mdreg.fit_abs_exp_recovery_3p

mdreg.fit_abs_exp_recovery_3p(signal, TI=None, parallel=True, bounds=([0, 0, 0], [inf, inf, 2]), p0=[1, 1.3, 2], **kwargs)

2-parameter fit to an absolute exponential-recovery model fit.

\[S(\mathbf{r},T_I) = \left| S_0(\mathbf{r}) \left( 1 - A(\mathbf{r}) e^{-T_I/T(\mathbf{r})} \right) \right|\]

Parameters:

• signal (numpy.ndarray | zarr.Array) – 3D or 4D array with signal intensities. Dimensions are (x, y, t) or (x, y, z, t).

• TI (numpy.array) – Inversion times

• parallel (bool) – If True, use parallel processing. Default is False.

• bounds (tuple) – Bounds for the fit as (lower_bound, upper_bound) where lower_bound and upper_bound are either a scalar or a list of 3 values.

• p0 (list) – Initial values as a 3-element list.

• **kwargs – Additional keyword arguments accepted by fit_pixels.

Returns:

• fit (numpy.ndarray | zarr.Array) – Fit to the signal data, with same dimensions as the signal array.

• pars (numpy.ndarray | zarr.Array) – Fitted model parameters S0, T and A. Dimensions are (x,y,3) or (x,y,z,3).