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""" Classes and functions for fitting the diffusion kurtosis model """
from __future__ import division, print_function, absolute_import
import numpy as np
from dipy.reconst.dti import (TensorFit, mean_diffusivity, axial_diffusivity,
radial_diffusivity, from_lower_triangular,
lower_triangular, decompose_tensor,
_min_positive_signal)
from dipy.reconst.utils import dki_design_matrix as design_matrix
from dipy.utils.six.moves import range
from ..core.onetime import auto_attr
from .base import ReconstModel
from dipy.core.ndindex import ndindex
def _positive_evals(L1, L2, L3, er=2e-7):
""" Helper function that indentifies which voxels in a array have all
eigenvalues significantly larger than zero
Parameters
----------
L1 : ndarray
First independent variable of the integral.
L2 : ndarray
Second independent variable of the integral.
L3 : ndarray
Third independent variable of the integral.
er : float, optional
A eigenvalues is classified as larger than zero if it is larger than er
Returns
-------
ind : boolean (n,)
Array that marks the voxels that have all eigenvalues are larger than
zero.
"""
ind = np.logical_and(L1 > er, np.logical_and(L2 > er, L3 > er))
return ind
def carlson_rf(x, y, z, errtol=3e-4):
r""" Computes the Carlson's incomplete elliptic integral of the first kind
defined as:
.. math::
R_F = \frac{1}{2} \int_{0}^{\infty} \left [(t+x)(t+y)(t+z) \right ]
^{-\frac{1}{2}}dt
Parameters
----------
x : ndarray
First independent variable of the integral.
y : ndarray
Second independent variable of the integral.
z : ndarray
Third independent variable of the integral.
errtol : float
Error tolerance. Integral is computed with relative error less in
magnitude than the defined value
Returns
-------
RF : ndarray
Value of the incomplete first order elliptic integral
Note
-----
x, y, and z have to be nonnegative and at most one of them is zero.
References
----------
.. [1] Carlson, B.C., 1994. Numerical computation of real or complex
elliptic integrals. arXiv:math/9409227 [math.CA]
"""
xn = x.copy()
yn = y.copy()
zn = z.copy()
An = (xn + yn + zn) / 3.0
Q = (3.*errtol) ** (-1/6.) * np.max(np.abs([An - xn, An - yn, An - zn]),
axis=0)
# Convergence has to be done voxel by voxel
index = ndindex(x.shape)
for v in index:
n = 0
# Convergence condition
while 4.**(-n) * Q[v] > abs(An[v]):
xnroot = np.sqrt(xn[v])
ynroot = np.sqrt(yn[v])
znroot = np.sqrt(zn[v])
lamda = xnroot*(ynroot + znroot) + ynroot*znroot
n = n + 1
xn[v] = (xn[v]+lamda) * 0.250
yn[v] = (yn[v]+lamda) * 0.250
zn[v] = (zn[v]+lamda) * 0.250
An[v] = (An[v]+lamda) * 0.250
# post convergence calculation
X = 1. - xn/An
Y = 1. - yn/An
Z = - X - Y
E2 = X*Y - Z*Z
E3 = X * Y * Z
RF = An**(-1/2.) * (1 - E2/10. + E3/14. + (E2**2)/24. - 3/44.*E2*E3)
return RF
def carlson_rd(x, y, z, errtol=1e-4):
r""" Computes the Carlson's incomplete elliptic integral of the second kind
defined as:
.. math::
R_D = \frac{3}{2} \int_{0}^{\infty} (t+x)^{-\frac{1}{2}}
(t+y)^{-\frac{1}{2}}(t+z) ^{-\frac{3}{2}}
Parameters
----------
x : ndarray
First independent variable of the integral.
y : ndarray
Second independent variable of the integral.
z : ndarray
Third independent variable of the integral.
errtol : float
Error tolerance. Integral is computed with relative error less in
magnitude than the defined value
Returns
-------
RD : ndarray
Value of the incomplete second order elliptic integral
Note
-----
x, y, and z have to be nonnegative and at most x or y is zero.
"""
xn = x.copy()
yn = y.copy()
zn = z.copy()
A0 = (xn + yn + 3.*zn) / 5.0
An = A0.copy()
Q = (errtol/4.) ** (-1/6.) * np.max(np.abs([An - xn, An - yn, An - zn]),
axis=0)
sum_term = np.zeros(x.shape, dtype=x.dtype)
n = np.zeros(x.shape)
# Convergence has to be done voxel by voxel
index = ndindex(x.shape)
for v in index:
# Convergence condition
while 4.**(-n[v]) * Q[v] > abs(An[v]):
xnroot = np.sqrt(xn[v])
ynroot = np.sqrt(yn[v])
znroot = np.sqrt(zn[v])
lamda = xnroot*(ynroot + znroot) + ynroot*znroot
sum_term[v] = sum_term[v] + 4.**(-n[v]) / (znroot * (zn[v]+lamda))
n[v] = n[v] + 1
xn[v] = (xn[v]+lamda) * 0.250
yn[v] = (yn[v]+lamda) * 0.250
zn[v] = (zn[v]+lamda) * 0.250
An[v] = (An[v]+lamda) * 0.250
# post convergence calculation
X = (A0 - x) / (4.**(n) * An)
Y = (A0 - y) / (4.**(n) * An)
Z = - (X+Y) / 3.
E2 = X*Y - 6.*Z*Z
E3 = (3.*X*Y - 8.*Z*Z) * Z
E4 = 3. * (X*Y - Z*Z) * Z**2.
E5 = X * Y * Z**3.
RD = \
4**(-n) * An**(-3/2.) * \
(1 - 3/14.*E2 + 1/6.*E3 + 9/88.*(E2**2) - 3/22.*E4 - 9/52.*E2*E3 +
3/26.*E5) + 3*sum_term
return RD
def _F1m(a, b, c):
""" Helper function that computes function $F_1$ which is required to
compute the analytical solution of the Mean kurtosis.
