.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_tracking_bootstrap_peaks:


====================================================
Bootstrap and Closest Peak Direction Getters Example
====================================================

This example shows how choices in direction-getter impact fiber
tracking results by demonstrating the bootstrap direction getter (a type of
probabilistic tracking, as described in Berman et al. (2008) [Berman2008]_ a
nd the closest peak direction getter (a type of deterministic tracking).
(Amirbekian, PhD thesis, 2016)

This example is an extension of the :ref:`example_tracking_introduction_eudx`
example. Let's start by loading the necessary modules for executing this
tutorial.

::
  
  # Enables/disables interactive visualization
  interactive = False
  
  from dipy.data import read_stanford_labels, small_sphere
  from dipy.direction import BootDirectionGetter, ClosestPeakDirectionGetter
  from dipy.io.stateful_tractogram import Space, StatefulTractogram
  from dipy.io.streamline import save_trk
  from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
                                     auto_response)
  from dipy.reconst.shm import CsaOdfModel
  from dipy.tracking import utils
  from dipy.tracking.local_tracking import LocalTracking
  from dipy.tracking.streamline import Streamlines
  from dipy.tracking.stopping_criterion import ThresholdStoppingCriterion
  from dipy.viz import window, actor, colormap, has_fury
  
  
  hardi_img, gtab, labels_img = read_stanford_labels()
  data = hardi_img.get_data()
  labels = labels_img.get_data()
  affine = hardi_img.affine
  
  seed_mask = (labels == 2)
  white_matter = (labels == 1) | (labels == 2)
  seeds = utils.seeds_from_mask(seed_mask, affine, density=1)
  

Next, we fit the CSD model.

::
  
  response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
  csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
  csd_fit = csd_model.fit(data, mask=white_matter)
  

we use the CSA fit to calculate GFA, which will serve as our stopping
criterion.

::
  
  csa_model = CsaOdfModel(gtab, sh_order=6)
  gfa = csa_model.fit(data, mask=white_matter).gfa
  stopping_criterion = ThresholdStoppingCriterion(gfa, .25)
  

Next, we need to set up our two direction getters

::
  

Example #1: Bootstrap direction getter with CSD Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

::
  
  
  boot_dg_csd = BootDirectionGetter.from_data(data, csd_model, max_angle=30.,
                                              sphere=small_sphere)
  boot_streamline_generator = LocalTracking(boot_dg_csd, stopping_criterion,
                                            seeds, affine, step_size=.5)
  streamlines = Streamlines(boot_streamline_generator)
  sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
  save_trk(sft, "tractogram_bootstrap_dg.trk")
  
  if has_fury:
      r = window.Renderer()
      r.add(actor.line(streamlines, colormap.line_colors(streamlines)))
      window.record(r, out_path='tractogram_bootstrap_dg.png', size=(800, 800))
      if interactive:
          window.show(r)
  

.. figure:: tractogram_bootstrap_dg.png
   :align: center

   **Corpus Callosum Bootstrap Probabilistic Direction Getter**

We have created a bootstrapped probabilistic set of streamlines. If you repeat
the fiber tracking (keeping all inputs the same) you will NOT get exactly the
same set of streamlines.

::
  

Example #2: Closest peak direction getter with CSD Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

::
  
  
  pmf = csd_fit.odf(small_sphere).clip(min=0)
  peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30.,
                                                sphere=small_sphere)
  peak_streamline_generator = LocalTracking(peak_dg, stopping_criterion, seeds,
                                            affine, step_size=.5)
  streamlines = Streamlines(peak_streamline_generator)
  sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
  save_trk(sft, "closest_peak_dg_CSD.trk")
  
  if has_fury:
      r = window.Renderer()
      r.add(actor.line(streamlines, colormap.line_colors(streamlines)))
      window.record(r, out_path='tractogram_closest_peak_dg.png',
                    size=(800, 800))
      if interactive:
          window.show(r)
  

.. figure:: tractogram_closest_peak_dg.png
   :align: center

   **Corpus Callosum Closest Peak Deterministic Direction Getter**

We have created a set of streamlines using the closest peak direction getter,
which is a type of deterministic tracking. If you repeat the fiber tracking
(keeping all inputs the same) you will get exactly the same set of streamlines.

::
  
  

References
----------
.. [Berman2008] Berman, J. et al., Probabilistic streamline q-ball
tractography using the residual bootstrap, NeuroImage, vol 39, no 1, 2008

.. include:: ../links_names.inc



.. admonition:: Example source code

   You can download :download:`the full source code of this example <./tracking_bootstrap_peaks.py>`. This same script is also included in the dipy source distribution under the :file:`doc/examples/` directory.