/usr/lib/python2.7/dist-packages/dipy/reconst/tests/test_dki.py is in python-dipy 0.10.1-1.
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from __future__ import division, print_function, absolute_import
import numpy as np
import random
import dipy.reconst.dki as dki
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_almost_equal)
from nose.tools import assert_raises
from dipy.sims.voxel import multi_tensor_dki
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.data import get_data
from dipy.reconst.dti import (from_lower_triangular, decompose_tensor)
from dipy.reconst.dki import (mean_kurtosis, carlson_rf, carlson_rd,
axial_kurtosis, radial_kurtosis, _positive_evals)
from dipy.core.sphere import Sphere
from dipy.core.geometry import perpendicular_directions
fimg, fbvals, fbvecs = get_data('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
# 2 shells for techniques that requires multishell data
bvals_2s = np.concatenate((bvals, bvals * 2), axis=0)
bvecs_2s = np.concatenate((bvecs, bvecs), axis=0)
gtab_2s = gradient_table(bvals_2s, bvecs_2s)
# Simulation 1. signals of two crossing fibers are simulated
mevals_cross = np.array([[0.00099, 0, 0], [0.00226, 0.00087, 0.00087],
[0.00099, 0, 0], [0.00226, 0.00087, 0.00087]])
angles_cross = [(80, 10), (80, 10), (20, 30), (20, 30)]
fie = 0.49
frac_cross = [fie*50, (1-fie) * 50, fie*50, (1-fie) * 50]
# Noise free simulates
signal_cross, dt_cross, kt_cross = multi_tensor_dki(gtab_2s, mevals_cross,
S0=100,
angles=angles_cross,
fractions=frac_cross,
snr=None)
evals_cross, evecs_cross = decompose_tensor(from_lower_triangular(dt_cross))
crossing_ref = np.concatenate((evals_cross, evecs_cross[0], evecs_cross[1],
evecs_cross[2], kt_cross), axis=0)
# Simulation 2. Spherical kurtosis tensor.- for white matter, this can be a
# biological implaussible scenario, however this simulation is usefull for
# testing the estimation of directional apparent kurtosis and the mean
# kurtosis, since its directional and mean kurtosis ground truth are a constant
# which can be easly mathematicaly calculated.
Di = 0.00099
De = 0.00226
mevals_sph = np.array([[Di, Di, Di], [De, De, De]])
frac_sph = [50, 50]
signal_sph, dt_sph, kt_sph = multi_tensor_dki(gtab_2s, mevals_sph, S0=100,
fractions=frac_sph,
snr=None)
evals_sph, evecs_sph = decompose_tensor(from_lower_triangular(dt_sph))
params_sph = np.concatenate((evals_sph, evecs_sph[0], evecs_sph[1],
evecs_sph[2], kt_sph), axis=0)
