault=True,
field='subj.defaults.defaultsval.uflags.pad',
desc='padding kernel width');
field='subj.defaults.defaultsval.uflags.pad',
desc='padding kernel width');
ws = traits.Bool(True, usedefault=True,
field='subj.defaults.defaultsval.uflags.ws',
desc='weighted smoothing');
field='subj.defaults.defaultsval.uflags.ws',
desc='weighted smoothing');
# Brain mask defaults parameters
template = File(copyfile=False, exists=True,
field='subj.defaults.defaultsval.mflags.template',
desc='template image for brain masking');
field='subj.defaults.defaultsval.mflags.template',
desc='template image for brain masking');
mask_fwhm = traits.Range(low=0, value=5, usedefault=True,
field='subj.defaults.defaultsval.mflags.fwhm',
desc='gaussian smoothing kernel width');
field='subj.defaults.defaultsval.mflags.fwhm',
desc='gaussian smoothing kernel width');
nerode = traits.Range(low=0, value=2, usedefault=True,
field='subj.defaults.defaultsval.mflags.nerode',
desc='number of erosions');
field='subj.defaults.defaultsval.mflags.nerode',
desc='number of erosions');
ndilate = traits.Range(low=0, value=4, usedefault=True,
field='subj.defaults.defaultsval.mflags.ndilate',
desc='number of erosions');
field='subj.defaults.defaultsval.mflags.ndilate',
desc='number of erosions');
thresh = traits.Float(0.5, usedefault=True,
field='subj.defaults.defaultsval.mflags.thresh',
desc='threshold used to create brain mask from segmented data');
field='subj.defaults.defaultsval.mflags.thresh',
desc='threshold used to create brain mask from segmented data');
reg = traits.Float(0.02, usedefault=True,
field='subj.defaults.defaultsval.mflags.reg',
desc='regularization value used in the segmentation');
field='subj.defaults.defaultsval.mflags.reg',
desc='regularization value used in the segmentation');
# EPI unwarping for quality check
epi_file = File(copyfile=False, exists=True, mandatory=True,
field='subj.session.epi',
desc='EPI to unwarp');
epi_file = File(copyfile=False, exists=True,
field='subj.session.epi',
desc='EPI to unwarp for quality check');
matchvdm = traits.Bool(True, usedefault=True,
field='subj.matchvdm',
desc='match VDM to EPI');
field='subj.matchvdm',
desc='match VDM to EPI');
sessname = Str('_run-', usedefault=True,
field='subj.sessname',
desc='VDM filename extension');
field='subj.sessname',
desc='VDM filename extension');
writeunwarped = traits.Bool(False, usedefault=True,
field='subj.writeunwarped',
desc='write unwarped EPI');
field='subj.writeunwarped',
desc='write unwarped EPI');
anat_file = File(copyfile=False, exists=True,
field='subj.anat',
desc='anatomical image for comparison');
field='subj.anat',
desc='anatomical image for comparison');
matchanat = traits.Bool(True, usedefault=True,
field='subj.matchanat',
desc='match anatomical image to EPI');
field='subj.matchanat',
desc='match anatomical image to EPI');

in_files = InputMultiObject(
traits.Either(ImageFileSPM(exists=True),
traits.List(ImageFileSPM(exists=True))),
field='data.scans',mandatory=True,
copyfile=True,
desc='list of filenames to apply the vdm to')
vdmfile = File(
field='data.vdmfile',
desc='Voxel displacement map to use',mandatory=True,
copyfile=True)
distortion_direction = traits.Int(
2, field='roptions.pedir', desc='phase encode direction input data have been acquired with',
usedefault=True)
write_which = traits.ListInt(
[2, 1],
field='roptions.which',
minlen=2,
maxlen=2,
usedefault=True,
desc='determines which images to apply vdm to')
interpolation = traits.Int(
4, field='roptions.rinterp', desc='phase encode direction input data have been acquired with',
usedefault=True)
reslice_interp = traits.Range(
low=0,
high=7,
field='roptions.rinterp',
desc='degree of b-spline used for interpolation')
write_wrap = traits.List(
traits.Int(),
minlen=3,
maxlen=3,
field='roptions.wrap',
desc=('Check if interpolation should wrap in [x,y,z]'))
write_mask = traits.Bool(
field='roptions.mask', desc='True/False mask time series images')
out_prefix = traits.String(
'u',
field='roptions.prefix',
usedefault=True,
desc='fieldmap corrected output prefix')


class FieldMapOutputSpec(TraitedSpec):
vdm = File(exists=True, desc='voxel difference map')
out_files = OutputMultiPath(
traits.Either(traits.List(File(exists=True)), File(exists=True)),
desc=('If jobtype is applyvdm, '
'these will be the fieldmap corrected files.'
' Otherwise, they will be copies '
'of in_files that have had their '
'headers rewritten.'))
mean_image = File(exists=True, desc='Mean image')


class FieldMap(SPMCommand):
Expand Down Expand Up @@ -139,22 +188,75 @@ class FieldMap(SPMCommand):
def _format_arg(self, opt, spec, val):
"""Convert input to appropriate format for spm
"""
if opt in ['phase_file', 'magnitude_file', 'anat_file', 'epi_file']:
if ((self.inputs.jobtype == "calculatevdm") and (opt in ['phase_file', 'magnitude_file', 'anat_file', 'epi_file'])):
return scans_for_fname(ensure_list(val))

if ((self.inputs.jobtype == "applyvdm") and (opt =='in_files')):
return scans_for_fnames(ensure_list(val))
if ((self.inputs.jobtype == "applyvdm") and (opt =='vdmfile')):
return scans_for_fname(ensure_list(val))
return super(FieldMap, self)._format_arg(opt, spec, val)



