added SPM12's new Normalize routine and updated version info and manual links

nipype/interfaces/spm/preprocess.py

@@ -671,6 +671,149 @@ def _list_outputs(self):
  return outputs
 
 
+class Normalize12InputSpec(SPMCommandInputSpec):
+ image_to_align = File(exists=True, field='subj.vol',
+ desc='file to estimate the normalization parameters with',
+ xor=['deformation_file'],
+ mandatory=True, copyfile=True)
+ apply_to_files = InputMultiPath(traits.Either(File(exists=True),
+ traits.List(File(exists=True))),
+ field='subj.resample',
+ desc='files to apply transformation to',
+ copyfile=True)
+ deformation_file = File(field='subj.def', mandatory=True,
+ xor=['image_to_align', 'tpm'],
+ desc='file y_*.nii containing 3 deformation fields for the deformation in x, y and z dimension',
+ copyfile=False)
+ jobtype = traits.Enum('estwrite', 'est', 'write',
+ desc='one of: est, write, estwrite (opt, estwrite)',
+ usedefault=True)
+ bias_regularization = traits.Enum(0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10,
+ field='eoptions.biasreg',
+ desc='no(0) - extremely heavy (10) (opt)')
+ bias_fwhm = traits.Enum(30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
+ 'Inf', field='eoptions.biasfwhm',
+ desc='FWHM of Gaussian smoothness of bias (opt)')
+ tpm = File(exists=True, field='eoptions.tpm',
+ desc='template in form of a tissue probablitiy map to normalize to (opt)',
+ mandatory=False, xor=['deformation_file'],
+ copyfile=False)
+ affine_regularization_type = traits.Enum('mni', 'size', 'none', field='eoptions.affreg',
+ desc='mni, size, none (opt)')
+ warping_regularization = traits.List(traits.Float(), field='eoptions.reg',
+ minlen=5, maxlen=5,
+ desc='Controls balance between parameters and data (opt)')
+ smoothness = traits.Float(field='eoptions.fwhm',
+ desc='value (in mm) to smooth the data before normalization (opt)')
+ sampling_distance = traits.Float(field='eoptions.samp',
+ desc='Sampling distance on data for parameter estimation (opt)')
+ write_bounding_box = traits.List(traits.List(traits.Float(),
+ minlen=3, maxlen=3),
+ field='woptions.bb', minlen=2, maxlen=2,
+ desc='3x2-element list of lists representing the bounding box (in mm) to be written (opt)')
+ write_voxel_sizes = traits.List(traits.Float(), field='woptions.vox',
+ minlen=3, maxlen=3,
+ desc='3-element list representing the voxel sizes (in mm) of the written normalised images (opt)')
+ write_interp = traits.Range(low=0, high=7, field='woptions.interp',
+ desc='degree of b-spline used for interpolation (opt)')
+
+
+class Normalize12OutputSpec(TraitedSpec):
+ deformation_field = OutputMultiPath(File(exists=True), desc='NIfTI file containing 3 deformation fields for the deformation in x, y and z dimension')
+ normalized_image = OutputMultiPath(File(exists=True), desc='Normalized file that needed to be aligned')
+ normalized_files = OutputMultiPath(File(exists=True), desc='Normalized other files')
+
+
+class Normalize12(SPMCommand):
+ """uses SPM12's new Normalise routine for warping an image to a template.
+ Spatial normalisation is now done via the segmentation routine (which was
+ known as ``New Segment`` in SPM8). Note that the normalisation in SPM12
+ is done towards a file containing multiple tissue probability maps, which
+ was not the cass in SPM8.
+
+ http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=49
+
+ Examples
+ --------
+ >>> import nipype.interfaces.spm as spm
+ >>> norm12 = spm.Normalize12()
+ >>> norm12.inputs.image_to_align = 'structural.nii'
+ >>> norm12.inputs.apply_to_files = 'functional.nii'
+ >>> norm12.run() # doctest: +SKIP
+
+ """
+
+ input_spec = Normalize12InputSpec
+ output_spec = Normalize12OutputSpec
+ _jobtype = 'spatial'
+ _jobname = 'normalise'
+
+ def _format_arg(self, opt, spec, val):
+ """Convert input to appropriate format for spm
+ """
+ if opt == 'tpm':
+ return scans_for_fname(filename_to_list(val))
+ if opt == 'image_to_align':
+ return scans_for_fname(filename_to_list(val))
+ if opt == 'apply_to_files':
+ return scans_for_fnames(filename_to_list(val))
+ if opt == 'deformation_file':
+ return np.array([list_to_filename(val)], dtype=object)
+ if opt in ['nonlinear_regularization']:
+ if len(val) != 5:
+ raise ValueError('%s must have 5 elements' % opt)
+ return super(Normalize12, self)._format_arg(opt, spec, val)
+
+ def _parse_inputs(self):
+ """validate spm normalize options if set to None ignore
+ """
+ einputs = super(Normalize12, self)._parse_inputs(skip=('jobtype',
+ 'apply_to_files'))
+ if isdefined(self.inputs.apply_to_files):
+ inputfiles = deepcopy(self.inputs.apply_to_files)
+ if isdefined(self.inputs.image_to_align):
+ inputfiles.extend([self.inputs.image_to_align])
+ einputs[0]['subj']['resample'] = scans_for_fnames(inputfiles)
+ jobtype = self.inputs.jobtype
+ if jobtype in ['estwrite', 'write']:
+ if not isdefined(self.inputs.apply_to_files):
+ if isdefined(self.inputs.image_to_align):
+ einputs[0]['subj']['resample'] = scans_for_fname(self.inputs.image_to_align)
+ return [{'%s' % (jobtype): einputs[0]}]
+
+ def _list_outputs(self):
+ outputs = self._outputs().get()
+
+ jobtype = self.inputs.jobtype
+ if jobtype.startswith('est'):
+ outputs['deformation_field'] = []
+ for imgf in filename_to_list(self.inputs.image_to_align):
+ outputs['deformation_field'].append(fname_presuffix(imgf,prefix='y_'))
+ outputs['deformation_field'] = list_to_filename(outputs['deformation_field'])
+
+ if self.inputs.jobtype == "estimate":
+ if isdefined(self.inputs.apply_to_files):
+ outputs['normalized_files'] = self.inputs.apply_to_files
+ outputs['normalized_image'] = fname_presuffix(self.inputs.image_to_align,
+ prefix='w')
+ elif 'write' in self.inputs.jobtype:
+ outputs['normalized_files'] = []
+ if isdefined(self.inputs.apply_to_files):
+ filelist = filename_to_list(self.inputs.apply_to_files)
+ for f in filelist:
+ if isinstance(f, list):
+ run = [fname_presuffix(in_f,prefix='w') for in_f in f]
+ else:
+ run = [fname_presuffix(f,prefix='w')]
+ outputs['normalized_files'].extend(run)
+ if isdefined(self.inputs.image_to_align):
+ outputs['normalized_image'] = fname_presuffix(self.inputs.image_to_align,
+ prefix='w')
+
+ return outputs
+
+
+
 class SegmentInputSpec(SPMCommandInputSpec):
  data = InputMultiPath(File(exists=True), field='data', desc='one scan per subject',
  copyfile=False, mandatory=True)