.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/anatomical/example_EPI_2D.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_anatomical_example_EPI_2D.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_anatomical_example_EPI_2D.py:


Single anatomical volume with 2D EPI with SNAKE-fMRI
====================================================

This examples walks through the elementary components of SNAKE.

Here we proceed step by step and use the Python interface. A more integrated
alternative is to use the CLI ``snake-main``

.. GENERATED FROM PYTHON SOURCE LINES 14-22

.. code-block:: Python


    # Imports
    import numpy as np
    from snake.core.simulation import SimConfig, default_hardware, GreConfig
    from snake.core.phantom import Phantom
    from snake.core.smaps import get_smaps
    from snake.core.sampling import EPI3dAcquisitionSampler








.. GENERATED FROM PYTHON SOURCE LINES 23-25

Setting up the base simulation Config.  This configuration holds all key
parameters for the simulation, describing the scanner parameters.

.. GENERATED FROM PYTHON SOURCE LINES 25-36

.. code-block:: Python


    sim_conf = SimConfig(
        max_sim_time=3,
        seq=GreConfig(TR=50, TE=30, FA=3),
        hardware=default_hardware,
    )
    sim_conf.hardware.n_coils = 8

    sim_conf.fov.res_mm = (3,3,3)
    sim_conf






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <table style="border:1px solid lightgray;"><caption style="border:1px solid lightgray;"><strong>SimConfig</strong></caption>
    <tr><td>max_sim_time(<i>float</i>)</td><td>3</td></tr>
    <tr><td>seq(<i>GreConfig</i>)</td><td><table style="border:1px solid lightgray;"><caption style="border:1px solid lightgray;"><strong>GreConfig</strong></caption>
    <tr><td>TR (<i>float</i>)</td><td>TE (<i>float</i>)</td><td>FA (<i>float</i>)</td></tr>
    <tr><td>50</td><td>30</td><td>3</td></tr>
    </table></td></tr>
    <tr><td>hardware(<i>HardwareConfig</i>)</td><td><table style="border:1px solid lightgray;"><caption style="border:1px solid lightgray;"><strong>HardwareConfig</strong></caption>
    <tr><td>gmax (<i>float</i>)</td><td>smax (<i>float</i>)</td><td>n_coils (<i>int</i>)</td><td>dwell_time_ms (<i>float</i>)</td><td>raster_time_ms (<i>float</i>)</td><td>field (<i>float</i>)</td></tr>
    <tr><td>40</td><td>200</td><td>8</td><td>0.001</td><td>0.005</td><td>3.0</td></tr>
    </table></td></tr>
    <tr><td>fov(<i>FOVConfig</i>)</td><td><table style="border:1px solid lightgray;"><caption style="border:1px solid lightgray;"><strong>FOVConfig</strong></caption>
    <tr><td>size (<i>ThreeFloats</i>)</td><td>offset (<i>ThreeFloats</i>)</td><td>angles (<i>ThreeFloats</i>)</td><td>res_mm (<i>ThreeFloats</i>)</td></tr>
    <tr><td>(181, 217, 181)</td><td>(-90.25, -126.25, -72.25)</td><td>(0, 0, 0)</td><td>(3, 3, 3)</td></tr>
    </table></td></tr>
    <tr><td>rng_seed(<i>int</i>)</td><td>19290506</td></tr>
    </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-46

Creating the base Phantom
-------------------------

The simulation acquires
the data describe in a phantom. A phantom consists of fuzzy segmentation of
head tissue, and their MR intrinsic parameters
(density, T1, T2, T2*,  magnetic susceptibilities)

Here we use Brainweb reference mask and values for convenience.

.. GENERATED FROM PYTHON SOURCE LINES 46-53

.. code-block:: Python


    phantom = Phantom.from_brainweb(sub_id=4, sim_conf=sim_conf, output_res=1)

    # Here are the tissue availables and their parameters
    phantom






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

 .. code-block:: none

    No matrix size found in the header. The header is probably missing.

    Phantom[brainweb-{sub_id:02d}]: (12, 5)
    tissue name   PropTissueEnum.T1PropTissueEnum.T2PropTissueEnum.T2sPropTissueEnum.rhoPropTissueEnum.chi
    vessels       1600.080.070.0 1.0-9.0
    wm            500.070.061.00.7699999809265137-9.050000190734863
    muscles       900.050.040.0 1.0-9.050000190734863
    skull         300.0 1.0 1.00.10000000149011612-8.859999656677246
    dura          1000.0100.050.00.6000000238418579-9.050000190734863
    marrow        1000.050.040.0 1.0-9.050000190734863
    csf           4000.02000.02000.0 1.0-9.050000190734863
    muscles_skin  2569.0329.058.0 1.0-9.050000190734863
    gm            900.090.069.00.8600000143051147-9.050000190734863
    fat           250.070.060.0 1.0-7.789999961853027
    bck           800.0800.0800.0 0.00.36000001430511475
    fat2          250.070.060.0 1.0-7.789999961853027




