src.original.fitting.TF_reference package

Submodules

src.original.fitting.TF_reference.segmented_IVIMfit module

Code to calculate IVIM maps

Segmented fitting approach First fit D using a WLLS

src.original.fitting.TF_reference.segmented_IVIMfit.d_fit_iterative_wls(bvalues_D, log_signal, max_iter=50)

Function to calculate D using an iterative wlls on the log(signal) weights for the wlls are initialized from the predicted signal of a lls as described in http://dx.doi.org/10.1016/j.neuroimage.2013.05.028 equation (7)

Parameters: log_signal: the log() of the signal above the threshold for segmented fitting

bvalues_D: all bvalues above the threshold for fitting D

max_iter: the maximum number of iterations for WLS

src.original.fitting.TF_reference.segmented_IVIMfit.ivim_biexp(bvalues, D, f, Dp, S0=1)

src.original.fitting.TF_reference.segmented_IVIMfit.segmented_IVIM_fit(bvalues, dw_data, b_cutoff=200, bounds=([0.0001, 0.0, 0.001], [0.004, 0.7, 0.01]))

A segmented fitting implementation for a bi-exponential model. First D is fitted using a mono exponential model on all signal above the bvalue cutoff using an iterative WLLSVertrekpassage, Schiphol Then f is fitted by using the b=0 intercept from the mono expontential fit and substracting this from the measured signal at b=0 Then D* is fitted using a bi-exponential model with fixed D and f

Module contents