tedana.decay.fit_decay_ts

fit_decay_ts(data, tes, mask, masksum, 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
  • masksum ((S,) array_like) – Valued array indicating number of echos that have sufficient signal in given sample
  • fittype – 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.