dti.py 文件源码

def predict(params_file, gtab, S0_file=None, out_dir=None):
    """
    Create a signal prediction from DTI params

    params_file : str
        Full path to a file with parameters saved from a DKI fit

    gtab : GradientTable object
        The gradient table to predict for

    S0_file : str
        Full path to a nifti file that contains S0 measurements to incorporate
        into the prediction. If the file contains 4D data, the volumes that
        contain the S0 data must be the same as the gtab.b0s_mask.
    """
    if out_dir is None:
        out_dir = op.join(op.split(params_file)[0])

    if S0_file is None:
        S0 = 100
    else:
        S0 = nib.load(S0_file).get_data()
        # If the S0 data is 4D, we assume it comes from an acquisition that had
        # B0 measurements in the same volumes described in the gtab:
        if len(S0.shape) == 4:
            S0 = np.mean(S0[..., gtab.b0s_mask], -1)
        # Otherwise, we assume that it's already a 3D volume, and do nothing

    img = nib.load(params_file)
    params = img.get_data()
    pred = dti.tensor_prediction(params, gtab, S0=S0)
    fname = op.join(out_dir, 'dti_prediction.nii.gz')
    nib.save(nib.Nifti1Image(pred, img.affine), fname)

    return fname