.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated\examples\plot_3d_mask_translation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_generated_examples_plot_3d_mask_translation.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_examples_plot_3d_mask_translation.py:


=============================
3D translation: image to mask
=============================

This example illustrates coregistration by 3D translation. The moving volume 
is a 3D magnetization transfer map and the static volume is a 3D mask covering 
both kidneys. 

Coregistration is performed by brute force optimization using a mutual 
information metric. 

.. GENERATED FROM PYTHON SOURCE LINES 15-17

Setup
-----

.. GENERATED FROM PYTHON SOURCE LINES 17-21

.. code-block:: Python

    import numpy as np
    import vreg
    import vreg.plot as plt








.. GENERATED FROM PYTHON SOURCE LINES 22-24

Get data
--------

.. GENERATED FROM PYTHON SOURCE LINES 24-35

.. code-block:: Python


    # Static oblique volumes
    lk = vreg.fetch('left_kidney')
    rk = vreg.fetch('right_kidney')

    # Moving volume
    mtr = vreg.fetch('MTR')

    # Geometrical reference
    dixon = vreg.fetch('Dixon_water')








.. GENERATED FROM PYTHON SOURCE LINES 36-40

Format data
-----------
Create a mask containing both kidneys (bk) with the geometry of the 
complete DIXON series

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: Python


    bk = lk.slice_like(dixon).add(rk)








.. GENERATED FROM PYTHON SOURCE LINES 44-48

Bounding box
------------
Extract a bounding box to reduce the size of the volume. This is not 
necessary but it speeds up the calculation a little as the volume is smaller.

.. GENERATED FROM PYTHON SOURCE LINES 48-51

.. code-block:: Python


    bk = bk.bounding_box() 








.. GENERATED FROM PYTHON SOURCE LINES 52-56

Overlay data before registration
--------------------------------
If we overlay the mask on the volume, we clearly see the misalignment due to 
different breath holding positions:

.. GENERATED FROM PYTHON SOURCE LINES 56-62

.. code-block:: Python


    plt.overlay_2d(mtr, bk, title='Before 3D translation', 
                   vmin=np.percentile(mtr.values, 10),
                   vmax=np.percentile(mtr.values, 99))





.. image-sg:: /generated/examples/images/sphx_glr_plot_3d_mask_translation_001.png
   :alt: Before 3D translation
   :srcset: /generated/examples/images/sphx_glr_plot_3d_mask_translation_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 63-70

Coregister
----------

We are coregistering using a 3D translation in the reference frame of the 
moving volume. We are using a brute force optimization which is slow but 
robust. We allow for translations between [-20, 20] mm in-slice, and 
[-5, 5] mm through-slice, in steps of 2mm.

.. GENERATED FROM PYTHON SOURCE LINES 70-83

.. code-block:: Python


    optimizer = {
        'method': 'brute',
        'grid': (
            [-20, 20, 20],
            [-20, 20, 20],
            [-5, 5, 5],
        ),
    }
    params = mtr.find_translate_to(bk, optimizer=optimizer, coords='volume')
    mtr = mtr.translate(params, coords='volume')









.. GENERATED FROM PYTHON SOURCE LINES 84-88

Overlay data after registration
-------------------------------
If we overlay the mask on the new volume, we can see that the misalignment 
is significantly reduced:

.. GENERATED FROM PYTHON SOURCE LINES 88-95

.. code-block:: Python


    plt.overlay_2d(mtr, bk, title='After 3D translation', 
                   vmin=np.percentile(mtr.values, 10),
                   vmax=np.percentile(mtr.values, 99))






.. image-sg:: /generated/examples/images/sphx_glr_plot_3d_mask_translation_002.png
   :alt: After 3D translation
   :srcset: /generated/examples/images/sphx_glr_plot_3d_mask_translation_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (4 minutes 10.542 seconds)


.. _sphx_glr_download_generated_examples_plot_3d_mask_translation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_3d_mask_translation.ipynb <plot_3d_mask_translation.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_3d_mask_translation.py <plot_3d_mask_translation.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_3d_mask_translation.zip <plot_3d_mask_translation.zip>`


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_