Parameters
----------
a : ndarray
Array containing the values of parameter $\lambda_1$ of function $F_1$
b : ndarray
Array containing the values of parameter $\lambda_2$ of function $F_1$
c : ndarray
Array containing the values of parameter $\lambda_3$ of function $F_1$
Returns
-------
F1 : ndarray
Value of the function $F_1$ for all elements of the arrays a, b, and c
Notes
--------
Function $F_1$ is defined as [1]_:
.. math::
F_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{18(\lambda_1-\lambda_2)(\lambda_1-\lambda_3)}
[\frac{\sqrt{\lambda_2\lambda_3}}{\lambda_1}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{3\lambda_1^2-\lambda_1\lambda_2-\lambda_2\lambda_3-
\lambda_1\lambda_3}
{3\lambda_1 \sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-1 ]
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
# Eigenvalues are considered equal if they are not 2.5% different to each
# other. This value is adjusted according to the analysis reported in:
# http://gsoc2015dipydki.blogspot.co.uk/2015/08/rnh-post-13-start-wrapping-up-test.html
er = 2.5e-2
# Initialize F1
F1 = np.zeros(a.shape)
# Only computes F1 in voxels that have all eigenvalues larger than zero
cond0 = _positive_evals(a, b, c)
# Apply formula for non problematic plaussible cases, i.e. a!=b and a!=c
cond1 = np.logical_and(cond0, np.logical_and(abs(a - b) >= a * er,
abs(a - c) >= a * er))
if np.sum(cond1) != 0:
L1 = a[cond1]
L2 = b[cond1]
L3 = c[cond1]
RFm = carlson_rf(L1/L2, L1/L3, np.ones(len(L1)))
RDm = carlson_rd(L1/L2, L1/L3, np.ones(len(L1)))
F1[cond1] = ((L1+L2+L3) ** 2) / (18 * (L1-L2) * (L1-L3)) * \
(np.sqrt(L2*L3) / L1 * RFm +
(3 * L1**2 - L1*L2 - L1*L3 - L2*L3) /
(3 * L1 * np.sqrt(L2*L3)) * RDm - 1)
# Resolve possible sigularity a==b
cond2 = np.logical_and(cond0, np.logical_and(abs(a - b) < a * er,
abs(a - c) > a * er))
if np.sum(cond2) != 0:
L1 = (a[cond2]+b[cond2]) / 2.
L3 = c[cond2]
F1[cond2] = _F2m(L3, L1, L1) / 2.
# Resolve possible sigularity a==c
cond3 = np.logical_and(cond0, np.logical_and(abs(a - c) < a * er,
abs(a - b) > a * er))
if np.sum(cond3) != 0:
L1 = (a[cond3]+c[cond3]) / 2.
L2 = b[cond3]
F1[cond3] = _F2m(L2, L1, L1) / 2
# Resolve possible sigularity a==b and a==c
cond4 = np.logical_and(cond0, np.logical_and(abs(a - c) < a * er,
abs(a - b) < a * er))
if np.sum(cond4) != 0:
F1[cond4] = 1/5.
return F1
def _F2m(a, b, c):
""" Helper function that computes function $F_2$ which is required to
compute the analytical solution of the Mean kurtosis.
Parameters
----------
a : ndarray
Array containing the values of parameter $\lambda_1$ of function $F_2$
b : ndarray
Array containing the values of parameter $\lambda_2$ of function $F_2$
c : ndarray
Array containing the values of parameter $\lambda_3$ of function $F_2$
Returns
-------
F2 : ndarray
Value of the function $F_2$ for all elements of the arrays a, b, and c
Notes
--------
Function $F_2$ is defined as [1]_:
.. math::
F_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{3(\lambda_2-\lambda_3)^2}
[\frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{2\lambda_1-\lambda_2-\lambda_3}{3\sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-2]
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
# Eigenvalues are considered equal if they are not 2.5% different to each
# other. This value is adjusted according to the analysis reported in:
# http://gsoc2015dipydki.blogspot.co.uk/2015/08/rnh-post-13-start-wrapping-up-test.html
er = 2.5e-2
# Initialize F2
F2 = np.zeros(a.shape)
# Only computes F2 in voxels that have all eigenvalues larger than zero
cond0 = _positive_evals(a, b, c)
# Apply formula for non problematic plaussible cases, i.e. b!=c
cond1 = np.logical_and(cond0, (abs(b - c) > b * er))
if np.sum(cond1) != 0:
L1 = a[cond1]
L2 = b[cond1]
L3 = c[cond1]
RF = carlson_rf(L1/L2, L1/L3, np.ones(len(L1)))
RD = carlson_rd(L1/L2, L1/L3, np.ones(len(L1)))
F2[cond1] = (((L1+L2+L3) ** 2) / (3. * (L2-L3) ** 2)) * \
(((L2+L3) / (np.sqrt(L2*L3))) * RF +
((2.*L1-L2-L3) / (3.*np.sqrt(L2*L3))) * RD - 2.)
# Resolve possible sigularity b==c
cond2 = np.logical_and(cond0, np.logical_and(abs(b - c) < b * er,
abs(a - b) > b * er))
if np.sum(cond2) != 0:
L1 = a[cond2]
L3 = (c[cond2]+b[cond2]) / 2.
# Cumpute alfa [1]_
x = 1. - (L1/L3)
alpha = np.zeros(len(L1))
for i in range(len(x)):
if x[i] > 0:
alpha[i] = 1./np.sqrt(x[i]) * np.arctanh(np.sqrt(x[i]))
else:
alpha[i] = 1./np.sqrt(-x[i]) * np.arctan(np.sqrt(-x[i]))
F2[cond2] = \
6. * ((L1 + 2.*L3)**2) / (144. * L3**2 * (L1-L3)**2) * \
(L3 * (L1 + 2.*L3) + L1 * (L1 - 4.*L3) * alpha)
# Resolve possible sigularity a==b and a==c
cond3 = np.logical_and(cond0, np.logical_and(abs(b - c) < b * er,
abs(a - b) < b * er))
if np.sum(cond3) != 0:
F2[cond3] = 6/15.
return F2
def apparent_kurtosis_coef(dki_params, sphere, min_diffusivity=0,
min_kurtosis=-1):
r""" Calculate the apparent kurtosis coefficient (AKC) in each direction
of a sphere.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvectors respectively
3) Fifteen elements of the kurtosis tensor
sphere : a Sphere class instance
The AKC will be calculated for each of the vertices in the sphere
min_diffusivity : float (optional)
Because negative eigenvalues are not physical and small eigenvalues
cause quite a lot of noise in diffusion based metrics, diffusivity
values smaller than `min_diffusivity` are replaced with
`min_diffusivity`. defaut = 0
min_kurtosis : float (optional)
Because high amplitude negative values of kurtosis are not physicaly
and biologicaly pluasible, and these causes huge artefacts in kurtosis
based measures, directional kurtosis values than `min_kurtosis` are
replaced with `min_kurtosis`. defaut = -1
Returns
--------
AKC : ndarray (x, y, z, g) or (n, g)
Apparent kurtosis coefficient (AKC) for all g directions of a sphere.
