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# fMRI: Coregistration - Slicer, BRAINS

This is currently not working and will raise an exception in release 0.3. It will be fixed in a later release.

> python fmri_slicer_coregistration.py

::
  #raise RuntimeWarning, ‘Slicer not fully implmented’ from nipype.interfaces.slicer import BRAINSFit, BRAINSResample

Import necessary modules from nipype.

    import nipype.interfaces.io as nio           # Data i/o
    import nipype.interfaces.utility as util     # utility
    import nipype.pipeline.engine as pe          # pypeline engine
    import os                                    # system functions

## Preliminaries

Confirm package dependencies are installed. (This is only for the tutorial, rarely would you put this in your own code.)

    from nipype.utils.misc import package_check

    package_check('numpy', '1.3', 'tutorial1')
    package_check('scipy', '0.7', 'tutorial1')
    package_check('networkx', '1.0', 'tutorial1')
    package_check('IPython', '0.10', 'tutorial1')

The nipype tutorial contains data for two subjects. Subject data is in two subdirectories, `s1` and `s2`. Each subject directory contains four functional volumes: f3.nii, f5.nii, f7.nii, f10.nii. And one anatomical volume named struct.nii.

Below we set some variables to inform the `datasource` about the layout of our data. We specify the location of the data, the subject sub-directories and a dictionary that maps each run to a mnemonic (or field) for the run type (`struct` or `func`). These fields become the output fields of the `datasource` node in the pipeline.

In the example below, run ‘f3’ is of type ‘func’ and gets mapped to a nifti filename through a template ‘%s.nii’. So ‘f3’ would become ‘f3.nii’.

    # Specify the location of the data.
    data_dir = os.path.abspath('data')
    # Specify the subject directories
    subject_list = ['s1', 's3']
    # Map field names to individual subject runs.
    info = dict(func=[['subject_id', 'f3']],
                struct=[['subject_id','struct']])

    infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
                         name="infosource")

Here we set up iteration over all the subjects. The following line is a particular example of the flexibility of the system. The `datasource` attribute `iterables` tells the pipeline engine that it should repeat the analysis on each of the items in the `subject_list`. In the current example, the entire first level preprocessing and estimation will be repeated for each subject contained in subject_list.

    infosource.iterables = ('subject_id', subject_list)

## Preprocessing pipeline nodes

Now we create a `nipype.interfaces.io.DataSource` object and fill in the information from above about the layout of our data. The `nipype.pipeline.NodeWrapper` module wraps the interface object and provides additional housekeeping and pipeline specific functionality.

    datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                   outfields=['func', 'struct']),
                         name = 'datasource')
    datasource.inputs.base_directory = data_dir
    datasource.inputs.template = '%s/%s.nii'
    datasource.inputs.template_args = info

    coregister = pe.Node(interface=BRAINSFit(), name="coregister")
    coregister.inputs.outputTransform = True
    coregister.inputs.outputVolume = True
    coregister.inputs.transformType = ["Affine"]

    reslice = pe.Node(interface=BRAINSResample(), name="reslice")
    reslice.inputs.outputVolume = True

    pipeline = pe.Workflow(name="pipeline")
    pipeline.base_dir = os.path.abspath('slicer_tutorial/workingdir')

    pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                      (datasource,coregister,[('func','movingVolume')]),
                      (datasource,coregister,[('struct','fixedVolume')]),
                      (coregister,reslice,[('outputTransform', 'warpTransform')]),
                      (datasource,reslice,[('func','inputVolume')]),
                      (datasource,reslice,[('struct','referenceVolume')])
                      ])

    if __name__ == '__main__':
        pipeline.run()
        pipeline.write_graph()

Example source code

You can download `the full source code of this example`. This same script is also included in the Nipype source distribution under the `examples` directory.