# Compute ground truth - since KT is spherical, appparent kurtosic coeficient
# for all gradient directions and mean kurtosis have to be equal to Kref_sph.
f = 0.5
Dg = f*Di + (1-f)*De
Kref_sphere = 3 * f * (1-f) * ((Di-De) / Dg) ** 2
# Simulation 3. Multi-voxel simulations - dataset of four voxels is simulated.
# Since the objective of this simulation is to see if procedures are able to
# work with multi-dimentional data all voxels contains the same crossing signal
# produced in simulation 1.
DWI = np.zeros((2, 2, 1, len(gtab_2s.bvals)))
DWI[0, 0, 0] = DWI[0, 1, 0] = DWI[1, 0, 0] = DWI[1, 1, 0] = signal_cross
multi_params = np.zeros((2, 2, 1, 27))
multi_params[0, 0, 0] = multi_params[0, 1, 0] = crossing_ref
multi_params[1, 0, 0] = multi_params[1, 1, 0] = crossing_ref
def test_positive_evals():
# Tested evals
L1 = np.array([[1e-3, 1e-3, 2e-3], [0, 1e-3, 0]])
L2 = np.array([[3e-3, 0, 2e-3], [1e-3, 1e-3, 0]])
L3 = np.array([[4e-3, 1e-4, 0], [0, 1e-3, 0]])
# only the first voxels have all eigenvalues larger than zero, thus:
expected_ind = np.array([[True, False, False], [False, True, False]],
dtype=bool)
# test function _positive_evals
ind = _positive_evals(L1, L2, L3)
assert_array_equal(ind, expected_ind)
def test_split_dki_param():
dkiM = dki.DiffusionKurtosisModel(gtab_2s, fit_method="OLS")
dkiF = dkiM.fit(DWI)
evals, evecs, kt = dki.split_dki_param(dkiF.model_params)
assert_array_almost_equal(evals, dkiF.evals)
assert_array_almost_equal(evecs, dkiF.evecs)
assert_array_almost_equal(kt, dkiF.kt)
def test_dki_fits():
""" DKI fits are tested on noise free crossing fiber simulates """
# OLS fitting
dkiM = dki.DiffusionKurtosisModel(gtab_2s, fit_method="OLS")
dkiF = dkiM.fit(signal_cross)
assert_array_almost_equal(dkiF.model_params, crossing_ref)
# WLS fitting
dki_wlsM = dki.DiffusionKurtosisModel(gtab_2s, fit_method="WLS")
dki_wlsF = dki_wlsM.fit(signal_cross)
assert_array_almost_equal(dki_wlsF.model_params, crossing_ref)
# testing multi-voxels
dkiF_multi = dkiM.fit(DWI)
assert_array_almost_equal(dkiF_multi.model_params, multi_params)
dkiF_multi = dki_wlsM.fit(DWI)
assert_array_almost_equal(dkiF_multi.model_params, multi_params)
def test_apparent_kurtosis_coef():
""" Apparent kurtosis coeficients are tested for a spherical kurtosis
tensor """
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
AKC = dki.apparent_kurtosis_coef(params_sph, sph)
# check all direction
for d in range(len(gtab.bvecs[gtab.bvals > 0])):
assert_array_almost_equal(AKC[d], Kref_sphere)
def test_dki_predict():
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
pred = dkiM.predict(crossing_ref, S0=100)
assert_array_almost_equal(pred, signal_cross)
# just to check that it works with more than one voxel:
pred_multi = dkiM.predict(multi_params, S0=100)
assert_array_almost_equal(pred_multi, DWI)
# check the function predict of the DiffusionKurtosisFit object
dkiF = dkiM.fit(DWI)
pred_multi = dkiF.predict(gtab_2s, S0=100)
assert_array_almost_equal(pred_multi, DWI)
