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

.. _example_reconst_dsi_metrics:


===============================
Calculate DSI-based scalar maps
===============================

We show how to calculate two DSI-based scalar maps: return to origin
probability (rtop) [Descoteaux2011]_ and mean square displacement (msd)
[Wu2007]_, [Wu2008]_ on your dataset.

First import the necessary modules:

::
  
  import numpy as np
  import matplotlib.pyplot as plt
  from dipy.data import fetch_taiwan_ntu_dsi, read_taiwan_ntu_dsi
  from dipy.reconst.dsi import DiffusionSpectrumModel
  

Download and read the data for this tutorial.

::
  
  fetch_taiwan_ntu_dsi()
  img, gtab = read_taiwan_ntu_dsi()
  

img contains a nibabel Nifti1Image object (data) and gtab contains a GradientTable
object (gradient information e.g. b-values). For example to read the b-values
it is possible to write print(gtab.bvals).

Load the raw diffusion data and the affine.

::
  
  data = img.get_data()
  affine = img.get_affine()
  print('data.shape (%d, %d, %d, %d)' % data.shape)
  

Instantiate the Model and apply it to the data.

::
  
  dsmodel = DiffusionSpectrumModel(gtab, qgrid_size=35, filter_width=18.5)
  

Lets just use one slice only from the data.

::
  
  dataslice = data[30:70, 20:80, data.shape[2] / 2]
  

Normalize the signal by the b0

::
  
  dataslice = dataslice / (dataslice[..., 0, None]).astype(np.float)
  

Calculate the return to origin probability on the signal
that corresponds to the integral of the signal.

::
  
  print('Calculating... rtop_signal')
  rtop_signal = dsmodel.fit(dataslice).rtop_signal()
  

Now we calculate the return to origin probability on the propagator,
that corresponds to its central value.
By default the propagator is divided by its sum in order to obtain a properly normalized pdf,
however this normalization changes the values of rtop, therefore in order to compare it
with the rtop previously calculated on the signal we turn the normalized parameter to false.

::
  
  print('Calculating... rtop_pdf')
  rtop_pdf = dsmodel.fit(dataslice).rtop_pdf(normalized=False)
  

In theory, these two measures must be equal,
to show that we calculate the mean square error on this two measures.

::
  
  mse = np.sum((rtop_signal - rtop_pdf) ** 2) / rtop_signal.size
  print("mse = %f" % mse)
  

mse = 0.000000

Leaving the normalized parameter to the default changes the values of the
rtop but not the contrast between the voxels.

::
  
  print('Calculating... rtop_pdf_norm')
  rtop_pdf_norm = dsmodel.fit(dataslice).rtop_pdf()
  

Let's calculate the mean square displacement on the normalized propagator.

::
  
  print('Calculating... msd_norm')
  msd_norm = dsmodel.fit(dataslice).msd_discrete()
  

Turning the normalized parameter to false makes it possible to calculate
the mean square displacement on the propagator without normalization.

::
  
  print('Calculating... msd')
  msd = dsmodel.fit(dataslice).msd_discrete(normalized=False)
  

Show the rtop images and save them in rtop.png.

::
  
  fig = plt.figure(figsize=(6, 6))
  ax1 = fig.add_subplot(2, 2, 1, title='rtop_signal')
  ax1.set_axis_off()
  ind = ax1.imshow(rtop_signal.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  ax2 = fig.add_subplot(2, 2, 2, title='rtop_pdf_norm')
  ax2.set_axis_off()
  ind = ax2.imshow(rtop_pdf_norm.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  ax3 = fig.add_subplot(2, 2, 3, title='rtop_pdf')
  ax3.set_axis_off()
  ind = ax3.imshow(rtop_pdf.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  plt.savefig('rtop.png')
  

.. figure:: rtop.png
   :align: center

   **Return to origin probability**.

Show the msd images and save them in msd.png.

::
  
  fig = plt.figure(figsize=(7, 3))
  ax1 = fig.add_subplot(1, 2, 1, title='msd_norm')
  ax1.set_axis_off()
  ind = ax1.imshow(msd_norm.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  ax2 = fig.add_subplot(1, 2, 2, title='msd')
  ax2.set_axis_off()
  ind = ax2.imshow(msd.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  plt.savefig('msd.png')
  

.. figure:: msd.png
   :align: center

   **Mean square displacement**.

.. [Descoteaux2011] Descoteaux M. et. al , "Multiple q-shell diffusion
					propagator imaging", Medical Image Analysis, vol 15,
					No. 4, p. 603-621, 2011.

.. [Wu2007] Wu Y. et al., "Hybrid diffusion imaging", NeuroImage, vol 36,
        	p. 617-629, 2007.

.. [Wu2008] Wu Y. et al., "Computation of Diffusion Function Measures
			in q -Space Using Magnetic Resonance Hybrid Diffusion Imaging",
			IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 27, No. 6, p. 858-865,
			2008

.. include:: ../links_names.inc


        
.. admonition:: Example source code

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

