예제 #1
0
파일: dki.py 프로젝트: oesteban/dipy
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 follow:
            1) Three diffusion tensor's eingenvalues
            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
예제 #2
0
파일: dki.py 프로젝트: hassemlal/dipy
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 follow:
            1) Three diffusion tensor's eingenvalues
            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
예제 #3
0
파일: dki.py 프로젝트: hassemlal/dipy
    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)
예제 #4
0
파일: dki.py 프로젝트: oesteban/dipy
    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)