Notes
-----
For each sphere direction with coordinates $(n_{1}, n_{2}, n_{3})$, the
calculation of AKC is done using formula:
.. math ::
AKC(n)=\frac{MD^{2}}{ADC(n)^{2}}\sum_{i=1}^{3}\sum_{j=1}^{3}
\sum_{k=1}^{3}\sum_{l=1}^{3}n_{i}n_{j}n_{k}n_{l}W_{ijkl}
where $W_{ijkl}$ are the elements of the kurtosis tensor, MD the mean
diffusivity and ADC the apparent diffusion coefficent computed as:
.. math ::
ADC(n)=\sum_{i=1}^{3}\sum_{j=1}^{3}n_{i}n_{j}D_{ij}
where $D_{ij}$ are the elements of the diffusion tensor.
"""
# Flat parameters
outshape = dki_params.shape[:-1]
dki_params = dki_params.reshape((-1, dki_params.shape[-1]))
# Split data
evals, evecs, kt = split_dki_param(dki_params)
# Initialize AKC matrix
V = sphere.vertices
AKC = np.zeros((len(kt), len(V)))
# select relevant voxels to process
rel_i = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])
kt = kt[rel_i]
evecs = evecs[rel_i]
evals = evals[rel_i]
AKCi = AKC[rel_i]
# Compute MD
MD = mean_diffusivity(evals)
# loop over all relevant voxels
for vox in range(len(kt)):
R = evecs[vox]
dt = lower_triangular(np.dot(np.dot(R, np.diag(evals[vox])), R.T))
AKCi[vox] = _directional_kurtosis(dt, MD[vox], kt[vox], V,
min_diffusivity=min_diffusivity,
min_kurtosis=min_kurtosis)
# reshape data according to input data
AKC[rel_i] = AKCi
return AKC.reshape((outshape + (len(V),)))
def _directional_kurtosis(dt, MD, kt, V, min_diffusivity=0, min_kurtosis=-1):
r""" Helper function that calculate the apparent kurtosis coefficient (AKC)
in each direction of a sphere for a single voxel.
Parameters
----------
dt : array (6,)
elements of the diffusion tensor of the voxel.
MD : float
mean diffusivity of the voxel
kt : array (15,)
elements of the kurtosis tensor of the voxel.
V : array (g, 3)
g directions of a Sphere in Cartesian coordinates
min_diffusivity : float (optional)
Because negative eigenvalues are not physical and small eigenvalues
cause quite a lot of noise in diffusion based metrics, diffusivity
values smaller than `min_diffusivity` are replaced with
`min_diffusivity`. defaut = 0
min_kurtosis : float (optional)
Because high amplitude negative values of kurtosis are not physicaly
and biologicaly pluasible, and these causes huge artefacts in kurtosis
based measures, directional kurtosis values than `min_kurtosis` are
replaced with `min_kurtosis`. defaut = -1
Returns
--------
AKC : ndarray (g,)
Apparent kurtosis coefficient (AKC) in all g directions of a sphere for
a single voxel.
See Also
--------
apparent_kurtosis_coef
"""
ADC = \
V[:, 0] * V[:, 0] * dt[0] + \
2 * V[:, 0] * V[:, 1] * dt[1] + \
V[:, 1] * V[:, 1] * dt[2] + \
2 * V[:, 0] * V[:, 2] * dt[3] + \
2 * V[:, 1] * V[:, 2] * dt[4] + \
V[:, 2] * V[:, 2] * dt[5]
if min_diffusivity is not None:
ADC = ADC.clip(min=min_diffusivity)
AKC = \
V[:, 0] * V[:, 0] * V[:, 0] * V[:, 0] * kt[0] + \
V[:, 1] * V[:, 1] * V[:, 1] * V[:, 1] * kt[1] + \
V[:, 2] * V[:, 2] * V[:, 2] * V[:, 2] * kt[2] + \
4 * V[:, 0] * V[:, 0] * V[:, 0] * V[:, 1] * kt[3] + \
4 * V[:, 0] * V[:, 0] * V[:, 0] * V[:, 2] * kt[4] + \
4 * V[:, 0] * V[:, 1] * V[:, 1] * V[:, 1] * kt[5] + \
4 * V[:, 1] * V[:, 1] * V[:, 1] * V[:, 2] * kt[6] + \
4 * V[:, 0] * V[:, 2] * V[:, 2] * V[:, 2] * kt[7] + \
4 * V[:, 1] * V[:, 2] * V[:, 2] * V[:, 2] * kt[8] + \
6 * V[:, 0] * V[:, 0] * V[:, 1] * V[:, 1] * kt[9] + \
6 * V[:, 0] * V[:, 0] * V[:, 2] * V[:, 2] * kt[10] + \
6 * V[:, 1] * V[:, 1] * V[:, 2] * V[:, 2] * kt[11] + \
12 * V[:, 0] * V[:, 0] * V[:, 1] * V[:, 2] * kt[12] + \
12 * V[:, 0] * V[:, 1] * V[:, 1] * V[:, 2] * kt[13] + \
12 * V[:, 0] * V[:, 1] * V[:, 2] * V[:, 2] * kt[14]
if min_kurtosis is not None:
AKC = AKC.clip(min=min_kurtosis)
return (MD/ADC) ** 2 * AKC
def mean_kurtosis(dki_params, min_kurtosis=0, max_kurtosis=3):
r""" Computes mean Kurtosis (MK) from the kurtosis tensor.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, mean
kurtosis values that are smaller than `min_kurtosis` are replaced with
`min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, mean
kurtosis values that are larger than `max_kurtosis` are replaced with
`max_kurtosis`. defaut = 3
Returns
-------
mk : array
Calculated MK.
Notes
--------
The MK analytical solution is calculated using the following equation [1]_:
.. math::
MK=F_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{1111}+
F_1(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{2222}+
F_1(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{3333}+ \\
F_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}+
F_2(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{1133}+
F_2(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{1122}
where $\hat{W}_{ijkl}$ are the components of the $W$ tensor in the
coordinates system defined by the eigenvectors of the diffusion tensor
$\mathbf{D}$ and
F_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{18(\lambda_1-\lambda_2)(\lambda_1-\lambda_3)}
[\frac{\sqrt{\lambda_2\lambda_3}}{\lambda_1}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{3\lambda_1^2-\lambda_1\lambda_2-\lambda_2\lambda_3-
\lambda_1\lambda_3}
{3\lambda_1 \sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-1 ]
F_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{3(\lambda_2-\lambda_3)^2}
[\frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{2\lambda_1-\lambda_2-\lambda_3}{3\sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-2]
where $R_f$ and $R_d$ are the Carlson's elliptic integrals.
