tedana.decay.fit_decay

fit_decay(data, tes, mask, adaptive_mask, fittype, report=True)[source]

Fit voxel-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 ({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.

Returns:

  • t2s_limited ((S,) 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 ((S,) 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 ((S,) numpy.ndarray) – Full T2* map. For voxels affected by dropout, with good signal from only one echo, the full map uses the T2* estimate from the first two echoes.

  • s0_full ((S,) numpy.ndarray) – Full S0 map. For voxels affected by dropout, with good signal from only one echo, the full map uses the S0 estimate from the first two echoes.

See also

: func:tedana.utils.make_adaptive_maskThe function used to create the adaptive_mask

parameter.

Notes

This function replaces infinite values in the T_2^* map with 500 and T_2^* values less than or equal to zero with 1. Additionally, very small T_2^* values above zero are replaced with a floor value to prevent zero-division errors later on in the workflow. It also replaces NaN values in the S_0 map with 0.