tedana.model
.fit_decay¶
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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: - t2sa ((S x E)
numpy.ndarray
) – Limited T2* map - s0va ((S x E)
numpy.ndarray
) – Limited S0 map - t2ss ((S x E)
numpy.ndarray
) – ??? - s0vs ((S x E)
numpy.ndarray
) – ??? - t2saf ((S x E)
numpy.ndarray
) – Full T2* map - s0vaf ((S x E)
numpy.ndarray
) – Full S0 map
Notes
Fit monoexponential decay function to all values for a given voxel across TRs, per TE, to estimate voxel-wise and :
Replace infinite values in map with 500 and NaN values in map with 0.
Generate limited and maps by doing something.