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

.. only:: html

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

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

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

.. _sphx_glr_howto_howto_examples_plot_afq_fwdti.py:


==========================
How to use Free water DTI
==========================

The free-water DTI model [1, 2]_ fits a two compartment model to dMRI data
with more than one non-zero shell. One compartment is a spherical compartment
with the diffusivity of water, which accounts for free water in the tissue.
The other compartment is the standard diffusion tensor.

In this example, we will compare the results of the fwDTI model and the
standard DTI model.

.. GENERATED FROM PYTHON SOURCE LINES 15-28

.. code-block:: Python

    import os.path as op

    import matplotlib.pyplot as plt
    import nibabel as nib

    from AFQ.api.group import GroupAFQ
    import AFQ.data.fetch as afd

    from AFQ.definitions.image import ImageFile, RoiImage
    import AFQ.api.bundle_dict as abd

    import pandas as pd








.. GENERATED FROM PYTHON SOURCE LINES 29-40

Get some data
--------------
In this example, we will look at one subject from the Healthy Brain Network
Processed Open Diffusion Derivatives dataset (HBN-POD2) [3, 4]_. The data in
this study were collected with a multi-shell sequence, meaning that most
subjects in this study have data with more than one non-zero b-value. This
means that we can fit the fwDTI model to their data.

We'll use a fetcher to get preprocessd dMRI data for one of the >2,000
subjects in that study. The data gets organized into a BIDS-compatible
format in the `~/AFQ_data/HBN` folder.

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: Python


    study_dir = afd.fetch_hbn_preproc(["NDARAA948VFH"])[1]








.. GENERATED FROM PYTHON SOURCE LINES 44-55

Define an AFQ object
--------------------
In addition to preprocessd dMRI data, HBN-POD2 contains brain mask and mapping
information for each subject. We can use this information in our pipeline, by
inserting this information as `mapping_definition` and `brain_mask_definition`
inputs to the `GroupAFQ` class initializer. When initializing this object, we
will also ask for the fwDTI scalars to be computed. For expedience, we will
limit our investigation to the bilateral arcuate fasciculus and track only
around that bundle. If you would like to do this for all bundles, you would
remove the `bundle_dict` and `tracking_params` inputs to the initializer that
are provided below.

.. GENERATED FROM PYTHON SOURCE LINES 55-78

.. code-block:: Python


    brain_mask_definition = ImageFile(
        suffix="mask",
        filters={'desc': 'brain',
                 'space': 'T1w',
                 'scope': 'qsiprep'})

    bundle_names = ["Left Arcuate", "Right Arcuate"]
    bundle_dict = abd.default18_bd()[bundle_names]

    myafq = GroupAFQ(
        bids_path=study_dir,
        preproc_pipeline='qsiprep',
        output_dir=op.join(study_dir, "derivatives", "afq_fwdti"),
        bundle_info=bundle_dict,
        tracking_params={
            "n_seeds": 50000,
            "random_seeds": True,
            "seed_mask": RoiImage(use_waypoints=True, use_endpoints=True),
        },
        brain_mask_definition=brain_mask_definition,
        scalars=["fwdti_fa", "fwdti_md", "fwdti_fwf", "dti_fa", "dti_md"])





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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 79-83

Compare fwDTI and DTI maps
-------------------------
First, we take a look at the maps for the FA and MD calculated using the two
models

.. GENERATED FROM PYTHON SOURCE LINES 83-106

.. code-block:: Python


    fwFA = nib.load(myafq.export("fwdti_fa")["NDARAA948VFH"]).get_fdata()
    FA = nib.load(myafq.export("dti_fa")["NDARAA948VFH"]).get_fdata()

    fig, ax = plt.subplots(1, 2)
    ax[0].matshow(FA[:, :, FA.shape[-1] // 2], cmap='gray')
    ax[0].axis("off")

    ax[1].matshow(fwFA[:, :, fwFA.shape[-1] // 2], cmap='gray')
    ax[1].axis("off")


    fwMD = nib.load(myafq.export("fwdti_md")["NDARAA948VFH"]).get_fdata()
    MD = nib.load(myafq.export("dti_md")["NDARAA948VFH"]).get_fdata()

    fig, ax = plt.subplots(1, 2)
    ax[0].matshow(MD[:, :, MD.shape[-1] // 2], cmap='gray', vmax=0.005)
    ax[0].axis("off")

    ax[1].matshow(fwMD[:, :, fwMD.shape[-1] // 2], cmap='gray', vmax=0.005)
    ax[1].axis("off")





