def test_DKI_crossing_fibers_simulations():
""" Testing DKI simulations of a crossing fiber
"""
# two fiber not aligned to planes x = 0, y = 0, or z = 0
mevals = np.array([[0.00099, 0, 0], [0.00226, 0.00087, 0.00087],
[0.00099, 0, 0], [0.00226, 0.00087, 0.00087]])
angles = [(80, 10), (80, 10), (20, 30), (20, 30)]
fie = 0.49
frac = [fie*50, (1 - fie)*50, fie*50, (1 - fie)*50]
signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac, snr=None)
# in this simulations dt and kt cannot have zero elements
for i in range(len(dt)):
assert dt[i] != 0
for i in range(len(kt)):
assert kt[i] != 0
# test S, dt and kt relative to the expected values computed from another
# DKI package - UDKI (Neto Henriques et al., 2015)
dt_ref = [1.0576161e-3, 0.1292542e-3, 0.4786179e-3,
0.2667081e-3, 0.1136643e-3, 0.9888660e-3]
kt_ref = [2.3529944, 0.8226448, 2.3011221, 0.2017312, -0.0437535,
0.0404011, 0.0355281, 0.2449859, 0.2157668, 0.3495910,
0.0413366, 0.3461519, -0.0537046, 0.0133414, -0.017441]
assert_array_almost_equal(dt, dt_ref)
assert_array_almost_equal(kt, kt_ref)
assert_array_almost_equal(signal,
dki_signal(gtab_2s, dt_ref, kt_ref, S0=1.,
snr=None),
decimal=5)
def test_DKI_crossing_fibers_simulations():
""" Testing DKI simulations of a crossing fiber
"""
# two fiber not aligned to planes x = 0, y = 0, or z = 0
mevals = np.array([[0.00099, 0, 0], [0.00226, 0.00087, 0.00087],
[0.00099, 0, 0], [0.00226, 0.00087, 0.00087]])
angles = [(80, 10), (80, 10), (20, 30), (20, 30)]
fie = 0.49
frac = [fie * 50, (1 - fie) * 50, fie * 50, (1 - fie) * 50]
signal, dt, kt = multi_tensor_dki(gtab_2s,
mevals,
angles=angles,
fractions=frac,
snr=None)
# in this simulations dt and kt cannot have zero elements
for i in range(len(dt)):
assert dt[i] != 0
for i in range(len(kt)):
assert kt[i] != 0
# test S, dt and kt relative to the expected values computed from another
# DKI package - UDKI (Neto Henriques et al., 2015)
dt_ref = [
1.0576161e-3, 0.1292542e-3, 0.4786179e-3, 0.2667081e-3, 0.1136643e-3,
0.9888660e-3
]
kt_ref = [
2.3529944, 0.8226448, 2.3011221, 0.2017312, -0.0437535, 0.0404011,
0.0355281, 0.2449859, 0.2157668, 0.3495910, 0.0413366, 0.3461519,
-0.0537046, 0.0133414, -0.017441
]
assert_array_almost_equal(dt, dt_ref)
assert_array_almost_equal(kt, kt_ref)
assert_array_almost_equal(signal,
dki_signal(gtab_2s,
dt_ref,
kt_ref,
S0=1.,
snr=None),
decimal=5)
def test_DKI_simulations_aligned_fibers():
"""
Testing DKI simulations when aligning the same fiber to different axis.
If biological parameters don't change, kt[0] of a fiber aligned to axis x
has to be equal to kt[1] of a fiber aligned to the axis y and equal to
kt[2] of a fiber aligned to axis z. The same is applicable for dt
"""
# Defining parameters based on Neto Henriques et al., 2015. NeuroImage 111
mevals = np.array([
[0.00099, 0, 0], # Intra-cellular
[0.00226, 0.00087, 0.00087]
]) # Extra-cellular
frac = [49, 51] # Compartment volume fraction
# axis x
angles = [(90, 0), (90, 0)]
signal_fx, dt_fx, kt_fx = multi_tensor_dki(gtab_2s,
mevals,
angles=angles,
fractions=frac)
# axis y
angles = [(90, 90), (90, 90)]
signal_fy, dt_fy, kt_fy = multi_tensor_dki(gtab_2s,
mevals,
angles=angles,
fractions=frac)
# axis z
angles = [(0, 0), (0, 0)]
signal_fz, dt_fz, kt_fz = multi_tensor_dki(gtab_2s,
mevals,
angles=angles,
fractions=frac)
assert_array_equal([kt_fx[0], kt_fx[1], kt_fx[2]],
[kt_fy[1], kt_fy[0], kt_fy[2]])
assert_array_equal([kt_fx[0], kt_fx[1], kt_fx[2]],
[kt_fz[2], kt_fz[0], kt_fz[1]])
