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

.. _example_reconst_shore_metrics:


===========================
Calculate SHORE scalar maps
===========================

We show how to calculate two SHORE-based scalar maps: return to origin
probability (rtop) [Descoteaux2011]_ and mean square displacement (msd)
[Wu2007]_, [Wu2008]_ on your data. SHORE can be used with any multiple b-value
dataset like multi-shell or DSI.

First import the necessary modules:

::
  
  import nibabel as nib
  import numpy as np
  import matplotlib.pyplot as plt
  from dipy.data import fetch_taiwan_ntu_dsi, read_taiwan_ntu_dsi, get_sphere
  from dipy.data import get_data, dsi_voxels
  from dipy.reconst.shore import ShoreModel
  

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.

::
  
  asm = ShoreModel(gtab)
  

Lets just use only one slice only from the data.

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

Fit the signal with the model and calculate the SHORE coefficients.

::
  
  asmfit = asm.fit(dataslice)
  

Calculate the analytical rtop on the signal
that corresponds to the integral of the signal.

::
  
  print('Calculating... rtop_signal')
  rtop_signal = asmfit.rtop_signal()
  

Now we calculate the analytical rtop on the propagator,
that corresponds to its central value.

::
  
  print('Calculating... rtop_pdf')
  rtop_pdf = asmfit.rtop_pdf()

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

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

::
  
  print('Calculating... msd')
  msd = asmfit.msd()
  

Show the maps and save them in SHORE_maps.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')
  ax2.set_axis_off()
  ind = ax2.imshow(rtop_pdf.T, interpolation='nearest', origin='lower')
  plt.colorbar(ind)
  ax3 = fig.add_subplot(2, 2, 3, title='msd')
  ax3.set_axis_off()
  ind = ax3.imshow(msd.T, interpolation='nearest', origin='lower', vmin=0)
  plt.colorbar(ind)
  plt.savefig('SHORE_maps.png')
  

.. figure:: SHORE_maps.png
   :align: center

   **rtop and msd calculated using the SHORE model**.


.. [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_shore_metrics.py>`.
   This same script is also included in the dipy source distribution under the
   :file:`doc/examples/` directory.