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
# Flat parameters. For numpy versions more recent than 1.6.0, this step
# isn't required
outshape = dki_params.shape[:-1]
dki_params = dki_params.reshape((-1, dki_params.shape[-1]))
# Split the model parameters to three variable containing the evals, evecs,
# and kurtosis elements
evals, evecs, kt = split_dki_param(dki_params)
# Rotate the kurtosis tensor from the standard Cartesian coordinate system
# to another coordinate system in which the 3 orthonormal eigenvectors of
# DT are the base coordinate
Wxxxx = Wrotate_element(kt, 0, 0, 0, 0, evecs)
Wyyyy = Wrotate_element(kt, 1, 1, 1, 1, evecs)
Wzzzz = Wrotate_element(kt, 2, 2, 2, 2, evecs)
Wxxyy = Wrotate_element(kt, 0, 0, 1, 1, evecs)
Wxxzz = Wrotate_element(kt, 0, 0, 2, 2, evecs)
Wyyzz = Wrotate_element(kt, 1, 1, 2, 2, evecs)
# Compute MK
MK = \
_F1m(evals[..., 0], evals[..., 1], evals[..., 2])*Wxxxx + \
_F1m(evals[..., 1], evals[..., 0], evals[..., 2])*Wyyyy + \
_F1m(evals[..., 2], evals[..., 1], evals[..., 0])*Wzzzz + \
_F2m(evals[..., 0], evals[..., 1], evals[..., 2])*Wyyzz + \
_F2m(evals[..., 1], evals[..., 0], evals[..., 2])*Wxxzz + \
_F2m(evals[..., 2], evals[..., 1], evals[..., 0])*Wxxyy
if min_kurtosis is not None:
MK = MK.clip(min=min_kurtosis)
if max_kurtosis is not None:
MK = MK.clip(max=max_kurtosis)
return MK.reshape(outshape)
def _G1m(a, b, c):
""" Helper function that computes function $G_1$ which is required to
compute the analytical solution of the Radial kurtosis.
Parameters
----------
a : ndarray
Array containing the values of parameter $\lambda_1$ of function $G_1$
b : ndarray
Array containing the values of parameter $\lambda_2$ of function $G_1$
c : ndarray
Array containing the values of parameter $\lambda_3$ of function $G_1$
Returns
-------
G1 : ndarray
Value of the function $G_1$ for all elements of the arrays a, b, and c
Notes
--------
Function $G_1$ is defined as [1]_:
.. math::
G_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{18\lambda_2(\lambda_2-
\lambda_3)} \left (2\lambda_2 +
\frac{\lambda_3^2-3\lambda_2\lambda_3}{\sqrt{\lambda_2\lambda_3}}
\right)
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
# Float error used to compare two floats, abs(l1 - l2) < er for l1 = l2
# Error is defined as three order of magnitude larger than system's epslon
er = np.finfo(a.ravel()[0]).eps * 1e3
# Initialize G1
G1 = np.zeros(a.shape)
# Only computes G1 in voxels that have all eigenvalues larger than zero
cond0 = _positive_evals(a, b, c)
# Apply formula for non problematic plaussible cases, i.e. b!=c
cond1 = np.logical_and(cond0, (abs(b - c) > er))
if np.sum(cond1) != 0:
L1 = a[cond1]
L2 = b[cond1]
L3 = c[cond1]
G1[cond1] = \
(L1+L2+L3)**2 / (18 * L2 * (L2-L3)**2) * \
(2.*L2 + (L3**2 - 3*L2*L3) / np.sqrt(L2*L3))
# Resolve possible sigularity b==c
cond2 = np.logical_and(cond0, abs(b - c) < er)
if np.sum(cond2) != 0:
L1 = a[cond2]
L2 = b[cond2]
G1[cond2] = (L1 + 2.*L2)**2 / (24.*L2**2)
return G1
def _G2m(a, b, c):
""" Helper function that computes function $G_2$ which is required to
compute the analytical solution of the Radial kurtosis.
Parameters
----------
a : ndarray
Array containing the values of parameter $\lambda_1$ of function $G_2$
b : ndarray
Array containing the values of parameter $\lambda_2$ of function $G_2$
c : ndarray (n,)
Array containing the values of parameter $\lambda_3$ of function $G_2$
Returns
-------
G2 : ndarray
Value of the function $G_2$ for all elements of the arrays a, b, and c
Notes
--------
Function $G_2$ is defined as [1]_:
.. math::
G_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{(\lambda_2-\lambda_3)^2}
\left ( \frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}-2\right )
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
# Float error used to compare two floats, abs(l1 - l2) < er for l1 = l2
# Error is defined as three order of magnitude larger than system's epslon
er = np.finfo(a.ravel()[0]).eps * 1e3
# Initialize G2
G2 = np.zeros(a.shape)
# Only computes G2 in voxels that have all eigenvalues larger than zero
cond0 = _positive_evals(a, b, c)
# Apply formula for non problematic plaussible cases, i.e. b!=c
cond1 = np.logical_and(cond0, (abs(b - c) > er))
if np.sum(cond1) != 0:
L1 = a[cond1]
L2 = b[cond1]
L3 = c[cond1]
G2[cond1] = \
(L1+L2+L3)**2 / (3 * (L2-L3)**2) * ((L2+L3) / np.sqrt(L2*L3) - 2)
# Resolve possible sigularity b==c
cond2 = np.logical_and(cond0, abs(b - c) < er)
if np.sum(cond2) != 0:
L1 = a[cond2]
L2 = b[cond2]
G2[cond2] = (L1 + 2.*L2)**2 / (12.*L2**2)
return G2
def radial_kurtosis(dki_params, min_kurtosis=0, max_kurtosis=3):
r""" Radial Kurtosis (RK) of a diffusion kurtosis tensor.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, radial
kurtosis values that are smaller than `min_kurtosis` are replaced with
`min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, radial
kurtosis values that are larger than `max_kurtosis` are replaced with
`max_kurtosis`. defaut = 3
Returns
-------
rk : array
Calculated RK.