dkiF = dkiM.fit(pred_multi)
pred_from_fit = dkiF.predict(dkiM.gtab, S0=100)
assert_array_almost_equal(pred_from_fit, DWI)
def test_carlson_rf():
# Define inputs that we know the outputs from:
# Carlson, B.C., 1994. Numerical computation of real or complex
# elliptic integrals. arXiv:math/9409227 [math.CA]
# Real values (test in 2D format)
x = np.array([[1.0, 0.5], [2.0, 2.0]])
y = np.array([[2.0, 1.0], [3.0, 3.0]])
z = np.array([[0.0, 0.0], [4.0, 4.0]])
# Defene reference outputs
RF_ref = np.array([[1.3110287771461, 1.8540746773014],
[0.58408284167715, 0.58408284167715]])
# Compute integrals
RF = carlson_rf(x, y, z)
# Compare
assert_array_almost_equal(RF, RF_ref)
# Complex values
x = np.array([1j, 1j - 1, 1j, 1j - 1])
y = np.array([-1j, 1j, -1j, 1j])
z = np.array([0.0, 0.0, 2, 1 - 1j])
# Defene reference outputs
RF_ref = np.array([1.8540746773014, 0.79612586584234 - 1.2138566698365j,
1.0441445654064, 0.93912050218619 - 0.53296252018635j])
# Compute integrals
RF = carlson_rf(x, y, z, errtol=3e-5)
# Compare
assert_array_almost_equal(RF, RF_ref)
def test_carlson_rd():
# Define inputs that we know the outputs from:
# Carlson, B.C., 1994. Numerical computation of real or complex
# elliptic integrals. arXiv:math/9409227 [math.CA]
# Real values
x = np.array([0.0, 2.0])
y = np.array([2.0, 3.0])
z = np.array([1.0, 4.0])
# Defene reference outputs
RD_ref = np.array([1.7972103521034, 0.16510527294261])
# Compute integrals
RD = carlson_rd(x, y, z, errtol=1e-5)
# Compare
assert_array_almost_equal(RD, RD_ref)
# Complex values (testing in 2D format)
x = np.array([[1j, 0.0], [0.0, -2 - 1j]])
y = np.array([[-1j, 1j], [1j-1, -1j]])
z = np.array([[2.0, -1j], [1j, -1 + 1j]])
# Defene reference outputs
RD_ref = np.array([[0.65933854154220, 1.2708196271910 + 2.7811120159521j],
[-1.8577235439239 - 0.96193450888839j,
1.8249027393704 - 1.2218475784827j]])
# Compute integrals
RD = carlson_rd(x, y, z, errtol=1e-5)
# Compare
assert_array_almost_equal(RD, RD_ref)
def test_Wrotate_single_fiber():
# Rotate the kurtosis tensor of single fiber simulate to the diffusion
# tensor diagonal and check that is equal to the kurtosis tensor of the
# same single fiber simulated directly to the x-axis
# Define single fiber simulate
mevals = np.array([[0.00099, 0, 0], [0.00226, 0.00087, 0.00087]])
fie = 0.49
frac = [fie*100, (1 - fie)*100]
# simulate single fiber not aligned to the x-axis
theta = random.uniform(0, 180)
phi = random.uniform(0, 320)
angles = [(theta, phi), (theta, phi)]
signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac, snr=None)
evals, evecs = decompose_tensor(from_lower_triangular(dt))
kt_rotated = dki.Wrotate(kt, evecs)
# Now coordinate system has the DT diagonal aligned to the x-axis
# Reference simulation in which DT diagonal is directly aligned to the
# x-axis
angles = (90, 0), (90, 0)
signal, dt_ref, kt_ref = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac, snr=None)
assert_array_almost_equal(kt_rotated, kt_ref)
def test_Wrotate_crossing_fibers():
