.. _sphx_glr_auto_examples_02_preprocessing_plot_fmri_coregistration.py:


Functional and anatomical coregistration
========================================

Standard functional preprocessing and registration of functional image to the
anatomical.


Retrieve data
-------------



.. code-block:: python

    from sammba import data_fetchers

    retest = data_fetchers.fetch_zurich_test_retest(subjects=[0],
                                                    correct_headers=True)







retest contains paths to images and data description



.. code-block:: python

    anat_filename = retest.anat[0]
    func_filename = retest.func[0]
    print(func_filename)





.. rst-class:: sphx-glr-script-out

 Out::

    /home/salma/nilearn_data/zurich_retest/baseline/1366/rsfMRI_corrected.nii.gz


We use the `Coregistrator`, which coregisters the anatomical to
a given modality



.. code-block:: python

    from sammba.registration import Coregistrator

    coregistrator = Coregistrator(output_dir='animal_1366', brain_volume=400,
                                  use_rats_tool=False, caching=True)
    print(coregistrator)





.. rst-class:: sphx-glr-script-out

 Out::

    Coregistrator(brain_volume=400, caching=True, clipping_fraction=0.2,
                  output_dir='animal_1366', use_rats_tool=False, verbose=True)


`Coregistrator` comes with a parameter `clipping_fraction=.2` which
sometimes needs to be changed to get a good brain mask. You can check how
this parameter impacts the brain segmentation



.. code-block:: python

    from sammba.segmentation import brain_extraction_report

    print(brain_extraction_report(anat_filename, brain_volume=400,
                                  clipping_fractions=[.1, .2, .9, None],
                                  use_rats_tool=False))





.. rst-class:: sphx-glr-script-out

 Out::

    AP length    RL width   IS height    symmetry      volume

    fraction 0.10        13.50        9.70        6.10        0.90      373.50
    fraction 0.20        12.90        9.70        6.20        0.91      384.19
    fraction 0.90        19.00       14.00        7.00        1.00     1862.00
      no fraction        19.00       14.00        7.00        1.00     1862.00


Anatomical to functional registration
-------------------------------------



.. code-block:: python

    coregistrator.fit_anat(anat_filename)
    coregistrator.fit_modality(func_filename, 'func', t_r=1.,
                               prior_rigid_body_registration=True)







The paths to the registered functional and anatomical images are accessible
through the `coregistrator` attributes



.. code-block:: python

    registered_func_filename = coregistrator.undistorted_func_
    registered_anat_filename = coregistrator.anat_in_func_space_







Check out the results
---------------------



.. code-block:: python

    from nilearn import plotting, image

    display = plotting.plot_epi(image.mean_img(registered_func_filename),
                                title='coreg anat edges on top of mean coreg EPI')
    display.add_edges(registered_anat_filename)
    plotting.show()



.. image:: /auto_examples/02_preprocessing/images/sphx_glr_plot_fmri_coregistration_001.png
    :align: center




**Total running time of the script:** ( 0 minutes  50.045 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_fmri_coregistration.py <plot_fmri_coregistration.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_fmri_coregistration.ipynb <plot_fmri_coregistration.ipynb>`

.. rst-class:: sphx-glr-signature

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