Notes
--------
RK is calculated with the following equation:
.. math::
K_{\bot} = G_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2222} +
G_1(\lambda_1,\lambda_3,\lambda_2)\hat{W}_{3333} +
G_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}
where:
.. math::
G_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{18\lambda_2(\lambda_2-
\lambda_3)} \left (2\lambda_2 +
\frac{\lambda_3^2-3\lambda_2\lambda_3}{\sqrt{\lambda_2\lambda_3}}
\right)
and
.. math::
G_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{(\lambda_2-\lambda_3)^2}
\left ( \frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}-2\right )
"""
# Flat parameters. For numpy versions more recent than 1.6.0, this step
# isn't required
outshape = dki_params.shape[:-1]
dki_params = dki_params.reshape((-1, dki_params.shape[-1]))
# Split the model parameters to three variable containing the evals, evecs,
# and kurtosis elements
evals, evecs, kt = split_dki_param(dki_params)
# Rotate the kurtosis tensor from the standard Cartesian coordinate system
# to another coordinate system in which the 3 orthonormal eigenvectors of
# DT are the base coordinate
Wyyyy = Wrotate_element(kt, 1, 1, 1, 1, evecs)
Wzzzz = Wrotate_element(kt, 2, 2, 2, 2, evecs)
Wyyzz = Wrotate_element(kt, 1, 1, 2, 2, evecs)
# Compute RK
RK = \
_G1m(evals[..., 0], evals[..., 1], evals[..., 2]) * Wyyyy + \
_G1m(evals[..., 0], evals[..., 2], evals[..., 1]) * Wzzzz + \
_G2m(evals[..., 0], evals[..., 1], evals[..., 2]) * Wyyzz
if min_kurtosis is not None:
RK = RK.clip(min=min_kurtosis)
if max_kurtosis is not None:
RK = RK.clip(max=max_kurtosis)
return RK.reshape(outshape)
def axial_kurtosis(dki_params, min_kurtosis=0, max_kurtosis=3):
r""" Computes axial Kurtosis (AK) from the kurtosis tensor.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are smaller than `min_kurtosis` are replaced with
`min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are larger than `max_kurtosis` are replaced with
`max_kurtosis`. defaut = 3
Returns
-------
ak : array
Calculated AK.
"""
# Flat parameters
outshape = dki_params.shape[:-1]
dki_params = dki_params.reshape((-1, dki_params.shape[-1]))
# Split data
evals, evecs, kt = split_dki_param(dki_params)
# Initialize AK
AK = np.zeros(kt.shape[:-1])
# select relevant voxels to process
rel_i = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])
kt = kt[rel_i]
evecs = evecs[rel_i]
evals = evals[rel_i]
AKi = AK[rel_i]
# Compute MD
MD = mean_diffusivity(evals)
# loop over all voxels
for vox in range(len(kt)):
R = evecs[vox]
dt = lower_triangular(np.dot(np.dot(R, np.diag(evals[vox])), R.T))
AKi[vox] = _directional_kurtosis(dt, MD[vox], kt[vox],
np.array([R[:, 0]]))
# reshape data according to input data
AK[rel_i] = AKi
if min_kurtosis is not None:
AK = AK.clip(min=min_kurtosis)
if max_kurtosis is not None:
AK = AK.clip(max=max_kurtosis)
return AK.reshape(outshape)
def dki_prediction(dki_params, gtab, S0=150):
""" Predict a signal given diffusion kurtosis imaging parameters.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
gtab : a GradientTable class instance
The gradient table for this prediction
S0 : float or ndarray (optional)
The non diffusion-weighted signal in every voxel, or across all
voxels. Default: 150
Returns
--------
S : (..., N) ndarray
Simulated signal based on the DKI model:
.. math::
S=S_{0}e^{-bD+\frac{1}{6}b^{2}D^{2}K}
"""
evals, evecs, kt = split_dki_param(dki_params)
# Define DKI design matrix according to given gtab
A = design_matrix(gtab)
# Flat parameters and initialize pred_sig
fevals = evals.reshape((-1, evals.shape[-1]))
fevecs = evecs.reshape((-1,) + evecs.shape[-2:])
fkt = kt.reshape((-1, kt.shape[-1]))
pred_sig = np.zeros((len(fevals), len(gtab.bvals)))
# lopping for all voxels
for v in range(len(pred_sig)):
DT = np.dot(np.dot(fevecs[v], np.diag(fevals[v])), fevecs[v].T)
dt = lower_triangular(DT)
MD = (dt[0] + dt[2] + dt[5]) / 3
X = np.concatenate((dt, fkt[v]*MD*MD, np.array([np.log(S0)])), axis=0)
pred_sig[v] = np.exp(np.dot(A, X))
# Reshape data according to the shape of dki_params
pred_sig = pred_sig.reshape(dki_params.shape[:-1] + (pred_sig.shape[-1],))
return pred_sig
class DiffusionKurtosisModel(ReconstModel):
""" Class for the Diffusion Kurtosis Model
"""
def __init__(self, gtab, fit_method="OLS", *args, **kwargs):
""" Diffusion Kurtosis Tensor Model [1]
Parameters
----------
gtab : GradientTable class instance
fit_method : str or callable
str can be one of the following:
'OLS' or 'ULLS' for ordinary least squares
dki.ols_fit_dki
'WLS' or 'UWLLS' for weighted ordinary least squares
dki.wls_fit_dki
callable has to have the signature:
fit_method(design_matrix, data, *args, **kwargs)
args, kwargs : arguments and key-word arguments passed to the
fit_method. See dki.ols_fit_dki, dki.wls_fit_dki for details
References
----------
[1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
ReconstModel.__init__(self, gtab)
if not callable(fit_method):
try:
self.fit_method = common_fit_methods[fit_method]
except KeyError:
raise ValueError('"' + str(fit_method) + '" is not a known '
'fit method, the fit method should either be '
'a function or one of the common fit methods')
self.design_matrix = design_matrix(self.gtab)
self.args = args
self.kwargs = kwargs
self.min_signal = self.kwargs.pop('min_signal', None)
if self.min_signal is not None and self.min_signal <= 0:
e_s = "The `min_signal` key-word argument needs to be strictly"
e_s += " positive."
raise ValueError(e_s)
def fit(self, data, mask=None):
""" Fit method of the DKI model class
Parameters
----------
data : array
The measured signal from one voxel.
mask : array
A boolean array used to mark the coordinates in the data that
should be analyzed that has the shape data.shape[-1]
"""
if mask is None:
# Flatten it to 2D either way:
data_in_mask = np.reshape(data, (-1, data.shape[-1]))
else:
# Check for valid shape of the mask
if mask.shape != data.shape[:-1]:
raise ValueError("Mask is not the same shape as data.")