assert_array_equal([dt_fx[0], dt_fx[2], dt_fx[5]],
[dt_fy[2], dt_fy[0], dt_fy[5]])
assert_array_equal([dt_fx[0], dt_fx[2], dt_fx[5]],
[dt_fz[5], dt_fz[0], dt_fz[2]])
# testing S signal along axis x, y and z
bvals = np.array([0, 0, 0, 1000, 1000, 1000, 2000, 2000, 2000])
bvecs = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0],
[0, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]])
gtab_axis = gradient_table(bvals, bvecs)
# axis x
S_fx = dki_signal(gtab_axis, dt_fx, kt_fx, S0=100)
assert_array_almost_equal(S_fx[0:3], [100, 100, 100]) # test S f0r b=0
# axis y
S_fy = dki_signal(gtab_axis, dt_fy, kt_fy, S0=100)
assert_array_almost_equal(S_fy[0:3], [100, 100, 100]) # test S f0r b=0
# axis z
S_fz = dki_signal(gtab_axis, dt_fz, kt_fz, S0=100)
assert_array_almost_equal(S_fz[0:3], [100, 100, 100]) # test S f0r b=0
# test S for b = 1000
assert_array_almost_equal([S_fx[3], S_fx[4], S_fx[5]],
[S_fy[4], S_fy[3], S_fy[5]])
assert_array_almost_equal([S_fx[3], S_fx[4], S_fx[5]],
[S_fz[5], S_fz[3], S_fz[4]])
# test S for b = 2000
assert_array_almost_equal([S_fx[6], S_fx[7], S_fx[8]],
[S_fy[7], S_fy[6], S_fy[8]])
assert_array_almost_equal([S_fx[6], S_fx[7], S_fx[8]],
[S_fz[8], S_fz[6], S_fz[7]])
def test_DKI_simulations_aligned_fibers():
"""
Testing DKI simulations when aligning the same fiber to different axis.
If biological parameters don't change, kt[0] of a fiber aligned to axis x
has to be equal to kt[1] of a fiber aligned to the axis y and equal to
kt[2] of a fiber aligned to axis z. The same is applicable for dt
"""
# Defining parameters based on Neto Henriques et al., 2015. NeuroImage 111
mevals = np.array([[0.00099, 0, 0], # Intra-cellular
[0.00226, 0.00087, 0.00087]]) # Extra-cellular
frac = [49, 51] # Compartment volume fraction
# axis x
angles = [(90, 0), (90, 0)]
signal_fx, dt_fx, kt_fx = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac)
# axis y
angles = [(90, 90), (90, 90)]
signal_fy, dt_fy, kt_fy = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac)
# axis z
angles = [(0, 0), (0, 0)]
signal_fz, dt_fz, kt_fz = multi_tensor_dki(gtab_2s, mevals, angles=angles,
fractions=frac)
assert_array_equal([kt_fx[0], kt_fx[1], kt_fx[2]],
[kt_fy[1], kt_fy[0], kt_fy[2]])
assert_array_equal([kt_fx[0], kt_fx[1], kt_fx[2]],
[kt_fz[2], kt_fz[0], kt_fz[1]])
assert_array_equal([dt_fx[0], dt_fx[2], dt_fx[5]],
[dt_fy[2], dt_fy[0], dt_fy[5]])
assert_array_equal([dt_fx[0], dt_fx[2], dt_fx[5]],
[dt_fz[5], dt_fz[0], dt_fz[2]])
# testing S signal along axis x, y and z
bvals = np.array([0, 0, 0, 1000, 1000, 1000, 2000, 2000, 2000])
bvecs = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1],
[1, 0, 0], [0, 1, 0], [0, 0, 1],
[1, 0, 0], [0, 1, 0], [0, 0, 1]])
gtab_axis = gradient_table(bvals, bvecs)
# axis x
S_fx = dki_signal(gtab_axis, dt_fx, kt_fx, S0=100)
assert_array_almost_equal(S_fx[0:3], [100, 100, 100]) # test S f0r b=0
# axis y
S_fy = dki_signal(gtab_axis, dt_fy, kt_fy, S0=100)
assert_array_almost_equal(S_fy[0:3], [100, 100, 100]) # test S f0r b=0
# axis z
S_fz = dki_signal(gtab_axis, dt_fz, kt_fz, S0=100)
assert_array_almost_equal(S_fz[0:3], [100, 100, 100]) # test S f0r b=0
# test S for b = 1000
assert_array_almost_equal([S_fx[3], S_fx[4], S_fx[5]],
[S_fy[4], S_fy[3], S_fy[5]])
assert_array_almost_equal([S_fx[3], S_fx[4], S_fx[5]],
[S_fz[5], S_fz[3], S_fz[4]])
# test S for b = 2000
assert_array_almost_equal([S_fx[6], S_fx[7], S_fx[8]],
[S_fy[7], S_fy[6], S_fy[8]])
assert_array_almost_equal([S_fx[6], S_fx[7], S_fx[8]],
[S_fz[8], S_fz[6], S_fz[7]])
