.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/anatomical/example_anat_EPI.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_anat_EPI.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_anatomical_example_anat_EPI.py:


Single anatomical 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 14-24

.. code-block:: Python


    # Imports
    import numpy as np

    from snake.core.phantom import Phantom
    from snake.core.sampling import EPI3dAcquisitionSampler
    from snake.core.simulation import GreConfig, SimConfig, default_hardware
    from snake.toolkit.plotting import axis3dcut









.. GENERATED FROM PYTHON SOURCE LINES 25-27

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

.. GENERATED FROM PYTHON SOURCE LINES 27-37

.. code-block:: Python


    sim_conf = SimConfig(
        max_sim_time=6,
        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>6</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 38-47

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 47-56

.. code-block:: Python


    phantom = Phantom.from_brainweb(
        sub_id=4, sim_conf=sim_conf, output_res=1, tissue_file="tissue_7T"
    )

    # Here are the tissue availables and their parameters
    phantom.affine






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

 .. code-block:: none

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

    array([[1., 0., 0., 0.],
           [0., 1., 0., 0.],
           [0., 0., 1., 0.],
           [0., 0., 0., 1.]], dtype=float32)



.. GENERATED FROM PYTHON SOURCE LINES 57-59

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

.. GENERATED FROM PYTHON SOURCE LINES 59-66

.. 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")








.. GENERATED FROM PYTHON SOURCE LINES 67-81

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.

SNAKE comes with two models for the MR Physics:

- model="simple" :: Each k-space shot acquires a constant signal, which is the
  image contrast at TE.
- model="T2s" :: Each k-space shot is degraded by the T2* decay induced by
  each tissue.

.. GENERATED FROM PYTHON SOURCE LINES 81-107

.. code-block:: Python


    # Here we will use the "simple" model, which is faster.
    #
    # SNAKE's Engine are capable of simulating the data in parallel, by distributing
    # the shots to be acquired to a set of processes. To do so , we need to specify
    # the number of jobs that will run in parallel, as well as the size of a job.
    # Setting the job size and the number of jobs can have a great impact on total
    # runtime and memory consumption.
    #
    # Here, we have a single frame to acquire with 60 frames (one EPI per slice), so
    # a single worker will do.

    from snake.core.engine import EPIAcquisitionEngine


    engine = EPIAcquisitionEngine(model="simple")

    engine(
        "example_EPI.mrd",
        sampler=sampler,
        phantom=phantom,
        sim_conf=sim_conf,
        worker_chunk_size=16,
        n_workers=4,
    )



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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/volatile/github-ci-mind-inria/gpu_mind_runner/_work/snake-fmri/snake-fmri/examples/anatomical/example_anat_EPI.py", line 98, in <module>
        engine(
      File "/volatile/github-ci-mind-inria/gpu_mind_runner/_work/snake-fmri/snake-fmri/src/snake/core/engine/base.py", line 299, in __call__
        raise exc
      File "/volatile/github-ci-mind-inria/gpu_mind_runner/_work/snake-fmri/snake-fmri/src/snake/core/engine/base.py", line 296, in __call__
        f_chunk = str(future.result())
      File "/volatile/github-ci-mind-inria/gpu_mind_runner/_work/_tool/Python/3.10.16/x64/lib/python3.10/concurrent/futures/_base.py", line 451, in result
        return self.__get_result()
      File "/volatile/github-ci-mind-inria/gpu_mind_runner/_work/_tool/Python/3.10.16/x64/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
        raise self._exception
    IndexError: index 1 is out of bounds for axis 0 with size 1




.. GENERATED FROM PYTHON SOURCE LINES 108-120

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 120-127

.. code-block:: Python


    from snake.mrd_utils import CartesianFrameDataLoader

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



.. GENERATED FROM PYTHON SOURCE LINES 128-131

.. code-block:: Python

    with CartesianFrameDataLoader("example_EPI.mrd") as data_loader:
        phantom = data_loader.get_phantom()


.. GENERATED FROM PYTHON SOURCE LINES 132-134

.. code-block:: Python

    sim_conf.fov.affine


.. GENERATED FROM PYTHON SOURCE LINES 135-136

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

.. GENERATED FROM PYTHON SOURCE LINES 136-147

.. code-block:: Python


    from scipy.fft import fftshift, ifftn, ifftshift

    axes = (-3, -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 148-157

.. code-block:: Python


    import matplotlib.pyplot as plt


    fig, ax = plt.subplots()

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



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

   **Total running time of the script:** (0 minutes 20.716 seconds)


.. _sphx_glr_download_auto_examples_anatomical_example_anat_EPI.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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