mask = np.array(mask, dtype=bool, copy=False)
data_in_mask = np.reshape(data[mask], (-1, data.shape[-1]))
if self.min_signal is None:
min_signal = _min_positive_signal(data)
else:
min_signal = self.min_signal
data_in_mask = np.maximum(data_in_mask, min_signal)
params_in_mask = self.fit_method(self.design_matrix, data_in_mask,
*self.args, **self.kwargs)
if mask is None:
out_shape = data.shape[:-1] + (-1, )
dki_params = params_in_mask.reshape(out_shape)
else:
dki_params = np.zeros(data.shape[:-1] + (27,))
dki_params[mask, :] = params_in_mask
return DiffusionKurtosisFit(self, dki_params)
def predict(self, dki_params, S0=1):
""" Predict a signal for this DKI model class instance given
parameters.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the
first, second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
S0 : float or ndarray (optional)
The non diffusion-weighted signal in every voxel, or across all
voxels. Default: 1
"""
return dki_prediction(dki_params, self.gtab, S0)
class DiffusionKurtosisFit(TensorFit):
""" Class for fitting the Diffusion Kurtosis Model"""
def __init__(self, model, model_params):
""" Initialize a DiffusionKurtosisFit class instance.
Since DKI is an extension of DTI, class instance is defined as subclass
of the TensorFit from dti.py
Parameters
----------
model : DiffusionKurtosisModel Class instance
Class instance containing the Diffusion Kurtosis Model for the fit
model_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the
first, second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
"""
TensorFit.__init__(self, model, model_params)
@property
def kt(self):
"""
Returns the 15 independent elements of the kurtosis tensor as an array
"""
return self.model_params[..., 12:]
def akc(self, sphere):
r""" Calculate the apparent kurtosis coefficient (AKC) in each
direction on the sphere for each voxel in the data
Parameters
----------
sphere : Sphere class instance
Returns
-------
akc : ndarray
The estimates of the apparent kurtosis coefficient in every
direction on the input sphere
Notes
-----
For each sphere direction with coordinates $(n_{1}, n_{2}, n_{3})$, the
calculation of AKC is done using formula:
.. math ::
AKC(n)=\frac{MD^{2}}{ADC(n)^{2}}\sum_{i=1}^{3}\sum_{j=1}^{3}
\sum_{k=1}^{3}\sum_{l=1}^{3}n_{i}n_{j}n_{k}n_{l}W_{ijkl}
where $W_{ijkl}$ are the elements of the kurtosis tensor, MD the mean
diffusivity and ADC the apparent diffusion coefficent computed as:
.. math ::
ADC(n)=\sum_{i=1}^{3}\sum_{j=1}^{3}n_{i}n_{j}D_{ij}
where $D_{ij}$ are the elements of the diffusion tensor.
"""
return apparent_kurtosis_coef(self.model_params, sphere)
def mk(self, min_kurtosis=0, max_kurtosis=3):
r""" Computes mean Kurtosis (MK) from the kurtosis tensor.
Parameters
----------
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, mean
kurtosis values that are smaller than `min_kurtosis` are replaced
with `min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, mean
kurtosis values that are larger than `max_kurtosis` are replaced
with `max_kurtosis`. defaut = 3
Returns
-------
mk : array
Calculated MK.
Notes
--------
The MK analytical solution is calculated using the following equation
[1]_:
.. math::
MK=F_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{1111}+
F_1(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{2222}+
F_1(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{3333}+ \\
F_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}+
F_2(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{1133}+
F_2(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{1122}
where $\hat{W}_{ijkl}$ are the components of the $W$ tensor in the
coordinates system defined by the eigenvectors of the diffusion tensor
$\mathbf{D}$ and
.. math::
F_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{18(\lambda_1-\lambda_2)(\lambda_1-\lambda_3)}
[\frac{\sqrt{\lambda_2\lambda_3}}{\lambda_1}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{3\lambda_1^2-\lambda_1\lambda_2-\lambda_2\lambda_3-
\lambda_1\lambda_3}
{3\lambda_1 \sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-1 ]
and
.. math::
F_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}
{3(\lambda_2-\lambda_3)^2}
[\frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}
R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
\frac{2\lambda_1-\lambda_2-\lambda_3}{3\sqrt{\lambda_2 \lambda_3}}
R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-2]
where $R_f$ and $R_d$ are the Carlson's elliptic integrals.
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
return mean_kurtosis(self.model_params, min_kurtosis, max_kurtosis)
def ak(self, min_kurtosis=0, max_kurtosis=3):
r"""
Axial Kurtosis (AK) of a diffusion kurtosis tensor.
Parameters
----------
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are smaller than `min_kurtosis` are replaced
with `min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are larger than `max_kurtosis` are replaced
with `max_kurtosis`. defaut = 3
Returns
-------
ak : array
Calculated AK.
"""
return axial_kurtosis(self.model_params, min_kurtosis, max_kurtosis)
def rk(self, min_kurtosis=0, max_kurtosis=3):
r""" Radial Kurtosis (RK) of a diffusion kurtosis tensor.
Parameters
----------
min_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are smaller than `min_kurtosis` are replaced
with `min_kurtosis`. defaut = 0
max_kurtosis : float (optional)
To keep kurtosis values within a plausible biophysical range, axial
kurtosis values that are larger than `max_kurtosis` are replaced
with `max_kurtosis`. defaut = 3
Returns
-------
rk : array
Calculated RK.
Notes
------
RK is calculated with the following equation:
.. math::
K_{\bot} = G_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2222} +
G_1(\lambda_1,\lambda_3,\lambda_2)\hat{W}_{3333} +
G_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}
where:
.. math::
G_1(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{18\lambda_2(\lambda_2-
\lambda_3)} \left (2\lambda_2 +
\frac{\lambda_3^2-3\lambda_2\lambda_3}{\sqrt{\lambda_2\lambda_3}}
\right)
and
.. math::
G_2(\lambda_1,\lambda_2,\lambda_3)=
\frac{(\lambda_1+\lambda_2+\lambda_3)^2}{(\lambda_2-\lambda_3)^2}
\left ( \frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}-2
\right )
"""
return radial_kurtosis(self.model_params, min_kurtosis, max_kurtosis)
def predict(self, gtab, S0=1):
r""" Given a DKI model fit, predict the signal on the vertices of a
gradient table
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the
first, second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
gtab : a GradientTable class instance
The gradient table for this prediction
S0 : float or ndarray (optional)
The non diffusion-weighted signal in every voxel, or across all
voxels. Default: 1
Notes
-----
The predicted signal is given by:
.. math::
S(n,b)=S_{0}e^{-bD(n)+\frac{1}{6}b^{2}D(n)^{2}K(n)}
$\mathbf{D(n)}$ and $\mathbf{K(n)}$ can be computed from the DT and KT
using the following equations:
.. math::
D(n)=\sum_{i=1}^{3}\sum_{j=1}^{3}n_{i}n_{j}D_{ij}
and
.. math::
K(n)=\frac{MD^{2}}{D(n)^{2}}\sum_{i=1}^{3}\sum_{j=1}^{3}
\sum_{k=1}^{3}\sum_{l=1}^{3}n_{i}n_{j}n_{k}n_{l}W_{ijkl}
where $D_{ij}$ and $W_{ijkl}$ are the elements of the second-order DT
and the fourth-order KT tensors, respectively, and $MD$ is the mean
diffusivity.
