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===========================
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
"""
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