.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_001.png
         :alt: plot afq fwdti
         :srcset: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_002.png
         :alt: plot afq fwdti
         :srcset: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none


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    (-0.5, 128.5, 107.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 107-113

Free-water fraction map
-------------------------
In addition to the standard tensor scalars, provided by the fwDTI model, this
model also computes a free-water fraction, which is a number between 0 and 1
that assesses the fraction of the voxel signal that is explained by the free
water compartment.

.. GENERATED FROM PYTHON SOURCE LINES 113-119

.. code-block:: Python


    fwf = nib.load(myafq.export("fwdti_fwf")["NDARAA948VFH"]).get_fdata()
    fig, ax = plt.subplots()
    ax.matshow(fwf[:, :, fwf.shape[-1] // 2], cmap='gray')
    ax.axis("off")




.. image-sg:: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_003.png
   :alt: plot afq fwdti
   :srcset: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (-0.5, 128.5, 107.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 120-127

Comparing bundle profiles
-------------------------
Exporting the profiles will create a CSV file that contains information about
node-by-node values of the scalars computed with both models. Here, we read in
this information with Pandas and plot a comparison. As you can see, when free
water is accounted for with the fwDTI model, FA along the bundle is higher and
MD is lower than that estimated with the standard DTI model.

.. GENERATED FROM PYTHON SOURCE LINES 127-150

.. code-block:: Python


    profiles_csv = myafq.export("profiles")['NDARAA948VFH']
    profiles = pd.read_csv(profiles_csv)

    fig, ax = plt.subplots(3, 2)
    for ii, bundle in enumerate(["Left Arcuate", "Right Arcuate"]):
        ax[0, ii].plot(profiles[profiles["tractID"] == bundle]["fwdti_fa"],
                       label="fwDTI")
        ax[0, ii].plot(profiles[profiles["tractID"] == bundle]["dti_fa"],
                       label="DTI")
        ax[0, ii].set_ylabel("FA")
        ax[0, ii].legend()
        ax[1, ii].plot(profiles[profiles["tractID"] == bundle]["fwdti_md"],
                       label="fwDTI")
        ax[1, ii].plot(profiles[profiles["tractID"] == bundle]["dti_md"],
                       label="DTI")
        ax[1, ii].set_ylabel("MD")
        ax[1, ii].legend()
        ax[2, ii].plot(profiles[profiles["tractID"] == bundle]["fwdti_fwf"])
        ax[2, ii].set_ylabel("Free water fraction")
        ax[2, ii].set_xlabel("Distance along the bundle (A => P)")





.. image-sg:: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_004.png
   :alt: plot afq fwdti
   :srcset: /howto/howto_examples/images/sphx_glr_plot_afq_fwdti_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


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    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]

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.. GENERATED FROM PYTHON SOURCE LINES 151-170

References
----------

.. [1] Hoy AR, Koay CG, Kecskemeti SR, Alexander AL. Optimization of a free
    water elimination two-compartment model for diffusion tensor imaging.
    Neuroimage. 2014;103:323-333.

.. [2] Henriques RN, Rokem A, Garyfallidis E, St-Jean S, Peterson ET, Correia
    MM. [Re] Optimization of a free water elimination two-compartment model
    for diffusion tensor imaging. bioRxiv. February 2017:108795.
    doi:10.1101/108795

.. [3] Alexander LM, Escalera J, Ai L, et al. An open resource for
    transdiagnostic research in pediatric mental health and learning
    disorders. Sci Data. 2017;4:170181.

.. [4] Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and
    quality controlled resource for pediatric brain white-matter research.
    Scientific Data. 2022;9(1):1-27.


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

   **Total running time of the script:** (30 minutes 51.315 seconds)

**Estimated memory usage:**  3416 MB


.. _sphx_glr_download_howto_howto_examples_plot_afq_fwdti.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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