"""
return dki_prediction(self.model_params, gtab, S0)
def ols_fit_dki(design_matrix, data):
r""" Computes ordinary least squares (OLS) fit to calculate the diffusion
tensor and kurtosis tensor using a linear regression diffusion kurtosis
model [1]_.
Parameters
----------
design_matrix : array (g, 22)
Design matrix holding the covariants used to solve for the regression
coefficients.
data : array (N, g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
Returns
-------
dki_params : array (N, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
See Also
--------
wls_fit_dki
References
----------
[1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
"""
tol = 1e-6
# preparing data and initializing parameters
data = np.asarray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dki_params = np.empty((len(data_flat), 27))
# inverting design matrix and defining minimun diffusion aloud
min_diffusivity = tol / -design_matrix.min()
inv_design = np.linalg.pinv(design_matrix)
# lopping OLS solution on all data voxels
for vox in range(len(data_flat)):
dki_params[vox] = _ols_iter(inv_design, data_flat[vox],
min_diffusivity)
# Reshape data according to the input data shape
dki_params = dki_params.reshape((data.shape[:-1]) + (27,))
return dki_params
def _ols_iter(inv_design, sig, min_diffusivity):
""" Helper function used by ols_fit_dki - Applies OLS fit of the diffusion
kurtosis model to single voxel signals.
Parameters
----------
inv_design : array (g, 22)
Inverse of the design matrix holding the covariants used to solve for
the regression coefficients.
sig : array (g,)
Diffusion-weighted signal for a single voxel data.
min_diffusivity : float
Because negative eigenvalues are not physical and small eigenvalues,
much smaller than the diffusion weighting, cause quite a lot of noise
in metrics such as fa, diffusivity values smaller than
`min_diffusivity` are replaced with `min_diffusivity`.
Returns
-------
dki_params : array (27,)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
"""
# DKI ordinary linear least square solution
log_s = np.log(sig)
result = np.dot(inv_design, log_s)
# Extracting the diffusion tensor parameters from solution
DT_elements = result[:6]
evals, evecs = decompose_tensor(from_lower_triangular(DT_elements),
min_diffusivity=min_diffusivity)
# Extracting kurtosis tensor parameters from solution
MD_square = (evals.mean(0))**2
KT_elements = result[6:21] / MD_square
# Write output
dki_params = np.concatenate((evals, evecs[0], evecs[1], evecs[2],
KT_elements), axis=0)
return dki_params
def wls_fit_dki(design_matrix, data):
r""" Computes weighted linear least squares (WLS) fit to calculate
the diffusion tensor and kurtosis tensor using a weighted linear
regression diffusion kurtosis model [1]_.
Parameters
----------
design_matrix : array (g, 22)
Design matrix holding the covariants used to solve for the regression
coefficients.
data : array (N, g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
min_signal : default = 1
All values below min_signal are repalced with min_signal. This is done
in order to avoid taking log(0) durring the tensor fitting.
Returns
-------
dki_params : array (N, 27)
All parameters estimated from the diffusion kurtosis model for all N
voxels.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
References
----------
[1] Veraart, J., Sijbers, J., Sunaert, S., Leemans, A., Jeurissen, B.,
2013. Weighted linear least squares estimation of diffusion MRI
parameters: Strengths, limitations, and pitfalls. Magn Reson Med 81,
335-346.
"""
tol = 1e-6
# preparing data and initializing parametres
data = np.asarray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dki_params = np.empty((len(data_flat), 27))
# inverting design matrix and defining minimun diffusion aloud
min_diffusivity = tol / -design_matrix.min()
inv_design = np.linalg.pinv(design_matrix)
# lopping WLS solution on all data voxels
for vox in range(len(data_flat)):
dki_params[vox] = _wls_iter(design_matrix, inv_design, data_flat[vox],
min_diffusivity)
# Reshape data according to the input data shape
dki_params = dki_params.reshape((data.shape[:-1]) + (27,))
return dki_params
def _wls_iter(design_matrix, inv_design, sig, min_diffusivity):
""" Helper function used by wls_fit_dki - Applies WLS fit of the diffusion
kurtosis model to single voxel signals.
Parameters
----------
design_matrix : array (g, 22)
Design matrix holding the covariants used to solve for the regression
coefficients
inv_design : array (g, 22)
Inverse of the design matrix.
sig : array (g, )
Diffusion-weighted signal for a single voxel data.
min_diffusivity : float
Because negative eigenvalues are not physical and small eigenvalues,
much smaller than the diffusion weighting, cause quite a lot of noise
in metrics such as fa, diffusivity values smaller than
`min_diffusivity` are replaced with `min_diffusivity`.
Returns
-------
dki_params : array (27, )
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
"""
A = design_matrix
# DKI ordinary linear least square solution
log_s = np.log(sig)
ols_result = np.dot(inv_design, log_s)
# Define weights as diag(yn**2)
W = np.diag(np.exp(2 * np.dot(A, ols_result)))
# DKI weighted linear least square solution
inv_AT_W_A = np.linalg.pinv(np.dot(np.dot(A.T, W), A))
AT_W_LS = np.dot(np.dot(A.T, W), log_s)
wls_result = np.dot(inv_AT_W_A, AT_W_LS)
# Extracting the diffusion tensor parameters from solution
DT_elements = wls_result[:6]
evals, evecs = decompose_tensor(from_lower_triangular(DT_elements),
min_diffusivity=min_diffusivity)
# Extracting kurtosis tensor parameters from solution
MD_square = (evals.mean(0))**2
KT_elements = wls_result[6:21] / MD_square
# Write output
dki_params = np.concatenate((evals, evecs[0], evecs[1], evecs[2],
KT_elements), axis=0)
return dki_params
def Wrotate(kt, Basis):
r""" Rotate a kurtosis tensor from the standard Cartesian coordinate system
to another coordinate system basis
Parameters
----------
kt : (15,)
Vector with the 15 independent elements of the kurtosis tensor
Basis : array (3, 3)
Vectors of the basis column-wise oriented
inds : array(m, 4) (optional)
Array of vectors containing the four indexes of m specific elements of
the rotated kurtosis tensor. If not specified all 15 elements of the
rotated kurtosis tensor are computed.