def _parse_inputs(self):
"""validate spm fieldmap options if set to None ignore
"""
einputs = super(FieldMap, self)._parse_inputs()
return [{self.inputs.jobtype: einputs[0]}]
if self.inputs.jobtype == "applyvdm":
einputs = (super(FieldMap, self)
._parse_inputs(skip=('jobtype','phase_file', 'magnitude_file',
'echo_times', 'blip_direction',
'total_readout_time','maskbrain',
'epifm','jacobian_modulation',
'method','unwarp_fwhm','pad','ws',
'template','mask_fwhm','nerode','ndilate',
'thresh','reg','epi_file','matchvdm',
'sessname','writeunwarped',
'anat_file','matchanat')))

else:
einputs = (super(FieldMap, self)
._parse_inputs(skip=('jobtype','in_files', 'vdmfile')))
jobtype = self.inputs.jobtype
# einputs = super(FieldMap, self)._parse_inputs()
# return [{self.inputs.jobtype: einputs[0]}]

return [{'%s' % (jobtype): einputs[0]}]


def _list_outputs(self):
outputs = self._outputs().get()
jobtype = self.inputs.jobtype
resliced_all = self.inputs.write_which[0] > 0
resliced_mean = self.inputs.write_which[1] > 0
if jobtype == "calculatevdm":
outputs['vdm'] = fname_presuffix(self.inputs.phase_file, prefix='vdm5_sc')
elif jobtype == "applyvdm":
# outputs['out_files'] = fname_presuffix(
# self.inputs.in_files, prefix=self.inputs.out_prefix)
if resliced_mean:
if isinstance(self.inputs.in_files[0], list):
first_image = self.inputs.in_files[0][0]
else:
first_image = self.inputs.in_files[0]
outputs['mean_image'] = fname_presuffix(
first_image, prefix='meanu')

if resliced_all:
outputs['out_files'] = []
for idx, imgf in enumerate(ensure_list(self.inputs.in_files)):
appliedvdm_run = []
if isinstance(imgf, list):
for i, inner_imgf in enumerate(ensure_list(imgf)):
newfile = fname_presuffix(inner_imgf,
prefix=self.inputs.out_prefix)
appliedvdm_run.append(newfile)
else:
appliedvdm_run = fname_presuffix(imgf,
prefix=self.inputs.out_prefix)
outputs['out_files'].append(appliedvdm_run)
return outputs


return outputs

Expand Down Expand Up @@ -188,8 +290,8 @@ class SliceTimingInputSpec(SPMCommandInputSpec):
ref_slice = traits.Int(
field='refslice',
desc='1-based Number of the reference slice or '
'reference time point if slice_order is in '
'onsets (ms)',
'reference time point if slice_order is in '
'onsets (ms)',
mandatory=True)
out_prefix = traits.String(
'a', field='prefix', usedefault=True, desc='slicetimed output prefix')
Expand Down Expand Up @@ -438,7 +540,6 @@ def _list_outputs(self):


class RealignUnwarpInputSpec(SPMCommandInputSpec):

in_files = InputMultiObject(
traits.Either(ImageFileSPM(exists=True),
traits.List(ImageFileSPM(exists=True))),
Expand Down Expand Up @@ -504,7 +605,7 @@ class RealignUnwarpInputSpec(SPMCommandInputSpec):
est_jacobian_deformations = traits.Bool(
field='uweoptions.jm',
desc=('Jacobian deformations. In theory a good idea to include them, '
' in practice a bad idea. Default: No.'))
' in practice a bad idea. Default: No.'))
est_first_order_effects = traits.List(
traits.Int(),
minlen=1,
Expand Down Expand Up @@ -618,7 +719,6 @@ def _format_arg(self, opt, spec, val):
separate_sessions=True)
return super(RealignUnwarp, self)._format_arg(opt, spec, val)


def _parse_inputs(self, skip=()):

spmdict = super(RealignUnwarp, self)._parse_inputs(skip=())[0]
Expand All @@ -630,16 +730,15 @@ def _parse_inputs(self, skip=()):

if isdefined(self.inputs.in_files):
if isinstance(self.inputs.in_files, list):
data = [dict(scans = sess, pmscan = pmscan)
for sess in spmdict['data']['scans']]
data = [dict(scans=sess, pmscan=pmscan)
for sess in spmdict['data']['scans']]
else:
data = [dict(scans = spmdict['data']['scans'], pmscan = pmscan)]
data = [dict(scans=spmdict['data']['scans'], pmscan=pmscan)]

spmdict['data'] = data

return [spmdict]


def _list_outputs(self):
outputs = self._outputs().get()
resliced_all = self.inputs.reslice_which[0] > 0
Expand Down Expand Up @@ -2118,7 +2217,6 @@ def _list_outputs(self):


class VBMSegmentInputSpec(SPMCommandInputSpec):

in_files = InputMultiPath(
ImageFileSPM(exists=True),
desc="A list of files to be segmented",
Expand Down Expand Up @@ -2295,7 +2393,6 @@ class VBMSegmentInputSpec(SPMCommandInputSpec):


class VBMSegmentOuputSpec(TraitedSpec):

native_class_images = traits.List(
traits.List(File(exists=True)), desc='native space probability maps')
dartel_input_images = traits.List(
Expand Down Expand Up @@ -2394,7 +2491,7 @@ def _list_outputs(self):
outputs['dartel_input_images'][i].append(
os.path.join(pth, "rp%d%s_affine.nii" % (i + 1, base)))

# normalized space
# normalized space
if getattr(self.inputs, '%s_normalized' % tis):
outputs['normalized_class_images'][i].append(
os.path.join(pth, "w%sp%d%s.nii" % (dartel_px, i + 1,
Expand Down
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