.. GENERATED FROM PYTHON SOURCE LINES 54-56

Setting up Acquisition Pattern and Initializing Result file.
------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 56-67

.. code-block:: Python


    # The next piece of simulation is the acquisition trajectory.
    # Here nothing fancy, we are using a EPI (fully sampled), that samples a 3D
    # k-space (this akin to the 3D EPI sequence of XXXX)

    sampler = EPI3dAcquisitionSampler(accelz=1, acsz=0.1, orderz="top-down")

    smaps = None
    if sim_conf.hardware.n_coils > 1:
        smaps = get_smaps(sim_conf.shape, n_coils=sim_conf.hardware.n_coils)








.. GENERATED FROM PYTHON SOURCE LINES 68-78

Acquisition with Cartesian Engine
---------------------------------

The generated file ``example_EPI.mrd`` does not contains any k-space data for
now, only the sampling trajectory. let's put some in. In order to do so, we
need to setup the **acquisition engine** that models the MR physics, and get
sampled at the specified k-space trajectory.

Here, we have a single frame to acquire with 60 slice (one EPI per slice), so
a single worker will be enough.

.. GENERATED FROM PYTHON SOURCE LINES 78-92

.. code-block:: Python


    from snake.core.engine import EPIAcquisitionEngine

    engine = EPIAcquisitionEngine(model="T2s", slice_2d=True)  # use the 2D epi.

    engine(
        "example_EPI2D.mrd",
        sampler=sampler,
        phantom=phantom,
        sim_conf=sim_conf,
        worker_chunk_size=20,
        n_workers=2,
    )





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

 .. code-block:: none

    Using 2D acquisition, the TR_eff is updated to TR_vol
    Existing example_EPI2D.mrd it will be overwritten
    [Errno 2] No such file or directory: 'example_EPI2D.mrd'

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.. GENERATED FROM PYTHON SOURCE LINES 93-105

Simple reconstruction
---------------------

Getting k-space data is nice, but
SNAKE also provides rudimentary reconstruction tools to get images (and check
that we didn't mess up the acquisition process).
This is available in the companion package ``snake.toolkit``.

Loading the ``.mrd`` file to retrieve all information can be done using the
``ismrmd`` python package, but SNAKE provides convenient dataloaders, which are
more efficient, and take cares of managing underlying files access. As we are
showcasing the API, we will do things manually here, and use only core SNAKE.

.. GENERATED FROM PYTHON SOURCE LINES 105-112

.. code-block:: Python


    from snake.mrd_utils import CartesianFrameDataLoader

    with CartesianFrameDataLoader("example_EPI2D.mrd") as data_loader:
        mask, kspace_data = data_loader.get_kspace_frame(0)









.. GENERATED FROM PYTHON SOURCE LINES 113-114

Reconstructing a Single Frame of fully sampled EPI boils down to performing a 3D IFFT:

.. GENERATED FROM PYTHON SOURCE LINES 114-125

.. code-block:: Python


    from scipy.fft import ifftn, ifftshift, fftshift

    axes = (-2,-1)
    image_data = ifftshift(
        ifftn(fftshift(kspace_data, axes=axes), axes=axes, norm="ortho"), axes=axes
    )

    # Take the square root sum of squares to get the magnitude image (SSOS)
    image_data = np.sqrt(np.sum(np.abs(image_data) ** 2, axis=0))








.. GENERATED FROM PYTHON SOURCE LINES 126-135

.. code-block:: Python


    import matplotlib.pyplot as plt
    from snake.toolkit.plotting import axis3dcut

    fig, ax = plt.subplots()

    axis3dcut(image_data.squeeze().T, None, None, cbar=False, cuts=(0.5,0.5,0.5), ax=ax)
    plt.show()




.. image-sg:: /auto_examples/anatomical/images/sphx_glr_example_EPI_2D_001.png
   :alt: example EPI 2D
   :srcset: /auto_examples/anatomical/images/sphx_glr_example_EPI_2D_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 44.632 seconds)


.. _sphx_glr_download_auto_examples_anatomical_example_EPI_2D.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: example_EPI_2D.ipynb <example_EPI_2D.ipynb>`

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

      :download:`Download Python source code: example_EPI_2D.py <example_EPI_2D.py>`

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

      :download:`Download zipped: example_EPI_2D.zip <example_EPI_2D.zip>`


.. only:: html

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

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