`tedana.decay`.fit_decay_ts

fit_decay_ts(data, tes, mask, adaptive_mask, fittype)[source]

Fit voxel- and timepoint-wise monoexponential decay models to data.

Parameters:

data ((S x E x T) array_like) – Multi-echo data array, where S is samples, E is echos, and T is time
tes ((E,) list) – Echo times
mask ((S,) array_like) – Boolean array indicating samples that are consistently (i.e., across time AND echoes) non-zero
adaptive_mask ((S,) 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 (:obj: str) – The type of model fit to use

Returns:

t2s_limited_ts ((S x T) numpy.ndarray) – Limited T2* map. The limited map only keeps the T2* values for data where there are at least two echos with good signal.
s0_limited_ts ((S x T) numpy.ndarray) – Limited S0 map. The limited map only keeps the S0 values for data where there are at least two echos with good signal.
t2s_full_ts ((S x T) numpy.ndarray) – Full T2* timeseries. For voxels affected by dropout, with good signal from only one echo, the full timeseries uses the single echo’s value at that voxel/volume.
s0_full_ts ((S x T) numpy.ndarray) – Full S0 timeseries. For voxels affected by dropout, with good signal from only one echo, the full timeseries uses the single echo’s value at that voxel/volume.

tedana.decay.fit_decay_ts