# Test 2 - simulate crossing fibers intersecting at 70 degrees.
# In this case, diffusion tensor principal eigenvector will be aligned in
# the middle of the crossing fibers. Thus, after rotating the kurtosis
# tensor, this will be equal to a kurtosis tensor simulate of crossing
# fibers both deviating 35 degrees from the x-axis. Moreover, we know that
# crossing fibers will be aligned to the x-y plane, because the smaller
# diffusion eigenvalue, perpendicular to both crossings fibers, will be
# aligned to the z-axis.
# Simulate the crossing fiber
angles = [(90, 30), (90, 30), (20, 30), (20, 30)]
fie = 0.49
frac = [fie*50, (1-fie) * 50, fie*50, (1-fie) * 50]
mevals = np.array([[0.00099, 0, 0], [0.00226, 0.00087, 0.00087],
[0.00099, 0, 0], [0.00226, 0.00087, 0.00087]])
signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac, snr=None)
evals, evecs = decompose_tensor(from_lower_triangular(dt))
kt_rotated = dki.Wrotate(kt, evecs)
# Now coordinate system has diffusion tensor diagonal aligned to the x-axis
# Simulate the reference kurtosis tensor
angles = [(90, 35), (90, 35), (90, -35), (90, -35)]
signal, dt, kt_ref = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac, snr=None)
# Compare rotated with the reference
assert_array_almost_equal(kt_rotated, kt_ref)
def test_Wcons():
# Construct the 4D kurtosis tensor manualy from the crossing fiber kt
# simulate
Wfit = np.zeros([3, 3, 3, 3])
# Wxxxx
Wfit[0, 0, 0, 0] = kt_cross[0]
# Wyyyy
Wfit[1, 1, 1, 1] = kt_cross[1]
# Wzzzz
Wfit[2, 2, 2, 2] = kt_cross[2]
# Wxxxy
Wfit[0, 0, 0, 1] = Wfit[0, 0, 1, 0] = Wfit[0, 1, 0, 0] = kt_cross[3]
Wfit[1, 0, 0, 0] = kt_cross[3]
# Wxxxz
Wfit[0, 0, 0, 2] = Wfit[0, 0, 2, 0] = Wfit[0, 2, 0, 0] = kt_cross[4]
Wfit[2, 0, 0, 0] = kt_cross[4]
# Wxyyy
Wfit[0, 1, 1, 1] = Wfit[1, 0, 1, 1] = Wfit[1, 1, 1, 0] = kt_cross[5]
Wfit[1, 1, 0, 1] = kt_cross[5]
# Wxxxz
Wfit[1, 1, 1, 2] = Wfit[1, 2, 1, 1] = Wfit[2, 1, 1, 1] = kt_cross[6]
Wfit[1, 1, 2, 1] = kt_cross[6]
# Wxzzz
Wfit[0, 2, 2, 2] = Wfit[2, 2, 2, 0] = Wfit[2, 0, 2, 2] = kt_cross[7]
Wfit[2, 2, 0, 2] = kt_cross[7]
# Wyzzz
Wfit[1, 2, 2, 2] = Wfit[2, 2, 2, 1] = Wfit[2, 1, 2, 2] = kt_cross[8]
Wfit[2, 2, 1, 2] = kt_cross[8]
# Wxxyy
Wfit[0, 0, 1, 1] = Wfit[0, 1, 0, 1] = Wfit[0, 1, 1, 0] = kt_cross[9]
Wfit[1, 0, 0, 1] = Wfit[1, 0, 1, 0] = Wfit[1, 1, 0, 0] = kt_cross[9]
# Wxxzz
Wfit[0, 0, 2, 2] = Wfit[0, 2, 0, 2] = Wfit[0, 2, 2, 0] = kt_cross[10]
Wfit[2, 0, 0, 2] = Wfit[2, 0, 2, 0] = Wfit[2, 2, 0, 0] = kt_cross[10]
# Wyyzz
Wfit[1, 1, 2, 2] = Wfit[1, 2, 1, 2] = Wfit[1, 2, 2, 1] = kt_cross[11]
Wfit[2, 1, 1, 2] = Wfit[2, 2, 1, 1] = Wfit[2, 1, 2, 1] = kt_cross[11]
# Wxxyz
Wfit[0, 0, 1, 2] = Wfit[0, 0, 2, 1] = Wfit[0, 1, 0, 2] = kt_cross[12]
Wfit[0, 1, 2, 0] = Wfit[0, 2, 0, 1] = Wfit[0, 2, 1, 0] = kt_cross[12]
Wfit[1, 0, 0, 2] = Wfit[1, 0, 2, 0] = Wfit[1, 2, 0, 0] = kt_cross[12]
Wfit[2, 0, 0, 1] = Wfit[2, 0, 1, 0] = Wfit[2, 1, 0, 0] = kt_cross[12]
# Wxyyz
Wfit[0, 1, 1, 2] = Wfit[0, 1, 2, 1] = Wfit[0, 2, 1, 1] = kt_cross[13]
Wfit[1, 0, 1, 2] = Wfit[1, 1, 0, 2] = Wfit[1, 1, 2, 0] = kt_cross[13]
Wfit[1, 2, 0, 1] = Wfit[1, 2, 1, 0] = Wfit[2, 0, 1, 1] = kt_cross[13]
Wfit[2, 1, 0, 1] = Wfit[2, 1, 1, 0] = Wfit[1, 0, 2, 1] = kt_cross[13]
# Wxyzz
Wfit[0, 1, 2, 2] = Wfit[0, 2, 1, 2] = Wfit[0, 2, 2, 1] = kt_cross[14]
