tedana.model.monoexponential

Functions to estimate S0 and T2* from multi-echo data.

Functions

fit_decay(data, tes, mask, masksum, start_echo) Fit voxel-wise monoexponential decay models to data
fit_decay_ts(data, mask, tes, masksum, …) Fit voxel- and timepoint-wise monoexponential decay models to data
fit_decay(data, tes, mask, masksum, start_echo)[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
  • masksum ((S, ) array_like) – Valued array indicating number of echos that have sufficient signal in given sample
  • start_echo (int) – First echo to consider
Returns:

Notes

  1. Fit monoexponential decay function to all values for a given voxel across TRs, per TE, to estimate voxel-wise S_0 and T_2^*:

    S(TE) = S_0 * exp(-R_2^* * TE) T_2^* = 1 / R_2^*

  2. Replace infinite values in T_2^* map with 500 and NaN values in S_0 map with 0.

  3. Generate limited T_2^* and S_0 maps by doing something.

fit_decay_ts(data, mask, tes, masksum, start_echo)[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
  • start_echo (int) – First echo to consider
Returns: