コード例 #1
0
ファイル: test_dki.py プロジェクト: JuergenNeubauer/dipy
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)
コード例 #2
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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)
コード例 #3
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ファイル: fwdti.py プロジェクト: siddy1989/dipy
def fwdti_prediction(params, gtab, S0=1, Diso=3.0e-3):
    r""" Signal prediction given the free water DTI model parameters.

    Parameters
    ----------
    params : (..., 13) ndarray
        Model parameters. The last dimension should have the 12 tensor
        parameters (3 eigenvalues, followed by the 3 corresponding
        eigenvectors) and the volume fraction of the free water compartment.
    gtab : a GradientTable class instance
        The gradient table for this prediction
    S0 : float or ndarray
        The non diffusion-weighted signal in every voxel, or across all
        voxels. Default: 1
    Diso : float, optional
        Value of the free water isotropic diffusion. Default is set to 3e-3
        $mm^{2}.s^{-1}$. Please adjust this value if you are assuming different
        units of diffusion.

    Returns
    --------
    S : (..., N) ndarray
        Simulated signal based on the free water DTI model

    Notes
    -----
    The predicted signal is given by:
    $S(\theta, b) = S_0 * [(1-f) * e^{-b ADC} + f * e^{-b D_{iso}]$, where
    $ADC = \theta Q \theta^T$, $\theta$ is a unit vector pointing at any
    direction on the sphere for which a signal is to be predicted, $b$ is the b
    value provided in the GradientTable input for that direction, $Q$ is the
    quadratic form of the tensor determined by the input parameters, $f$ is the
    free water diffusion compartment, $D_{iso}$ is the free water diffusivity
    which is equal to $3 * 10^{-3} mm^{2}s^{-1} [1]_.

    References
    ----------
    .. [1] Hoy, A.R., Koay, C.G., Kecskemeti, S.R., Alexander, A.L., 2014.
           Optimization of a free water elimination two-compartmental model
           for diffusion tensor imaging. NeuroImage 103, 323-333.
           doi: 10.1016/j.neuroimage.2014.09.053
    """
    evals = params[..., :3]
    evecs = params[..., 3:-1].reshape(params.shape[:-1] + (3, 3))
    f = params[..., 12]
    qform = vec_val_vect(evecs, evals)
    lower_dt = lower_triangular(qform, S0)
    lower_diso = lower_dt.copy()
    lower_diso[..., 0] = lower_diso[..., 2] = lower_diso[..., 5] = Diso
    lower_diso[..., 1] = lower_diso[..., 3] = lower_diso[..., 4] = 0
    D = design_matrix(gtab)

    pred_sig = np.zeros(f.shape + (gtab.bvals.shape[0], ))
    mask = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])
    index = ndindex(f.shape)
    for v in index:
        if mask[v]:
            pred_sig[v] = (1 - f[v]) * np.exp(np.dot(lower_dt[v], D.T)) + \
                          f[v] * np.exp(np.dot(lower_diso[v], D.T))

    return pred_sig
コード例 #4
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def dkimicro_prediction(params, gtab, S0=1):
    r""" Signal prediction given the DKI microstructure model parameters.

    Parameters
    ----------
    params : ndarray (x, y, z, 40) or (n, 40)
    All parameters estimated from the diffusion kurtosis microstructure 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
            4) Six elements of the hindered diffusion tensor
            5) Six elements of the restricted diffusion tensor
            6) Axonal water fraction
    gtab : a GradientTable class instance
        The gradient table for this prediction
    S0 : float or ndarray
        The non diffusion-weighted signal in every voxel, or across all
        voxels. Default: 1

    Returns
    -------
    S : (..., N) ndarray
        Simulated signal based on the DKI microstructure model

    Notes
    -----
    1) The predicted signal is given by:
    $S(\theta, b) = S_0 * [f * e^{-b ADC_{r}} + (1-f) * e^{-b ADC_{h}]$, where
    $ ADC_{r} and ADC_{h} are the apparent diffusion coefficients of the
    diffusion hindered and restricted compartment for a given direction
    $\theta$, $b$ is the b value provided in the GradientTable input for that
    direction, $f$ is the volume fraction of the restricted diffusion
    compartment (also known as the axonal water fraction).

    2) In the original article of DKI microstructural model [1]_, the hindered
    and restricted tensors were definde as the intra-cellular and
    extra-cellular diffusion compartments respectively.
    """

    # Initialize pred_sig
    pred_sig = np.zeros(params.shape[:-1] + (gtab.bvals.shape[0], ))

    # Define dti design matrix and region to process
    D = dti_design_matrix(gtab)
    evals = params[..., :3]
    mask = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])

    # Prepare parameters
    f = params[..., 27]
    adce = params[..., 28:34]
    adci = params[..., 34:40]

    if isinstance(S0, np.ndarray):
        S0_vol = S0 * np.ones(params.shape[:-1])
    else:
        S0_vol = S0

    # Process pred_sig for all data voxels
    index = ndindex(evals.shape[:-1])
    for v in index:
        if mask[v]:
            pred_sig[v] = (1. - f[v]) * np.exp(np.dot(D[:, :6], adce[v])) + \
                f[v] * np.exp(np.dot(D[:, :6], adci[v]))

    return pred_sig * S0_vol
コード例 #5
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ファイル: fwdti.py プロジェクト: StongeEtienne/dipy
def fwdti_prediction(params, gtab, S0=1, Diso=3.0e-3):
    r""" Signal prediction given the free water DTI model parameters.

    Parameters
    ----------
    params : (..., 13) ndarray
        Model parameters. The last dimension should have the 12 tensor
        parameters (3 eigenvalues, followed by the 3 corresponding
        eigenvectors) and the volume fraction of the free water compartment.
    gtab : a GradientTable class instance
        The gradient table for this prediction
    S0 : float or ndarray
        The non diffusion-weighted signal in every voxel, or across all
        voxels. Default: 1
    Diso : float, optional
        Value of the free water isotropic diffusion. Default is set to 3e-3
        $mm^{2}.s^{-1}$. Please adjust this value if you are assuming different
        units of diffusion.

    Returns
    --------
    S : (..., N) ndarray
        Simulated signal based on the free water DTI model

    Notes
    -----
    The predicted signal is given by:
    $S(\theta, b) = S_0 * [(1-f) * e^{-b ADC} + f * e^{-b D_{iso}]$, where
    $ADC = \theta Q \theta^T$, $\theta$ is a unit vector pointing at any
    direction on the sphere for which a signal is to be predicted, $b$ is the b
    value provided in the GradientTable input for that direction, $Q$ is the
    quadratic form of the tensor determined by the input parameters, $f$ is the
    free water diffusion compartment, $D_{iso}$ is the free water diffusivity
    which is equal to $3 * 10^{-3} mm^{2}s^{-1} [1]_.

    References
    ----------
    .. [1] Hoy, A.R., Koay, C.G., Kecskemeti, S.R., Alexander, A.L., 2014.
           Optimization of a free water elimination two-compartmental model
           for diffusion tensor imaging. NeuroImage 103, 323-333.
           doi: 10.1016/j.neuroimage.2014.09.053
    """
    evals = params[..., :3]
    evecs = params[..., 3:-1].reshape(params.shape[:-1] + (3, 3))
    f = params[..., 12]
    qform = vec_val_vect(evecs, evals)
    lower_dt = lower_triangular(qform, S0)
    lower_diso = lower_dt.copy()
    lower_diso[..., 0] = lower_diso[..., 2] = lower_diso[..., 5] = Diso
    lower_diso[..., 1] = lower_diso[..., 3] = lower_diso[..., 4] = 0
    D = design_matrix(gtab)

    pred_sig = np.zeros(f.shape + (gtab.bvals.shape[0],))
    mask = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])
    index = ndindex(f.shape)
    for v in index:
        if mask[v]:
            pred_sig[v] = (1 - f[v]) * np.exp(np.dot(lower_dt[v], D.T)) + \
                          f[v] * np.exp(np.dot(lower_diso[v], D.T))

    return pred_sig
コード例 #6
0
ファイル: dki_micro.py プロジェクト: MarcCote/dipy
def dkimicro_prediction(params, gtab, S0=1):
    r""" Signal prediction given the DKI microstructure model parameters.

    Parameters
    ----------
    params : ndarray (x, y, z, 40) or (n, 40)
    All parameters estimated from the diffusion kurtosis microstructure 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
            4) Six elements of the hindered diffusion tensor
            5) Six elements of the restricted diffusion tensor
            6) Axonal water fraction
    gtab : a GradientTable class instance
        The gradient table for this prediction
    S0 : float or ndarray
        The non diffusion-weighted signal in every voxel, or across all
        voxels. Default: 1

    Returns
    --------
    S : (..., N) ndarray
        Simulated signal based on the DKI microstructure model

    Notes
    -----
    1) The predicted signal is given by:
    $S(\theta, b) = S_0 * [f * e^{-b ADC_{r}} + (1-f) * e^{-b ADC_{h}]$, where
    $ ADC_{r} and ADC_{h} are the apparent diffusion coefficients of the
    diffusion hindered and restricted compartment for a given direction
    $\theta$, $b$ is the b value provided in the GradientTable input for that
    direction, $f$ is the volume fraction of the restricted diffusion
    compartment (also known as the axonal water fraction).

    2) In the original article of DKI microstructural model [1]_, the hindered
    and restricted tensors were definde as the intra-cellular and
    extra-cellular diffusion compartments respectively.
    """

    # Initialize pred_sig
    pred_sig = np.zeros(params.shape[:-1] + (gtab.bvals.shape[0],))

    # Define dti design matrix and region to process
    D = dti_design_matrix(gtab)
    evals = params[..., :3]
    mask = _positive_evals(evals[..., 0], evals[..., 1], evals[..., 2])

    # Prepare parameters
    f = params[..., 27]
    adce = params[..., 28:34]
    adci = params[..., 34:40]

    if isinstance(S0, np.ndarray):
        S0_vol = S0 * np.ones(params.shape[:-1])
    else:
        S0_vol = S0

    # Process pred_sig for all data voxels
    index = ndindex(evals.shape[:-1])
    for v in index:
        if mask[v]:
            pred_sig[v] = (1. - f[v]) * np.exp(np.dot(D[:, :6], adce[v])) + \
                f[v] * np.exp(np.dot(D[:, :6], adci[v]))

    return pred_sig * S0_vol