tedana.decay.rmse_of_fit_decay_ts

rmse_of_fit_decay_ts(*, data: ndarray, tes: List[float], adaptive_mask: ndarray, t2s: ndarray, s0: ndarray, fitmode: Literal['all', 'ts']) → Tuple[ndarray, ndarray, ndarray]

Estimate model fit of voxel- and timepoint-wise monoexponential decay models to data.

Parameters:

• data ((Mb x E x T) numpy.ndarray) – Multi-echo data array, where Mb is samples in base mask, E is echos, and T is time.

• tes ((E,) list) – Echo times.

• adaptive_mask ((Mb,) numpy.ndarray) – 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().

• t2s ((Mb [x T]) numpy.ndarray) – Voxel-wise (and possibly volume-wise) T2* estimates from fit_decay_ts().

• s0 ((Mb [x T]) numpy.ndarray) – Voxel-wise (and possibly volume-wise) S0 estimates from fit_decay_ts().

• fitmode ({“fit”, “all”}) – Whether the T2* and S0 estimates are volume-wise (“fit”) or not (“all”).

Returns:

• rmse_map ((Mb,) numpy.ndarray) – Mean root mean squared error of the model fit across all volumes at each voxel.

• rmse_df (pandas.DataFrame) – Each column is the root mean squared error of the model fit at each timepoint. Columns are mean, standard deviation, and percentiles across voxels. Column labels are “rmse_mean”, “rmse_std”, “rmse_min”, “rmse_percentile02”, “rmse_percentile25”, “rmse_median”, “rmse_percentile75”, “rmse_percentile98”, and “rmse_max”