Wfit[1, 0, 2, 2] = Wfit[1, 2, 0, 2] = Wfit[1, 2, 2, 0] = kt_cross[14]
Wfit[2, 0, 1, 2] = Wfit[2, 0, 2, 1] = Wfit[2, 1, 0, 2] = kt_cross[14]
Wfit[2, 1, 2, 0] = Wfit[2, 2, 0, 1] = Wfit[2, 2, 1, 0] = kt_cross[14]
# Function to be tested
W4D = dki.Wcons(kt_cross)
Wfit = Wfit.reshape(-1)
W4D = W4D.reshape(-1)
assert_array_almost_equal(W4D, Wfit)
def test_spherical_dki_statistics():
# tests if MK, AK and RK are equal to expected values of a spherical
# kurtosis tensor
# Define multi voxel spherical kurtosis simulations
MParam = np.zeros((2, 2, 2, 27))
MParam[0, 0, 0] = MParam[0, 0, 1] = MParam[0, 1, 0] = params_sph
MParam[0, 1, 1] = MParam[1, 1, 0] = params_sph
# MParam[1, 1, 1], MParam[1, 0, 0], and MParam[1, 0, 1] remains zero
MRef = np.zeros((2, 2, 2))
MRef[0, 0, 0] = MRef[0, 0, 1] = MRef[0, 1, 0] = Kref_sphere
MRef[0, 1, 1] = MRef[1, 1, 0] = Kref_sphere
MRef[1, 1, 1] = MRef[1, 0, 0] = MRef[1, 0, 1] = 0
# Mean kurtosis analytical solution
MK_multi = mean_kurtosis(MParam)
assert_array_almost_equal(MK_multi, MRef)
# radial kurtosis analytical solution
RK_multi = radial_kurtosis(MParam)
assert_array_almost_equal(RK_multi, MRef)
# axial kurtosis analytical solution
AK_multi = axial_kurtosis(MParam)
assert_array_almost_equal(AK_multi, MRef)
def test_compare_MK_method():
# tests if analytical solution of MK is equal to the average of directional
# kurtosis sampled from a sphere
# DKI Model fitting
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
dkiF = dkiM.fit(signal_cross)
# MK analytical solution
MK_as = dkiF.mk()
# MK numerical method
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
MK_nm = np.mean(dki.apparent_kurtosis_coef(dkiF.model_params, sph),
axis=-1)
assert_array_almost_equal(MK_as, MK_nm, decimal=1)
def test_single_voxel_DKI_stats():
# tests if AK and RK are equal to expected values for a single fiber
# simulate randomly oriented
ADi = 0.00099
ADe = 0.00226
RDi = 0
RDe = 0.00087
# Reference values
AD = fie*ADi + (1-fie)*ADe
AK = 3 * fie * (1-fie) * ((ADi-ADe) / AD) ** 2
RD = fie*RDi + (1-fie)*RDe
RK = 3 * fie * (1-fie) * ((RDi-RDe) / RD) ** 2
ref_vals = np.array([AD, AK, RD, RK])
# simulate fiber randomly oriented
theta = random.uniform(0, 180)
phi = random.uniform(0, 320)
angles = [(theta, phi), (theta, phi)]
mevals = np.array([[ADi, RDi, RDi], [ADe, RDe, RDe]])
frac = [fie*100, (1-fie)*100]
signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, S0=100, angles=angles,
fractions=frac, snr=None)
evals, evecs = decompose_tensor(from_lower_triangular(dt))
dki_par = np.concatenate((evals, evecs[0], evecs[1], evecs[2], kt), axis=0)
# Estimates using dki functions
ADe1 = dki.axial_diffusivity(evals)
RDe1 = dki.radial_diffusivity(evals)
AKe1 = axial_kurtosis(dki_par)
RKe1 = radial_kurtosis(dki_par)
e1_vals = np.array([ADe1, AKe1, RDe1, RKe1])
assert_array_almost_equal(e1_vals, ref_vals)
# Estimates using the kurtosis class object
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
dkiF = dkiM.fit(signal)
e2_vals = np.array([dkiF.ad, dkiF.ak(), dkiF.rd, dkiF.rk()])
assert_array_almost_equal(e2_vals, ref_vals)
# test MK (note this test correspond to the MK singularity L2==L3)
MK_as = dkiF.mk()
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