Returns
--------
Wrot : array (m,) or (15,)
Vector with the m independent elements of the rotated kurtosis tensor.
If 'indices' is not specified all 15 elements of the rotated kurtosis
tensor are computed.
Note
------
KT elements are assumed to be ordered as follows:
.. math::
\begin{matrix} ( & W_{xxxx} & W_{yyyy} & W_{zzzz} & W_{xxxy} & W_{xxxz}
& ... \\
& W_{xyyy} & W_{yyyz} & W_{xzzz} & W_{yzzz} & W_{xxyy}
& ... \\
& W_{xxzz} & W_{yyzz} & W_{xxyz} & W_{xyyz} & W_{xyzz}
& & )\end{matrix}
References
----------
[1] Hui ES, Cheung MM, Qi L, Wu EX, 2008. Towards better MR
characterization of neural tissues using directional diffusion kurtosis
analysis. Neuroimage 42(1): 122-34
"""
inds = np.array([[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2],
[0, 0, 0, 1], [0, 0, 0, 2], [0, 1, 1, 1],
[1, 1, 1, 2], [0, 2, 2, 2], [1, 2, 2, 2],
[0, 0, 1, 1], [0, 0, 2, 2], [1, 1, 2, 2],
[0, 0, 1, 2], [0, 1, 1, 2], [0, 1, 2, 2]])
Wrot = np.zeros(kt.shape)
for e in range(len(inds)):
Wrot[..., e] = Wrotate_element(kt, inds[e][0], inds[e][1], inds[e][2],
inds[e][3], Basis)
return Wrot
# Defining keys to select a kurtosis tensor element with indexes (i, j, k, l)
# on a kt vector that contains only the 15 independent elements of the kurtosis
# tensor: Considering y defined by (i+1) * (j+1) * (k+1) * (l+1). Two elements
# of the full 4D kurtosis tensor are equal if y obtain from the indexes of
# these two element are equal. Therefore, the possible values of y (1, 16, 81,
# 2, 3, 8, 24 27, 54, 4, 9, 36, 6, 12, 18) are used to point each element of
# the kurtosis tensor on the format of a vector containing the 15 independent
# elements.
ind_ele = {1: 0, 16: 1, 81: 2, 2: 3, 3: 4, 8: 5, 24: 6, 27: 7, 54: 8, 4: 9,
9: 10, 36: 11, 6: 12, 12: 13, 18: 14}
def Wrotate_element(kt, indi, indj, indk, indl, B):
r""" Computes the the specified index element of a kurtosis tensor rotated
to the coordinate system basis B.
Parameters
----------
kt : ndarray (x, y, z, 15) or (n, 15)
Array containing the 15 independent elements of the kurtosis tensor
indi : int
Rotated kurtosis tensor element index i (0 for x, 1 for y, 2 for z)
indj : int
Rotated kurtosis tensor element index j (0 for x, 1 for y, 2 for z)
indk : int
Rotated kurtosis tensor element index k (0 for x, 1 for y, 2 for z)
indl: int
Rotated kurtosis tensor element index l (0 for x, 1 for y, 2 for z)
B: array (x, y, z, 3, 3) or (n, 15)
Vectors of the basis column-wise oriented
Returns
-------
Wre : float
rotated kurtosis tensor element of index ind_i, ind_j, ind_k, ind_l
Note
-----
It is assumed that initial kurtosis tensor elementes are defined on the
Cartesian coordinate system.
References
----------
[1] Hui ES, Cheung MM, Qi L, Wu EX, 2008. Towards better MR
characterization of neural tissues using directional diffusion kurtosis
analysis. Neuroimage 42(1): 122-34
"""
Wre = 0
xyz = [0, 1, 2]
for il in xyz:
for jl in xyz:
for kl in xyz:
for ll in xyz:
key = (il+1) * (jl+1) * (kl+1) * (ll+1)
multiplyB = \
B[..., il, indi] * B[..., jl, indj] * \
B[..., kl, indk] * B[..., ll, indl]
Wre = Wre + multiplyB * kt[..., ind_ele[key]]
return Wre
def Wcons(k_elements):
r""" Construct the full 4D kurtosis tensors from its 15 independent
elements
Parameters
----------
k_elements : (15,)
elements of the kurtosis tensor in the following order:
.. math::
\begin{matrix} ( & W_{xxxx} & W_{yyyy} & W_{zzzz} & W_{xxxy} & W_{xxxz}
& ... \\
& W_{xyyy} & W_{yyyz} & W_{xzzz} & W_{yzzz} & W_{xxyy}
& ... \\
& W_{xxzz} & W_{yyzz} & W_{xxyz} & W_{xyyz} & W_{xyzz}
& & )\end{matrix}
Returns
-------
W : array(3, 3, 3, 3)
Full 4D kurtosis tensor
"""
W = np.zeros((3, 3, 3, 3))
xyz = [0, 1, 2]
for ind_i in xyz:
for ind_j in xyz:
for ind_k in xyz:
for ind_l in xyz:
key = (ind_i+1) * (ind_j+1) * (ind_k+1) * (ind_l+1)
W[ind_i][ind_j][ind_k][ind_l] = k_elements[ind_ele[key]]
return W
def split_dki_param(dki_params):
r""" Extract the diffusion tensor eigenvalues, the diffusion tensor
eigenvector matrix, and the 15 independent elements of the kurtosis tensor
from the model parameters estimated from the DKI model
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
Returns
--------
eigvals : array (x, y, z, 3) or (n, 3)
Eigenvalues from eigen decomposition of the tensor.
eigvecs : array (x, y, z, 3, 3) or (n, 3, 3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
kt : array (x, y, z, 15) or (n, 15)
Fifteen elements of the kurtosis tensor
"""
evals = dki_params[..., :3]
evecs = dki_params[..., 3:12].reshape(dki_params.shape[:-1] + (3, 3))
kt = dki_params[..., 12:]
return evals, evecs, kt
common_fit_methods = {'WLS': wls_fit_dki,
'OLS': ols_fit_dki,
'UWLLS': wls_fit_dki,
'ULLS': ols_fit_dki,
}
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