Here you find a B1-correction algorithm as published in Windschuh et al. (2015), NMR Biomed., 28(5):529-37. doi: 10.1002/nbm.3283.

Dowload zipped Matlab implementations here or find the package on https://github.com/cest-sources/B1_correction/

There are two functions, Z_B1_correction and contrast_B1_correction, which both require data of several CEST acquisitions with different B1. Both reconstruct either Z-spectrum stacks or contrast stacks at the B1 values given by B1_output.

function [Z_stack_corr] = Z_B1_correction(Z_stack,rel_B1map,B1_input,B1_output,SEGMENT,fit_type,B1_input_index)
% [Z_stack_corr] = Z_B1_correction(Z_stack,rel_B1map,SEGMENT_2D,B1_input,fit_type,B1_input_index,B1_output)
% output: B1 corrected Z-stack (5D-stack)
% input:    Z_stack = 5D-stack of Z-spectra (y,x,z,offset,B1)
%           rel_B1map = relative B1map
%           B1_output = output B1 (scalar or vector)
%           SEGMENT = voxel/pixel mask for evaluation
%           fit_type = fit or interpolation type
%           B1_input_index = index vector of B1 samples that should be used
%           for correction
%

function [corr_img]=contrast_B1_correction(img,rel_B1map,B1_input,B1_output,SEGMENT_2D,fit_type,B1_input_index)
% [corr_img]=contrast_B1_correction(img,rel_B1map,B1_input,B1_output,SEGMENT_2D,fit_type,B1_input_index)
% output: B1 corrected images (2D or 3D-stack) (x,y,B1)
% input:    img = 3D-stack of images (x,y,B1) which are used for B1-correciton
%           rel_B1map = relative B1map
%           B1_output = output B1 (scalar or vector)
%           SEGMENT_2D = pixel mask for evaluation
%           fit_type = fit or interpolation type
%           B1_input_index = index vector of B1 samples that should be used
%           for correction