MK_nm = np.mean(dkiF.akc(sph))
assert_array_almost_equal(MK_as, MK_nm, decimal=1)
def test_compare_RK_methods():
# tests if analytical solution of RK is equal to the perpendicular kurtosis
# relative to the first diffusion axis
# DKI Model fitting
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
dkiF = dkiM.fit(signal_cross)
# MK analytical solution
RK_as = dkiF.rk()
# MK numerical method
evecs = dkiF.evecs
p_dir = perpendicular_directions(evecs[:, 0], num=30, half=True)
ver = Sphere(xyz=p_dir)
RK_nm = np.mean(dki.apparent_kurtosis_coef(dkiF.model_params, ver),
axis=-1)
assert_array_almost_equal(RK_as, RK_nm)
def test_MK_singularities():
# To test MK in case that analytical solution was a singularity not covered
# by other tests
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
# test singularity L1 == L2 - this is the case of a prolate diffusion
# tensor for crossing fibers at 90 degrees
angles_all = np.array([[(90, 0), (90, 0), (0, 0), (0, 0)],
[(89.9, 0), (89.9, 0), (0, 0), (0, 0)]])
for angles_90 in angles_all:
s_90, dt_90, kt_90 = multi_tensor_dki(gtab_2s, mevals_cross, S0=100,
angles=angles_90,
fractions=frac_cross, snr=None)
dkiF = dkiM.fit(s_90)
MK = dkiF.mk()
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
MK_nm = np.mean(dkiF.akc(sph))
assert_almost_equal(MK, MK_nm, decimal=2)
# test singularity L1 == L3 and L1 != L2
# since L1 is defined as the larger eigenvalue and L3 the smallest
# eigenvalue, this singularity teoretically will never be called,
# because for L1 == L3, L2 have also to be = L1 and L2.
# Nevertheless, I decided to include this test since this singularity
# is revelant for cases that eigenvalues are not ordered
# artificially revert the eigenvalue and eigenvector order
dki_params = dkiF.model_params.copy()
dki_params[1] = dkiF.model_params[2]
dki_params[2] = dkiF.model_params[1]
dki_params[4] = dkiF.model_params[5]
dki_params[5] = dkiF.model_params[4]
dki_params[7] = dkiF.model_params[8]
dki_params[8] = dkiF.model_params[7]
dki_params[10] = dkiF.model_params[11]
dki_params[11] = dkiF.model_params[10]
MK = dki.mean_kurtosis(dki_params)
MK_nm = np.mean(dki.apparent_kurtosis_coef(dki_params, sph))
assert_almost_equal(MK, MK_nm, decimal=2)
def test_dki_errors():
# first error of DKI module is if a unknown fit method is given
assert_raises(ValueError, dki.DiffusionKurtosisModel, gtab_2s,
fit_method="JOANA")
# second error of DKI module is if a min_signal is defined as negative
assert_raises(ValueError, dki.DiffusionKurtosisModel, gtab_2s,
min_signal=-1)
# try case with correct min_signal
dkiM = dki.DiffusionKurtosisModel(gtab_2s, min_signal=1)
dkiF = dkiM.fit(DWI)
assert_array_almost_equal(dkiF.model_params, multi_params)
# third error is if a given mask do not have same shape as data
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
# test a correct mask
dkiF = dkiM.fit(DWI)
mask_correct = dkiF.fa > 0
mask_correct[1, 1] = False
multi_params[1, 1] = np.zeros(27)
mask_not_correct = np.array([[True, True, False], [True, False, False]])
dkiF = dkiM.fit(DWI, mask=mask_correct)
assert_array_almost_equal(dkiF.model_params, multi_params)
# test a incorrect mask
assert_raises(ValueError, dkiM.fit, DWI, mask=mask_not_correct)
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