`tedana.decay`.fit_decay

fit_decay(data, tes, adaptive_mask, fittype, report=True, n_threads=1)[source]

Fit voxel-wise monoexponential decay models to data.

Parameters:

data ((Md x E [x T]) array_like) – Multi-echo data array, where M is samples in denoising mask, E is echos, and T is time.
tes ((E,) list) – Echo times in milliseconds.
adaptive_mask ((Md,) array_like) – Array where each value indicates the number of echoes with good signal for that voxel. This mask may be thresholded; for example, with values less than 3 set to 0. For more information on thresholding, see make_adaptive_mask.
fittype ({loglin, curvefit}) – The type of model fit to use
report (bool, optional) – Whether to log a description of this step or not. Default is True.
n_threads (int, optional) – Number of threads to use. Default is 1. If None or <= 0, uses the number of available CPU cores.

Returns:

t2s ((Md,) numpy.ndarray) – “Full” T2* map without floors or ceilings applied. This includes T2* estimates for all voxels with adaptive mask >= 1. Voxels with adaptive mask == 1 have T2* estimates from the first two echoes.
s0 ((Md,) numpy.ndarray) – “Full” S0 map without floors or ceilings applied. This includes S0 estimates for all voxels with adaptive mask >= 1. Voxels with adaptive mask == 1 have S0 estimates from the first two echoes.
failures ((Md,) numpy.ndarray or None) – Boolean array indicating samples that failed to fit the model. None if fittype is not “curvefit”.
t2s_var ((Md,) numpy.ndarray or None) – Variance of the T2* estimates. None if fittype is not “curvefit”.
s0_var ((Md,) numpy.ndarray or None) – Variance of the S0 estimates. None if fittype is not “curvefit”.
t2s_s0_covar ((Md,) numpy.ndarray or None) – Covariance of the T2* and S0 estimates. None if fittype is not “curvefit”.

tedana.decay.fit_decay